diff --git a/.gitignore b/.gitignore
index ca82aa5..e3128f9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -178,6 +178,8 @@ test_output_1/
 examples/test_output/SimID_*
 /examples/notebooks/*.py
 /examples/solver_output/zarr/
+/examples/solver_output/*.vtu
+/examples/solver_output/*.json
 
 examples/notebooks/workspace/
 
diff --git a/CLAUDE.md b/CLAUDE.md
index 829bcc7..4cc285c 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -28,6 +28,14 @@ Run `make check`, which does:
 
 Then run tests: `poetry run pytest tests -v`
 
+For changes to remote/session code, also prompt the developer to run authenticated integration tests manually:
+
+```bash
+poetry run pytest tests/vcml/test_remote_integration.py -v --run-remote
+```
+
+This requires interactive browser login and a live VCell server.
+
 ## Project structure
 
 ```
@@ -58,8 +66,10 @@ scripts/
 
 The main entry point is `import pyvcell.vcml as vc`. Key functions:
 
-- **Load models**: `vc.load_vcml_file()`, `vc.load_vcml_url()`, `vc.load_biomodel(id)`, `vc.load_sbml_file()`, `vc.load_antimony_str()`
-- **Simulate**: `vc.simulate(biomodel, sim_name)` (local), `vc.run_remote(...)` (server)
+- **Load models (local)**: `vc.load_vcml_file()`, `vc.load_vcml_url()`, `vc.load_sbml_file()`, `vc.load_antimony_str()`
+- **Simulate (local)**: `vc.simulate(biomodel, sim_name)`
+- **Remote (anonymous)**: `session = vc.connect()`, `session.load_biomodel(id)`, `session.list_biomodels()`
+- **Remote (authenticated)**: `session = vc.connect(login=True)`, `session.run_sim(...)`, `session.start_sim(...)`
 - **Results**: `result.plotter.plot_concentrations()`, `result.plotter.plot_slice_3d()`
 
 ## Code conventions
diff --git a/docs/getting-started/quickstart.md b/docs/getting-started/quickstart.md
index f9c6695..b12ec84 100644
--- a/docs/getting-started/quickstart.md
+++ b/docs/getting-started/quickstart.md
@@ -11,28 +11,20 @@ biomodel = vc.load_vcml_file("path/to/model.vcml")
 print(biomodel)
 ```
 
-You can also load public models directly from the VCell database by ID:
+You can also load public models from the VCell server. Use `vc.connect()` to create a session for remote access:
 
 ```python
-biomodel = vc.load_biomodel("279851639")
-```
-
-To browse or search models by name, authenticate first:
-
-```python
-from pyvcell._internal.api.vcell_client.auth.auth_utils import login_interactive
+session = vc.connect()
 
-api_client = login_interactive()  # opens a browser for login
+# Load by database ID
+biomodel = session.load_biomodel("279851639")
 
-# List available models (public, shared, and your private models)
-for m in vc.list_biomodels(api_client=api_client)[:2]:
+# List available models
+for m in session.list_biomodels()[:2]:
     print(m)
 
 # Load by name and owner
-biomodel = vc.load_biomodel(name="Tutorial_MultiApp", owner="tutorial", api_client=api_client)
-
-# Load by database ID
-biomodel = vc.load_biomodel("279851639", api_client=api_client)
+biomodel = session.load_biomodel(name="Tutorial_MultiApp", owner="tutorial")
 ```
 
 Output:
diff --git a/docs/guides/notebooks/remote-simulations.ipynb b/docs/guides/notebooks/remote-simulations.ipynb
index db9e87b..0bf9977 100644
--- a/docs/guides/notebooks/remote-simulations.ipynb
+++ b/docs/guides/notebooks/remote-simulations.ipynb
@@ -28,20 +28,14 @@
      "start_time": "2026-03-09T12:59:48.311989Z"
     }
    },
-   "source": [
-    "from pyvcell._internal.api.vcell_client.auth.auth_utils import login_interactive\n",
-    "\n",
-    "api_client = login_interactive()"
-   ],
+   "source": "import pyvcell.vcml as vc\n\nsession = vc.connect(login=True)",
    "outputs": [],
-   "execution_count": 1
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "## Build and save model to VCell server"
-   ]
+   "source": "## Build a model locally"
   },
   {
    "cell_type": "code",
@@ -51,52 +45,21 @@
      "start_time": "2026-03-09T12:59:50.326519Z"
     }
    },
-   "source": "import pyvcell.vcml as vc\n\nantimony_str = \"\"\"\n    compartment ec = 1;\n    compartment cell = 2;\n    compartment pm = 1;\n    species A in cell;\n    species B in cell;\n    J0: A -> B; cell * (k1*A - k2*B)\n    J0 in cell;\n    k1 = 5.0; k2 = 2.0\n    A = 10\n\"\"\"\nbiomodel = vc.load_antimony_str(antimony_str)\nmodel = biomodel.model\nmodel.get_compartment(\"pm\").dim = 2\n\ngeo = vc.Geometry(name=\"geo\", origin=(0, 0, 0), extent=(10, 10, 10), dim=3)\ngeo.add_sphere(name=\"cell_domain\", radius=4, center=(5, 5, 5))\ngeo.add_background(name=\"ec_domain\")\ngeo.add_surface(name=\"pm_domain\", sub_volume_1=\"cell_domain\", sub_volume_2=\"ec_domain\")\n\napp = biomodel.add_application(\"app1\", geometry=geo)\napp.map_compartment(\"cell\", \"cell_domain\")\napp.map_compartment(\"ec\", \"ec_domain\")\napp.map_species(\"A\", init_conc=\"3+sin(x)\", diff_coef=1.0)\napp.map_species(\"B\", init_conc=\"2+cos(x+y+z)\", diff_coef=1.0)\n\nsim = app.add_sim(name=\"sim1\", duration=2.0, output_time_step=0.05, mesh_size=(50, 50, 50))",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2026-03-09T12:59:51.082123Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
-      "Please remove the `packages` attribute from your configuration file.\n",
-      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
-      "2026-03-09 08:59:51,085 ERROR (SBMLDocument.java:573) - There was an error accessing the sbml online validator!\n",
-      "2026-03-09T12:59:51.090261Z main WARN The Logger cbit.vcell.model.Kinetics was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@67b1217b and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
-      "2026-03-09T12:59:51.093216Z main WARN The Logger cbit.vcell.mapping.AbstractMathMapping was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@67b1217b and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
-      "2026-03-09 08:59:51,093  INFO (DiffEquMathMapping.java:1457) - WARNING:::: MathMapping.refreshMathDescription() ... assigning boundary condition types not unique\n",
-      "2026-03-09 08:59:51,093  INFO (DiffEquMathMapping.java:1457) - WARNING:::: MathMapping.refreshMathDescription() ... assigning boundary condition types not unique\n",
-      "2026-03-09 08:59:51,094  INFO (Entrypoints.java:172) - Returning from sbmlToVcell: {\"success\":true,\"message\":\"Success\"}\n"
-     ]
-    }
-   ],
-   "execution_count": 2
+   "source": "antimony_str = \"\"\"\n    compartment ec = 1;\n    compartment cell = 2;\n    compartment pm = 1;\n    species A in cell;\n    species B in cell;\n    J0: A -> B; cell * (k1*A - k2*B)\n    J0 in cell;\n    k1 = 5.0; k2 = 2.0\n    A = 10\n\"\"\"\nbiomodel = vc.load_antimony_str(antimony_str)\nmodel = biomodel.model\nmodel.get_compartment(\"pm\").dim = 2\n\ngeo = vc.Geometry(name=\"geo\", origin=(0, 0, 0), extent=(10, 10, 10), dim=3)\ngeo.add_sphere(name=\"cell_domain\", radius=4, center=(5, 5, 5))\ngeo.add_background(name=\"ec_domain\")\ngeo.add_surface(name=\"pm_domain\", sub_volume_1=\"cell_domain\", sub_volume_2=\"ec_domain\")\n\napp = biomodel.add_application(\"app1\", geometry=geo)\napp.map_compartment(\"cell\", \"cell_domain\")\napp.map_compartment(\"ec\", \"ec_domain\")\napp.map_species(\"A\", init_conc=\"3+sin(x)\", diff_coef=1.0)\napp.map_species(\"B\", init_conc=\"2+cos(x+y+z)\", diff_coef=1.0)\n\nsim = app.add_sim(name=\"sim1\", duration=0.5, output_time_step=0.1, mesh_size=(20, 20, 20))",
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "cell_type": "code",
+   "cell_type": "markdown",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2026-03-09T12:59:52.619682Z",
      "start_time": "2026-03-09T12:59:51.154384Z"
     }
    },
-   "source": "from datetime import datetime\nfrom pyvcell._internal.api.vcell_client.api.bio_model_resource_api import BioModelResourceApi\n\nvcml_str = vc.to_vcml_str(biomodel)\n\nbm_api = BioModelResourceApi(api_client)\nmodel_name = f\"MyRemoteModel_{datetime.now().strftime('%Y%m%d_%H%M%S')}\"\nsaved_vcml = bm_api.save_bio_model(body=vcml_str, new_name=model_name)\nprint(f\"Saved model as: {model_name}\")",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2026-03-09T12:59:51.168514Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
-      "Please remove the `packages` attribute from your configuration file.\n",
-      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
-      "2026-03-09T12:59:51.171992Z main WARN The Logger cbit.vcell.model.Kinetics was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@3248a0bf and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
-      "2026-03-09T12:59:51.363477Z main WARN The Logger cbit.vcell.mapping.AbstractMathMapping was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@3248a0bf and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
-      "2026-03-09 08:59:51,364  INFO (DiffEquMathMapping.java:1457) - WARNING:::: MathMapping.refreshMathDescription() ... assigning boundary condition types not unique\n",
-      "2026-03-09 08:59:51,373  INFO (Entrypoints.java:200) - Returning from vcellToVcml: {\"success\":true,\"message\":\"Success\"}\n",
-      "Saved model as: MyRemoteModel_20260309_085951\n"
-     ]
-    }
-   ],
-   "execution_count": 3
+   "source": "## Run remotely (blocking)\n\nThe simplest approach — save, run, wait, and export in one call:",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
@@ -106,24 +69,14 @@
      "start_time": "2026-03-09T12:59:52.625336Z"
     }
    },
-   "source": "saved_biomodel = vc.load_vcml_str(saved_vcml)\nbm_key = saved_biomodel.version.key\nsaved_app = next(a for a in saved_biomodel.applications if a.name == \"app1\")\nsim_key = saved_app.simulations[0].version.key\nsim_name = saved_app.simulations[0].name\nprint(f\"BioModel key: {bm_key}, Simulation key: {sim_key}\")",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BioModel key: 306602051, Simulation key: 306602048\n"
-     ]
-    }
-   ],
-   "execution_count": 4
+   "source": "from datetime import datetime\n\nmodel_name = f\"MyRemoteModel_{datetime.now().strftime('%Y%m%d_%H%M%S')}\"\nstore = session.run_sim(biomodel, \"sim1\", model_name=model_name, on_progress=print)\nprint(f\"Shape: {store.shape}, Dtype: {store.dtype}\")\n# Shape is (X, Y, Variables, Z, Time)",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "## Start simulation"
-   ]
+   "source": "## Run remotely (non-blocking)\n\nFor more control, use `start_sim()` to get a `SimulationJob`:"
   },
   {
    "cell_type": "code",
@@ -133,24 +86,14 @@
      "start_time": "2026-03-09T12:59:52.639633Z"
     }
    },
-   "source": "from pyvcell._internal.api.vcell_client.api.simulation_resource_api import SimulationResourceApi\n\nsim_api = SimulationResourceApi(api_client)\nsim_api.start_simulation(sim_id=sim_key)\nprint(\"Simulation started\")",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Simulation started\n"
-     ]
-    }
-   ],
-   "execution_count": 5
+   "source": "model_name = f\"MyRemoteModel_{datetime.now().strftime('%Y%m%d_%H%M%S')}\"\njob = session.start_sim(biomodel, \"sim1\", model_name=model_name, on_progress=print)\nprint(f\"Simulation started, status: {job.status}\")",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "## Monitor progress"
-   ]
+   "source": "Wait for completion and export results:"
   },
   {
    "cell_type": "code",
@@ -160,179 +103,9 @@
      "start_time": "2026-03-09T12:59:52.769842Z"
     }
    },
-   "source": [
-    "import time\n",
-    "\n",
-    "while True:\n",
-    "    status_record = sim_api.get_simulation_status(\n",
-    "        sim_id=sim_key,\n",
-    "        bio_model_id=bm_key,\n",
-    "    )\n",
-    "    print(f\"Status: {status_record.status}, Details: {status_record.details}\")\n",
-    "\n",
-    "    if status_record.status in (\"COMPLETED\", \"FAILED\", \"STOPPED\"):\n",
-    "        break\n",
-    "\n",
-    "    time.sleep(5)\n",
-    "\n",
-    "if status_record.status != \"COMPLETED\":\n",
-    "    raise RuntimeError(f\"Simulation ended with status: {status_record.status}\")"
-   ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Status: Status.WAITING, Details: waiting to be dispatched\n",
-      "Status: Status.RUNNING, Details: running...\n",
-      "Status: Status.RUNNING, Details: running...\n",
-      "Status: Status.RUNNING, Details: running...\n",
-      "Status: Status.RUNNING, Details: simulation [SimID_306602048_0_] started\n",
-      "Status: Status.RUNNING, Details: simulation [SimID_306602048_0_] started\n",
-      "Status: Status.RUNNING, Details: simulation [SimID_306602048_0_] started\n",
-      "Status: Status.RUNNING, Details: 1.35\n",
-      "Status: Status.RUNNING, Details: 1.35\n",
-      "Status: Status.RUNNING, Details: 1.35\n",
-      "Status: Status.COMPLETED, Details: completed\n"
-     ]
-    }
-   ],
-   "execution_count": 6
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Export results (N5 format)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-09T13:00:43.280494Z",
-     "start_time": "2026-03-09T13:00:43.223492Z"
-    }
-   },
-   "source": "from pyvcell._internal.api.vcell_client.api.export_resource_api import ExportResourceApi\nfrom pyvcell._internal.api.vcell_client.models.n5_export_request import N5ExportRequest\nfrom pyvcell._internal.api.vcell_client.models.standard_export_info import StandardExportInfo\nfrom pyvcell._internal.api.vcell_client.models.exportable_data_type import ExportableDataType\nfrom pyvcell._internal.api.vcell_client.models.variable_specs import VariableSpecs\nfrom pyvcell._internal.api.vcell_client.models.variable_mode import VariableMode\nfrom pyvcell._internal.api.vcell_client.models.time_specs import TimeSpecs\nfrom pyvcell._internal.api.vcell_client.models.time_mode import TimeMode\n\nexport_api = ExportResourceApi(api_client)\n\n# Compute time indices from simulation parameters\nnum_time_points = int(sim.duration / sim.output_time_step) + 1\nall_times = [i * sim.output_time_step for i in range(num_time_points)]\n\nrequest = N5ExportRequest(\n    standard_export_information=StandardExportInfo(\n        simulation_name=sim_name,\n        simulation_key=sim_key,\n        simulation_job=0,\n        variable_specs=VariableSpecs(\n            variable_names=[\"A\", \"B\"],\n            mode=VariableMode.VARIABLE_MULTI,\n        ),\n        time_specs=TimeSpecs(\n            begin_time_index=0,\n            end_time_index=num_time_points - 1,\n            all_times=all_times,\n            mode=TimeMode.TIME_RANGE,\n        ),\n    ),\n    exportable_data_type=ExportableDataType.PDE_VARIABLE_DATA,\n    dataset_name=\"my_results\",\n)\n\njob_id = export_api.export_n5(n5_export_request=request)\nprint(f\"Export job started: {job_id}\")",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Export job started: 4919316062\n"
-     ]
-    }
-   ],
-   "execution_count": 7
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-09T13:06:19.664644Z",
-     "start_time": "2026-03-09T13:00:43.281212Z"
-    }
-   },
-   "source": [
-    "while True:\n",
-    "    events = export_api.export_status()\n",
-    "    for event in events:\n",
-    "        if event.job_id == job_id:\n",
-    "            if event.event_type == \"EXPORT_COMPLETE\":\n",
-    "                export_url = event.location\n",
-    "                print(f\"Export complete: {export_url}\")\n",
-    "                break\n",
-    "            elif event.event_type == \"EXPORT_FAILURE\":\n",
-    "                raise RuntimeError(f\"Export failed: {event}\")\n",
-    "    else:\n",
-    "        time.sleep(5)\n",
-    "        continue\n",
-    "    break"
-   ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Export complete: https://vcell.cam.uchc.edu/n5Data/schaff/ffea0744240babd.n5?dataSetName=4919316062\n"
-     ]
-    }
-   ],
-   "execution_count": 8
-  },
-  {
-   "cell_type": "markdown",
-   "source": "## Read N5 results with TensorStore\n\nThe export URL is not a direct download — it points to a remote N5 dataset served via S3-compatible storage. Parse the URL and open with TensorStore for lazy chunked reads:",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "source": "from urllib.parse import urlparse, parse_qs\nimport tensorstore as ts\n\nparsed = urlparse(export_url)\npath_parts = parsed.path.strip(\"/\").split(\"/\", 1)\nbucket = path_parts[0]                                  # \"n5Data\"\ncontainer_key = path_parts[1]                            # \"{user}/{hash}.n5\"\ns3_endpoint = f\"{parsed.scheme}://{parsed.netloc}\"       # \"https://vcell.cam.uchc.edu\"\ndataset_name = parse_qs(parsed.query)[\"dataSetName\"][0]  # export job ID\n\nstore = ts.open({\n    \"driver\": \"n5\",\n    \"kvstore\": {\n        \"driver\": \"http\",\n        \"base_url\": f\"{s3_endpoint}/{bucket}/{container_key}/{dataset_name}\",\n    },\n    \"open\": True,\n}).result()\n\nprint(f\"Shape: {store.shape}, Dtype: {store.dtype}\")\n# Shape is (X, Y, Variables, Z, Time)\n# Channels 0..N-2 are exported variables (A, B), channel N-1 is the domain mask\n\n# Read a slice — e.g. variable A, all X/Y, first z-slice, first timepoint\nslice_data = store[:, :, 0, 0, 0].read().result()\nprint(f\"Slice shape: {slice_data.shape}\")",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-09T13:06:19.913631Z",
-     "start_time": "2026-03-09T13:06:19.673180Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape: (98, 98, 3, 98, 41), Dtype: dtype(\"float64\")\n",
-      "Slice shape: (98, 98)\n"
-     ]
-    }
-   ],
-   "execution_count": 9
-  },
-  {
-   "cell_type": "markdown",
-   "source": "## Convenience API\n\nThe steps above (save, start, monitor, export, open TensorStore) can be replaced with a few convenience functions from `pyvcell.vcml`:\n\n```python\n# One-liner: save, run, export, and open TensorStore\nstore = vc.run_remote(api_client, biomodel, \"sim1\")\n\n# Or composable:\nsaved_bm, saved_sim = vc.save_and_start(api_client, biomodel, \"sim1\")\nvc.wait_for_simulation(api_client, saved_bm, saved_sim)\nstore = vc.export_n5(api_client, saved_sim, biomodel=saved_bm)\n```",
-   "metadata": {}
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-09T13:24:18.971823Z",
-     "start_time": "2026-03-09T13:17:59.723184Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "# One-liner: save, run, export, and open TensorStore\nmodel_name = f\"MyRemoteModel_{datetime.now().strftime('%Y%m%d_%H%M%S')}\"\nstore = vc.run_remote(api_client, biomodel, \"sim1\", model_name=model_name, on_progress=print)\nprint(f\"Shape: {store.shape}, Dtype: {store.dtype}\")",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2026-03-09T13:17:59.738617Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
-      "Please remove the `packages` attribute from your configuration file.\n",
-      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
-      "2026-03-09T13:17:59.742812Z main WARN The Logger cbit.vcell.model.Kinetics was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@ae32def and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
-      "2026-03-09T13:17:59.931398Z main WARN The Logger cbit.vcell.mapping.AbstractMathMapping was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@ae32def and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
-      "2026-03-09 09:17:59,932  INFO (DiffEquMathMapping.java:1457) - WARNING:::: MathMapping.refreshMathDescription() ... assigning boundary condition types not unique\n",
-      "2026-03-09 09:17:59,941  INFO (Entrypoints.java:200) - Returning from vcellToVcml: {\"success\":true,\"message\":\"Success\"}\n",
-      "Saving biomodel to server...\n",
-      "Starting simulation...\n",
-      "Status: Status.WAITING, Details: waiting to be dispatched\n",
-      "Status: Status.RUNNING, Details: running...\n",
-      "Status: Status.RUNNING, Details: initializing mesh\n",
-      "Status: Status.RUNNING, Details: simulation [SimID_306603569_0_] started\n",
-      "Status: Status.RUNNING, Details: simulation [SimID_306603569_0_] started\n",
-      "Status: Status.RUNNING, Details: simulation [SimID_306603569_0_] started\n",
-      "Status: Status.RUNNING, Details: 1.35\n",
-      "Status: Status.RUNNING, Details: 1.35\n",
-      "Status: Status.RUNNING, Details: 1.35\n",
-      "Status: Status.COMPLETED, Details: completed\n",
-      "Starting N5 export...\n",
-      "Export job started: 4957758156\n",
-      "Export complete: https://vcell.cam.uchc.edu/n5Data/schaff/98e993615baa16f.n5?dataSetName=4957758156\n",
-      "Shape: (98, 98, 3, 98, 41), Dtype: dtype(\"float64\")\n"
-     ]
-    }
-   ],
-   "execution_count": 11
+   "source": "# Block until done, then export\nstore = job.result()\nprint(f\"Shape: {store.shape}, Dtype: {store.dtype}\")\n\n# Read a slice — e.g. variable A, all X/Y, first z-slice, first timepoint\nslice_data = store[:, :, 0, 0, 0].read().result()\nprint(f\"Slice shape: {slice_data.shape}\")",
+   "outputs": [],
+   "execution_count": null
   }
  ],
  "metadata": {
diff --git a/docs/guides/remote-simulations.md b/docs/guides/remote-simulations.md
index 1fea00b..3ce3331 100644
--- a/docs/guides/remote-simulations.md
+++ b/docs/guides/remote-simulations.md
@@ -11,35 +11,27 @@ This guide requires a VCell account and access to the VCell server. Code example
 - A [VCell account](https://vcell.org)
 - Familiarity with [Building a Model](building-a-model.md)
 
-## Authenticate
+## Connect to VCell
 
-Use `login_interactive()` to authenticate via OAuth2. This opens your browser for login and returns an authenticated API client:
+All remote operations go through a `VCellSession`. Use `vc.connect()` for anonymous access to public data, or `vc.connect(login=True)` for full authenticated access:
 
 ```python
-from pyvcell._internal.api.vcell_client.auth.auth_utils import login_interactive
-
-api_client = login_interactive()
-```
+import pyvcell.vcml as vc
 
-The defaults connect to the production VCell server (`https://vcell.cam.uchc.edu`). Only change them if you know what you are doing:
+# Anonymous — browse and load public models (no login required)
+session = vc.connect()
 
-```python
-api_client = login_interactive(
-    api_base_url="https://vcell.cam.uchc.edu",
-    client_id="cjoWhd7W8A8znf7Z7vizyvKJCiqTgRtf",
-    issuer_url="https://dev-dzhx7i2db3x3kkvq.us.auth0.com",
-)
+# Authenticated — full access (opens browser for OAuth2 login)
+session = vc.connect(login=True)
 ```
 
-## Save model to VCell server
+To clear an authenticated session, call `vc.logout()`.
 
-Build a model locally (see [Building a Model](building-a-model.md) for details), then save it to the server:
+## Build a model
 
-```python
-import pyvcell.vcml as vc
-from pyvcell._internal.api.vcell_client.api.bio_model_resource_api import BioModelResourceApi
+Build a model locally (see [Building a Model](building-a-model.md) for details):
 
-# Build a model locally
+```python
 antimony_str = """
     compartment ec = 1;
     compartment cell = 2;
@@ -66,58 +58,44 @@ app.map_compartment("ec", "ec_domain")
 app.map_species("A", init_conc="3+sin(x)", diff_coef=1.0)
 app.map_species("B", init_conc="2+cos(x+y+z)", diff_coef=1.0)
 
-sim = app.add_sim(name="sim1", duration=2.0, output_time_step=0.05, mesh_size=(50, 50, 50))
-
-# Serialize and save to server
-vcml_str = vc.to_vcml_str(biomodel)
-
-bm_api = BioModelResourceApi(api_client)
-saved_vcml = bm_api.save_bio_model(body=vcml_str, new_name="MyRemoteModel")
+sim = app.add_sim(name="sim1", duration=0.5, output_time_step=0.1, mesh_size=(20, 20, 20))
 ```
 
-Parse the saved VCML to get the biomodel ID and simulation key, which you'll need for the next steps:
+## Run a remote simulation (blocking)
+
+The simplest way — save, run, wait, and export in one call:
 
 ```python
-saved_biomodel = vc.load_vcml_str(saved_vcml)
-bm_key = saved_biomodel.version.key
-saved_app = next(a for a in saved_biomodel.applications if a.name == "app1")
-sim_key = saved_app.simulations[0].version.key
-sim_name = saved_app.simulations[0].name
+store = session.run_sim(biomodel, "sim1")
+data = store[:, :, 0, 0, 0].read().result()
 ```
 
-## Start simulation
+The returned `TensorStore` points to the exported N5 results. Reads are lazy — only the N5 blocks you access are fetched.
 
-Use `SimulationResourceApi` to start the simulation on the server:
+## Run a remote simulation (non-blocking)
+
+For more control, use `start_sim()` to get a `SimulationJob`:
 
 ```python
-from pyvcell._internal.api.vcell_client.api.simulation_resource_api import SimulationResourceApi
+job = session.start_sim(biomodel, "sim1")
 
-sim_api = SimulationResourceApi(api_client)
-status_messages = sim_api.start_simulation(sim_id=sim_key)
-print(status_messages)
-```
+# Check status without blocking
+print(job.status)
 
-## Monitor progress
+# Block until completion, then export
+store = job.result()
+```
 
-Poll the simulation status until it reaches a terminal state:
+Or control each step individually:
 
 ```python
-import time
-
-while True:
-    status_record = sim_api.get_simulation_status(
-        sim_id=sim_key,
-        bio_model_id=bm_key,
-    )
-    print(f"Status: {status_record.status}, Details: {status_record.details}")
-
-    if status_record.status in ("COMPLETED", "FAILED", "STOPPED"):
-        break
+job = session.start_sim(biomodel, "sim1", on_progress=print)
 
-    time.sleep(5)
+# Wait for completion
+job.wait()
 
-if status_record.status != "COMPLETED":
-    raise RuntimeError(f"Simulation ended with status: {status_record.status}")
+# Export results
+store = job.export()
 ```
 
 The simulation lifecycle follows these states:
@@ -126,129 +104,66 @@ The simulation lifecycle follows these states:
 
 A simulation can also end in `FAILED` or `STOPPED`.
 
-## Export results (N5 format)
+## Browse and load models
 
-Once the simulation completes, export the results in N5 format. The server writes the N5 dataset to S3-compatible storage and returns a URL you can read remotely with `zarr` and `s3fs`:
+Load public models by ID with an anonymous session:
 
 ```python
-from pyvcell._internal.api.vcell_client.api.export_resource_api import ExportResourceApi
-from pyvcell._internal.api.vcell_client.models.n5_export_request import N5ExportRequest
-from pyvcell._internal.api.vcell_client.models.standard_export_info import StandardExportInfo
-from pyvcell._internal.api.vcell_client.models.exportable_data_type import ExportableDataType
-from pyvcell._internal.api.vcell_client.models.variable_specs import VariableSpecs
-from pyvcell._internal.api.vcell_client.models.variable_mode import VariableMode
-from pyvcell._internal.api.vcell_client.models.time_specs import TimeSpecs
-from pyvcell._internal.api.vcell_client.models.time_mode import TimeMode
-
-export_api = ExportResourceApi(api_client)
-
-# Compute time indices from simulation parameters
-num_time_points = int(sim.duration / sim.output_time_step) + 1
-all_times = [i * sim.output_time_step for i in range(num_time_points)]
-
-request = N5ExportRequest(
-    standard_export_information=StandardExportInfo(
-        simulation_name=sim_name,
-        simulation_key=sim_key,
-        simulation_job=0,
-        variable_specs=VariableSpecs(
-            variable_names=["A", "B"],
-            mode=VariableMode.VARIABLE_MULTI,
-        ),
-        time_specs=TimeSpecs(
-            begin_time_index=0,
-            end_time_index=num_time_points - 1,
-            all_times=all_times,
-            mode=TimeMode.TIME_RANGE,
-        ),
-    ),
-    exportable_data_type=ExportableDataType.PDE_VARIABLE_DATA,
-    dataset_name="my_results",
-)
-
-job_id = export_api.export_n5(n5_export_request=request)
-print(f"Export job started: {job_id}")
+session = vc.connect()
+biomodel = session.load_biomodel("279851639")
 ```
 
-Poll for export completion:
+Listing and searching by name currently require an authenticated session (temporary — the VCell API will support anonymous listing in a future release):
 
 ```python
-while True:
-    events = export_api.export_status()
-    for event in events:
-        if event.job_id == job_id:
-            if event.event_type == "EXPORT_COMPLETE":
-                export_url = event.location
-                print(f"Export complete: {export_url}")
-                break
-            elif event.event_type == "EXPORT_FAILURE":
-                raise RuntimeError(f"Export failed: {event}")
-    else:
-        time.sleep(5)
-        continue
-    break
+session = vc.connect(login=True)
+
+# List accessible models (public + private)
+models = session.list_biomodels()
+for m in models[:5]:
+    print(m)
+
+# Load by name
+biomodel = session.load_biomodel(name="Tutorial_MultiApp", owner="tutorial")
 ```
 
-## Read N5 results with TensorStore
+## Save a model
 
-The export URL is not a direct download — it points to a remote N5 dataset served via S3-compatible storage. Parse the URL to extract the S3 endpoint, bucket, container path, and dataset name:
+Save a biomodel to the server without starting a simulation:
 
 ```python
-from urllib.parse import urlparse, parse_qs
-
-parsed = urlparse(export_url)
-path_parts = parsed.path.strip("/").split("/", 1)
-bucket = path_parts[0]                                  # "n5Data"
-container_key = path_parts[1]                            # "{user}/{hash}.n5"
-s3_endpoint = f"{parsed.scheme}://{parsed.netloc}"       # "https://vcell.cam.uchc.edu"
-dataset_name = parse_qs(parsed.query)["dataSetName"][0]  # export job ID
+saved_biomodel = session.save_biomodel(biomodel, name="MyModel")
+print(saved_biomodel.version.key)
 ```
 
-Open the N5 dataset with TensorStore. Reads are lazy — only the N5 blocks you access are fetched:
+## Session API reference
 
-```python
-import tensorstore as ts
-
-store = ts.open({
-    "driver": "n5",
-    "kvstore": {
-        "driver": "http",
-        "base_url": f"{s3_endpoint}/{bucket}/{container_key}/{dataset_name}",
-    },
-    "open": True,
-}).result()
-
-print(f"Shape: {store.shape}, Dtype: {store.dtype}")
-# Shape is (X, Y, Variables, Z, Time)
-# Channels 0..N-2 are exported variables (A, B), channel N-1 is the domain mask
+| Method                     | Auth required? | Purpose                       | Returns                      |
+| -------------------------- | -------------- | ----------------------------- | ---------------------------- |
+| `session.load_biomodel()`  | No (public ID) | Load model by ID or name      | `Biomodel`                   |
+| `session.list_biomodels()` | Yes\*          | List accessible models        | `list[dict]`                 |
+| `session.save_biomodel()`  | Yes            | Save model to server          | `Biomodel` with version keys |
+| `session.run_sim()`        | Yes            | Save, run, export (blocking)  | `TensorStore`                |
+| `session.start_sim()`      | Yes            | Save and start (non-blocking) | `SimulationJob`              |
 
-# Read a slice — e.g. variable A, all X/Y, first z-slice, first timepoint
-slice_data = store[:, :, 0, 0, 0].read().result()
-```
+| SimulationJob method | Purpose                    | Returns                    |
+| -------------------- | -------------------------- | -------------------------- |
+| `job.status`         | Poll current status        | `Status`                   |
+| `job.wait()`         | Block until terminal state | `None` (raises on failure) |
+| `job.export()`       | Export results as N5       | `TensorStore`              |
+| `job.result()`       | `wait()` then `export()`   | `TensorStore`              |
+
+\* Temporarily requires auth due to a VCell API limitation — will support anonymous access in a future release.
+
+All methods accept an optional `on_progress` callback and `timeout` parameter.
 
 ## Complete example
 
 ```python
-import time
-from datetime import datetime
-from urllib.parse import urlparse, parse_qs
-
-import tensorstore as ts
 import pyvcell.vcml as vc
-from pyvcell._internal.api.vcell_client.auth.auth_utils import login_interactive
-from pyvcell._internal.api.vcell_client.api.bio_model_resource_api import BioModelResourceApi
-from pyvcell._internal.api.vcell_client.api.simulation_resource_api import SimulationResourceApi
-from pyvcell._internal.api.vcell_client.api.export_resource_api import ExportResourceApi
-from pyvcell._internal.api.vcell_client.models.n5_export_request import N5ExportRequest
-from pyvcell._internal.api.vcell_client.models.standard_export_info import StandardExportInfo
-from pyvcell._internal.api.vcell_client.models.exportable_data_type import ExportableDataType
-from pyvcell._internal.api.vcell_client.models.variable_specs import VariableSpecs
-from pyvcell._internal.api.vcell_client.models.variable_mode import VariableMode
-from pyvcell._internal.api.vcell_client.models.time_specs import TimeSpecs
-from pyvcell._internal.api.vcell_client.models.time_mode import TimeMode
 
 # 1. Authenticate
-api_client = login_interactive()
+session = vc.connect(login=True)
 
 # 2. Build model locally
 antimony_str = """
@@ -276,129 +191,14 @@ app.map_compartment("cell", "cell_domain")
 app.map_compartment("ec", "ec_domain")
 app.map_species("A", init_conc="3+sin(x)", diff_coef=1.0)
 app.map_species("B", init_conc="2+cos(x+y+z)", diff_coef=1.0)
-sim = app.add_sim(name="sim1", duration=2.0, output_time_step=0.05, mesh_size=(50, 50, 50))
-
-# 3. Save to server
-vcml_str = vc.to_vcml_str(biomodel)
-bm_api = BioModelResourceApi(api_client)
-model_name = f"MyRemoteModel_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
-saved_vcml = bm_api.save_bio_model(body=vcml_str, new_name=model_name)
-
-saved_biomodel = vc.load_vcml_str(saved_vcml)
-bm_key = saved_biomodel.version.key
-saved_app = next(a for a in saved_biomodel.applications if a.name == "app1")
-sim_key = saved_app.simulations[0].version.key
-sim_name = saved_app.simulations[0].name
-
-# 4. Start simulation
-sim_api = SimulationResourceApi(api_client)
-sim_api.start_simulation(sim_id=sim_key)
-
-# 5. Monitor progress
-while True:
-    status_record = sim_api.get_simulation_status(sim_id=sim_key, bio_model_id=bm_key)
-    print(f"Status: {status_record.status}")
-    if status_record.status in ("COMPLETED", "FAILED", "STOPPED"):
-        break
-    time.sleep(5)
-
-if status_record.status != "COMPLETED":
-    raise RuntimeError(f"Simulation ended with status: {status_record.status}")
-
-# 6. Export results
-export_api = ExportResourceApi(api_client)
-num_time_points = int(sim.duration / sim.output_time_step) + 1
-all_times = [i * sim.output_time_step for i in range(num_time_points)]
-request = N5ExportRequest(
-    standard_export_information=StandardExportInfo(
-        simulation_name=sim_name,
-        simulation_key=sim_key,
-        simulation_job=0,
-        variable_specs=VariableSpecs(
-            variable_names=["A", "B"],
-            mode=VariableMode.VARIABLE_MULTI,
-        ),
-        time_specs=TimeSpecs(
-            begin_time_index=0,
-            end_time_index=num_time_points - 1,
-            all_times=all_times,
-            mode=TimeMode.TIME_RANGE,
-        ),
-    ),
-    exportable_data_type=ExportableDataType.PDE_VARIABLE_DATA,
-    dataset_name="my_results",
-)
-job_id = export_api.export_n5(n5_export_request=request)
-
-while True:
-    events = export_api.export_status()
-    for event in events:
-        if event.job_id == job_id:
-            if event.event_type == "EXPORT_COMPLETE":
-                export_url = event.location
-                break
-            elif event.event_type == "EXPORT_FAILURE":
-                raise RuntimeError(f"Export failed: {event}")
-    else:
-        time.sleep(5)
-        continue
-    break
-
-# 7. Read N5 results with TensorStore (lazy chunked reads)
-parsed = urlparse(export_url)
-path_parts = parsed.path.strip("/").split("/", 1)
-bucket = path_parts[0]
-container_key = path_parts[1]
-s3_endpoint = f"{parsed.scheme}://{parsed.netloc}"
-dataset_name = parse_qs(parsed.query)["dataSetName"][0]
-
-store = ts.open({
-    "driver": "n5",
-    "kvstore": {
-        "driver": "http",
-        "base_url": f"{s3_endpoint}/{bucket}/{container_key}/{dataset_name}",
-    },
-    "open": True,
-}).result()
+sim = app.add_sim(name="sim1", duration=0.5, output_time_step=0.1, mesh_size=(20, 20, 20))
+
+# 3. Run remotely (blocking)
+store = session.run_sim(biomodel, "sim1", on_progress=print)
 print(f"Results shape: {store.shape}, dtype: {store.dtype}")
 # Shape is (X, Y, Variables, Z, Time)
-# Channels 0..N-2 are exported variables (A, B), channel N-1 is the domain mask
-```
-
-## Convenience API
-
-The `pyvcell.vcml` module provides high-level functions that wrap the steps above into a few calls:
-
-```python
-import pyvcell.vcml as vc
-from pyvcell._internal.api.vcell_client.auth.auth_utils import login_interactive
-
-api_client = login_interactive()
-
-# ... build biomodel and sim as above ...
-
-# One call does everything: save, run, export, and open TensorStore
-store = vc.run_remote(api_client, biomodel, "sim1")
-data = store[:, :, 0, 0, 0].read().result()
-```
-
-Or use the composable functions for more control:
-
-```python
-saved_bm, saved_sim = vc.save_and_start(api_client, biomodel, "sim1")
-vc.wait_for_simulation(api_client, saved_bm, saved_sim)
-store = vc.export_n5(api_client, saved_sim, biomodel=saved_bm)
 ```
 
-| Function                | Purpose                             | Returns                                    |
-| ----------------------- | ----------------------------------- | ------------------------------------------ |
-| `save_and_start()`      | Save biomodel + start simulation    | `(Biomodel, Simulation)` with version keys |
-| `wait_for_simulation()` | Poll until completed/failed/stopped | `None` (raises on failure)                 |
-| `export_n5()`           | Export + poll + open TensorStore    | `TensorStore`                              |
-| `run_remote()`          | All three chained                   | `TensorStore`                              |
-
-All functions accept an optional `on_progress` callback and `timeout` parameter.
-
 ## Next steps
 
 - [Parameter Exploration](parameter-exploration.md) — Run batch simulations with varied parameters
diff --git a/docs/project-overview.md b/docs/project-overview.md
new file mode 100644
index 0000000..102cb32
--- /dev/null
+++ b/docs/project-overview.md
@@ -0,0 +1,113 @@
+# pyvcell — Project Overview
+
+Source material for presentations, slides, and stakeholder communications.
+
+## What it is
+
+Python interface for [Virtual Cell](https://vcell.org) — a platform for spatial modeling, simulation, and analysis of cell biological systems. Published as prebuilt Python packages on PyPI for Linux, macOS, and Windows.
+
+## Motivation
+
+Jupyter notebooks have become a valuable tool for learning, developing, and disseminating modeling projects, and enhance reproducibility. Python scripting provides flexibility to customize simulation and data analysis workflows and easily integrate familiar, powerful tools for data analysis and visualization. Following the approach of the BasiCO Python library (which wraps COPASI), pyvcell empowers modelers to use VCell's spatial simulation technology while providing complete control over the structure of their modeling projects — all from Python scripts, Jupyter notebooks, or other third-party tools.
+
+## Who it's for
+
+Computational biologists, biophysicists, and researchers who want to build and simulate cell models programmatically rather than through the VCell desktop GUI. pyvcell expands the reach of VCell's spatial simulation capabilities to modelers who prefer Python-based workflows.
+
+## Core capabilities
+
+1. **Model building** — Define compartments, species, reactions, and spatial geometries in Python using Antimony, SBML, or VCML formats
+2. **Complex geometries** — Analytic primitives (spheres, backgrounds), multi-compartment structures, reusable geometries from existing models, and image-based segmented geometries
+3. **Local simulation** — Run VCell solvers locally, get results as Zarr arrays, visualize with built-in plotters
+4. **Remote simulation** — Connect to the VCell server cluster for large-scale simulations, with results streamed back lazily
+5. **Simulation chaining** — Use output from one simulation as initial conditions for the next via field data references (`vcField`)
+6. **Synthetic field data** — Supply programmatic or image-derived spatial data as initial conditions
+7. **Parameter exploration** — Modify parameters, run batch sweeps, and compare results for sensitivity analysis
+8. **Visualization** — Concentration time series, 2D/3D spatial slices, animations, VTK mesh export, and interactive Trame widgets in Jupyter
+9. **Format interop** — Round-trip between VCML, SBML Spatial (designed by the VCell team), and Antimony representations
+
+## Key design principles
+
+- **Lightweight Python datamodels, proven Java algorithms** — Model authoring and manipulation uses pure Python dataclasses exposing essential VCell concepts only. Validation, math generation, and simulation preprocessing invoke battle-tested Java logic from libvcell, bundled via GraalVM native compilation. This includes model translation (SBML Spatial to VCell BioModel), math generation (BioModel to MathModel using VCell's formal equation-based descriptions of reaction-diffusion-advection PDE, particle Brownian dynamics, hybrid PDE/particle, or rule-based mesoscopic molecular processes), and mesh generation/solver preprocessing.
+
+- **Native solvers as Python packages** — VCell's C++ solvers are repackaged as standalone Python packages using thin wrappers (pybind11, scikit-build-core). pyvcell-fvsolver is on PyPI today; pyvcell-ode, pyvcell-nfsim, and pyvcell-stoch are in progress.
+
+- **Wrapped API with session-based remote access** — All VCell API endpoints are accessible, but common operations (load/save biomodels, run remote simulations, retrieve results) are wrapped in a remote session for simplified, discoverable access. A single entry point (`vc.connect()`) provides both anonymous and authenticated sessions.
+
+- **Cloud-native lazy data retrieval** — Remote results use N5 format (similar to Zarr) with TensorStore for multidimensional data access — only requested data is transmitted, on demand.
+
+- **Ecosystem integration** — Results are exported into VTK and Zarr format for convenient access using NumPy, Pandas, VTK, and PyVista for further data analysis and visualization. Interactive 3D visualization in Jupyter is provided via Trame widgets.
+
+## Minimal examples
+
+### Local: load, simulate, visualize
+
+```python
+import pyvcell.vcml as vc
+
+biomodel = vc.load_vcml_file("model.vcml")
+result = vc.simulate(biomodel, "Simulation1")
+result.plotter.plot_concentrations()
+```
+
+### Remote: connect, load from server, run on cluster
+
+```python
+import pyvcell.vcml as vc
+
+session = vc.connect(login=True)
+biomodel = session.load_biomodel("279851639")
+store = session.run_sim(biomodel, "sim1")
+```
+
+### Chain simulations: use first run's output as next run's initial conditions
+
+```python
+result1 = vc.simulate(biomodel, "sim1")
+species.init_conc = f"vcField('{result1.solver_output_dir.name}','s0',0.0,'Volume')"
+result2 = vc.simulate(biomodel, "sim1")
+```
+
+### Image data as initial conditions
+
+```python
+import numpy as np
+from scipy.ndimage import gaussian_filter
+import pyvcell.vcml as vc
+
+biomodel = vc.load_vcml_file("model.vcml")
+app = biomodel.applications[0]
+sim = app.simulations[0]
+
+# Build a synthetic "microscopy image" from the geometry's domain mask
+seg = app.geometry.to_segmented_image(resolution=64)
+mask = seg.get_mask("cell_domain").astype(float)
+image_data = gaussian_filter(mask, sigma=3) + 0.05 * np.random.randn(*mask.shape)
+
+# Create a field and use it as initial condition
+field = vc.Field(data_name="microscopy", data_nD=image_data)
+app.get_species_mapping("s0").init_conc = field.expression
+
+# Simulate — solver resamples field data to simulation mesh via trilinear interpolation
+result = vc.simulate(biomodel, sim.name, fields=[field])
+```
+
+### Parameter sweep
+
+```python
+for kf in [0.1, 1.0, 10.0]:
+    model.set_parameter_value("r0.Kf", kf)
+    result = vc.simulate(biomodel, "sim1")
+    result.plotter.plot_concentrations()
+```
+
+## Architecture summary
+
+| Layer             | Technology                          | Role                                                        |
+| ----------------- | ----------------------------------- | ----------------------------------------------------------- |
+| Python datamodels | Pydantic/dataclasses                | Model authoring, manipulation, essential VCell concepts     |
+| libvcell          | Java via GraalVM native compilation | Validation, math generation, solver preprocessing           |
+| Solvers           | C++ via pybind11/scikit-build-core  | Local finite volume, ODE, stochastic, rule-based simulation |
+| VCell API         | OpenAPI generated client            | Remote simulation, model storage, result export             |
+| Data access       | Zarr, N5/TensorStore, VTK           | Local and remote result retrieval                           |
+| Visualization     | Matplotlib, PyVista, Trame          | 2D/3D plotting, animations, interactive Jupyter widgets     |
diff --git a/examples/notebooks/_internal_data_demo.ipynb b/examples/notebooks/_internal_data_demo.ipynb
index 33a01ac..339d301 100644
--- a/examples/notebooks/_internal_data_demo.ipynb
+++ b/examples/notebooks/_internal_data_demo.ipynb
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "e88a4bb558aba920",
    "metadata": {
     "ExecuteTime": {
@@ -69,9 +69,7 @@
     }
    },
    "outputs": [],
-   "source": [
-    "test_data_dir = Path(os.getcwd()).parent / \"solver_output\"\n"
-   ]
+   "source": "test_data_dir = Path(os.getcwd()).parent / \"solver_output\""
   },
   {
    "cell_type": "markdown",
@@ -442,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "d560b5a94bb8a5cb",
    "metadata": {
     "ExecuteTime": {
@@ -454,30 +452,8 @@
      "outputs_hidden": false
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "t = metadata['times']\n",
-    "y = [c['mean_values'] for c in metadata['channels'] if c['index'] > 4]\n",
-    "y_labels = [c['label'] for c in metadata['channels'] if c['index'] > 4]\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "ax.plot(t, y)\n",
-    "ax.set(xlabel='time (s)', ylabel='concentration',\n",
-    "       title='Concentration over time')\n",
-    "ax.legend(y_labels)\n",
-    "ax.grid()\n"
-   ]
+   "outputs": [],
+   "source": "times = metadata['times']\nconc_values = [c['mean_values'] for c in metadata['channels'] if c['index'] > 4]\nconc_labels = [c['label'] for c in metadata['channels'] if c['index'] > 4]\n\nfig, ax = plt.subplots()\nax.plot(times, conc_values)\nax.set(xlabel='time (s)', ylabel='concentration',\n       title='Concentration over time')\nax.legend(conc_labels)\nax.grid()"
   },
   {
    "cell_type": "markdown",
@@ -629,4 +605,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/_internal_vcell_publications.ipynb b/examples/notebooks/_internal_vcell_publications.ipynb
index be18792..aafa5b3 100644
--- a/examples/notebooks/_internal_vcell_publications.ipynb
+++ b/examples/notebooks/_internal_vcell_publications.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "5e183b4a68315dfe",
    "metadata": {
     "ExecuteTime": {
@@ -15,12 +15,7 @@
     }
    },
    "outputs": [],
-   "source": [
-    "from IPython.display import display\n",
-    "from pyvcell._internal.api.vcell_client import ApiClient, Configuration, PublicationResourceApi\n",
-    "from pyvcell._internal.api.vcell_client.auth.auth_utils import login_interactive\n",
-    "from pyvcell._internal.api.vcell_client.models import Publication"
-   ]
+   "source": "from IPython.display import display\nfrom pyvcell._internal.api.vcell_client import ApiClient, Configuration, PublicationResourceApi\nfrom pyvcell._internal.api.vcell_client.auth.auth_utils import login_interactive\nfrom pyvcell._internal.api.vcell_client.models.publication import Publication"
   },
   {
    "cell_type": "markdown",
@@ -249,4 +244,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/fielddata_trame.ipynb b/examples/notebooks/fielddata_trame.ipynb
index dfabdab..97b62dd 100644
--- a/examples/notebooks/fielddata_trame.ipynb
+++ b/examples/notebooks/fielddata_trame.ipynb
@@ -2,70 +2,92 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
    "id": "cd6e4d13dfbf7174",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-13T19:23:29.721995Z",
-     "start_time": "2025-04-13T19:23:27.464335Z"
+     "end_time": "2026-03-28T13:22:37.347754Z",
+     "start_time": "2026-03-28T13:22:35.812722Z"
     }
    },
-   "outputs": [],
    "source": [
     "import os\n",
     "import shutil\n",
     "from pathlib import Path\n",
     "import pyvcell.vcml as vc\n"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 1
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
    "id": "90b534edd21ee708",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-13T19:23:31.213426Z",
-     "start_time": "2025-04-13T19:23:29.732582Z"
+     "end_time": "2026-03-28T13:22:39.137649Z",
+     "start_time": "2026-03-28T13:22:37.351076Z"
     }
    },
+   "source": [
+    "# ----- make a workspace\n",
+    "workspace_dir = Path(os.getcwd()) / \"workspace\"\n",
+    "sim1_dir = workspace_dir / \"sim1_dir\"\n",
+    "if sim1_dir.exists():\n",
+    "    shutil.rmtree(sim1_dir)\n",
+    "sim2_dir = workspace_dir / \"sim2_dir\"\n",
+    "if sim2_dir.exists():\n",
+    "    shutil.rmtree(sim2_dir)\n",
+    "\n",
+    "# ---- read in VCML file\n",
+    "model_fp = Path(os.getcwd()).parent / \"models\" / \"SmallSpatialProject_3D.vcml\"\n",
+    "bio_model1 = vc.load_vcml_file(model_fp)\n",
+    "\n",
+    "# ---- get the application and the species mappings for species \"s0\" and \"s1\"\n",
+    "app = bio_model1.applications[0]\n",
+    "s1_mapping = next(s for s in app.species_mappings if s.species_name == \"s1\")\n",
+    "s0_mapping = next(s for s in app.species_mappings if s.species_name == \"s0\")\n",
+    "\n",
+    "# ---- add a simulation to the first application in the biomodel (didn't already have a simulation in the VCML file)\n",
+    "new_sim = vc.Simulation(name=\"new_sim\", duration=10.0, output_time_step=0.1, mesh_size=(20, 20, 20))\n",
+    "app.simulations.append(new_sim)\n",
+    "\n",
+    "# ---- set the initial concentration of species \"s0\" and \"s1\" in the first application\n",
+    "s0_mapping.init_conc = \"3+sin(x)+cos(y)+sin(z)\"\n",
+    "s1_mapping.init_conc = \"3+sin(x+y+z)\"\n",
+    "\n",
+    "# ---- run simulation, store in sim1_dir, and plot results\n",
+    "# >>>>> This forms the data for the \"Field Data\" identified by 'sim1_dir' <<<<<<\n",
+    "sim1_result = vc.simulate(biomodel=bio_model1, simulation=new_sim.name)\n",
+    "# print([c.label for c in sim1_result.channel_data])\n",
+    "# print(sim1_result.time_points[::11])\n",
+    "# sim1_result.plotter.plot_slice_3d(time_index=0, channel_id=\"s0\")\n",
+    "# sim1_result.plotter.plot_slice_3d(time_index=0, channel_id=\"s1\")\n",
+    "# sim1_result.plotter.plot_concentrations()"
+   ],
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv_jupyter/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
-      "2025-04-14T02:39:13.347430Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-28T13:22:37.453969Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
       "Please remove the `packages` attribute from your configuration file.\n",
       "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
-      "2025-04-14T02:39:13.351282Z main WARN The Logger cbit.vcell.model.Kinetics was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@51a4211b and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
-      "2025-04-14T02:39:13.519687Z main WARN The Logger cbit.vcell.mapping.AbstractMathMapping was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@51a4211b and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
-      "2025-04-13 22:39:13,522  WARN (AbstractMathMapping.java:1130) - Unit exception: Incompatible units [l.uM.dm-2.s-1] and [molecules.s-1.um-2] in Expression: ((Kf_r1 * s0) - (Kr_r1 * s2))\n",
-      "2025-04-13 22:39:13,522  WARN (AbstractMathMapping.java:1130) - Unit exception: Incompatible units [molecules.s-1.um-2] and [l.uM.dm-2.s-1] in Expression: ((Kf_r2 * s2) - (Kr_r2 * s3))\n",
-      "2025-04-13 22:39:13,527  INFO (Entrypoints.java:200) - Returning from vcellToVcml: {\"success\":true,\"message\":\"Success\"}\n",
-      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv_jupyter/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
-      "2025-04-14T02:39:13.542295Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "2026-03-28T13:22:37.458328Z main WARN The Logger cbit.vcell.model.Kinetics was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@2a42b34e and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
+      "2026-03-28T13:22:37.684973Z main WARN The Logger cbit.vcell.mapping.AbstractMathMapping was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@2a42b34e and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
+      "2026-03-28 09:22:37,704  WARN (AbstractMathMapping.java:1130) - Unit exception: Incompatible units [l.uM.dm-2.s-1] and [molecules.s-1.um-2] in Expression: ((Kf_r1 * s0) - (Kr_r1 * s2))\n",
+      "2026-03-28 09:22:37,704  WARN (AbstractMathMapping.java:1130) - Unit exception: Incompatible units [molecules.s-1.um-2] and [l.uM.dm-2.s-1] in Expression: ((Kf_r2 * s2) - (Kr_r2 * s3))\n",
+      "2026-03-28 09:22:37,708  INFO (Entrypoints.java:202) - Returning from vcellToVcml: {\"success\":true,\"message\":\"Success\"}\n",
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-28T13:22:37.724671Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
       "Please remove the `packages` attribute from your configuration file.\n",
       "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
-      "2025-04-14T02:39:13.543110Z main WARN The Logger org.vcell.libvcell.Entrypoints was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@377bd8e6 and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
-      "2025-04-14T02:39:13.546545Z main WARN The Logger cbit.vcell.model.Kinetics was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@377bd8e6 and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
-      "2025-04-14T02:39:13.550469Z main WARN The Logger cbit.vcell.mapping.AbstractMathMapping was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@377bd8e6 and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
-      "2025-04-13 22:39:13,558  WARN (AbstractMathMapping.java:1130) - Unit exception: Incompatible units [l.uM.dm-2.s-1] and [molecules.s-1.um-2] in Expression: ((Kf_r1 * s0) - (Kr_r1 * s2))\n",
-      "2025-04-13 22:39:13,559  WARN (AbstractMathMapping.java:1130) - Unit exception: Incompatible units [molecules.s-1.um-2] and [l.uM.dm-2.s-1] in Expression: ((Kf_r2 * s2) - (Kr_r2 * s3))\n",
-      "2025-04-13 22:39:13,567  INFO (Entrypoints.java:83) - Returning from vcmlToFiniteVolumeInput: {\"success\":true,\"message\":\"Success\"}\n",
-      "Setting Base file name to: `\"/Users/jimschaff/Documents/workspace/pyvcell/examples/notebooks/workspace/out_dir_m6cuj2vl/SimID_208355207_0_\"`\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Simulation Complete in Main() ... \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "2026-03-28T13:22:37.725581Z main WARN The Logger org.vcell.libvcell.Entrypoints was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@cf16f1 and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
+      "2026-03-28T13:22:37.729407Z main WARN The Logger cbit.vcell.model.Kinetics was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@cf16f1 and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
+      "2026-03-28T13:22:37.733458Z main WARN The Logger cbit.vcell.mapping.AbstractMathMapping was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@cf16f1 and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
+      "2026-03-28 09:22:37,743  WARN (AbstractMathMapping.java:1130) - Unit exception: Incompatible units [l.uM.dm-2.s-1] and [molecules.s-1.um-2] in Expression: ((Kf_r1 * s0) - (Kr_r1 * s2))\n",
+      "2026-03-28 09:22:37,743  WARN (AbstractMathMapping.java:1130) - Unit exception: Incompatible units [molecules.s-1.um-2] and [l.uM.dm-2.s-1] in Expression: ((Kf_r2 * s2) - (Kr_r2 * s3))\n",
+      "2026-03-28 09:22:37,753  INFO (Entrypoints.java:85) - Returning from vcmlToFiniteVolumeInput: {\"success\":true,\"message\":\"Success\"}\n",
+      "Setting Base file name to: `\"/Users/jimschaff/Documents/workspace/pyvcell/examples/notebooks/workspace/out_dir_l5q5l31q/SimID_2113910508_0_\"`\n",
       "initializing mesh\n",
       "numVolume=8000\n",
       "\n",
@@ -86,9 +108,9 @@
       "mesh initialized\n",
       "preprocessing finished\n",
       "pdeCount=4, odeCount=0\n",
-      "No log-file found at constructed path `/Users/jimschaff/Documents/workspace/pyvcell/examples/notebooks/workspace/out_dir_m6cuj2vl/SimID_208355207_0_.log`.simulation [SimID_208355207_0_] started\n",
+      "No log-file found at constructed path `/Users/jimschaff/Documents/workspace/pyvcell/examples/notebooks/workspace/out_dir_l5q5l31q/SimID_2113910508_0_.log`.simulation [SimID_2113910508_0_] started\n",
       "temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0000.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0000.sim\n",
       "**This is a little endian machine.**\n",
       "[[[data:0]]]\n",
       "numVolRegions=2\n",
@@ -105,203 +127,203 @@
       "sundials pde solver is starting from time 0\n",
       "----------------------------------\n",
       "temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0001.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0001.sim\n",
       "[[[data:0.1]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0002.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0002.sim\n",
       "[[[data:0.2]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0003.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0003.sim\n",
       "[[[data:0.3]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0004.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0004.sim\n",
       "[[[data:0.4]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0005.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0005.sim\n",
       "[[[data:0.5]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0006.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0006.sim\n",
       "[[[data:0.6]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0007.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0007.sim\n",
       "[[[data:0.7]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0008.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0008.sim\n",
       "[[[data:0.8]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0009.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0009.sim\n",
       "[[[data:0.9]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0010.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0010.sim\n",
       "[[[data:1]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0011.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0011.sim\n",
       "[[[data:1.1]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0012.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0012.sim\n",
       "[[[data:1.2]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0013.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0013.sim\n",
       "[[[data:1.3]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0014.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0014.sim\n",
       "[[[data:1.4]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0015.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0015.sim\n",
       "[[[data:1.5]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0016.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0016.sim\n",
       "[[[data:1.6]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0017.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0017.sim\n",
       "[[[data:1.7]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0018.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0018.sim\n",
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       "[[[data:6.8]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0069.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0069.sim\n",
       "[[[data:6.9]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0070.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0070.sim\n",
       "[[[data:7]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0071.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0071.sim\n",
       "[[[data:7.1]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0072.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0072.sim\n",
       "[[[data:7.2]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0073.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0073.sim\n",
       "[[[data:7.3]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0074.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0074.sim\n",
       "[[[data:7.4]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0075.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0075.sim\n",
       "[[[data:7.5]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0076.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0076.sim\n",
       "[[[data:7.6]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0077.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0077.sim\n",
       "[[[data:7.7]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0078.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0078.sim\n",
       "[[[data:7.8]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0079.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0079.sim\n",
       "[[[data:7.9]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0080.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0080.sim\n",
       "[[[data:8]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0081.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0081.sim\n",
       "[[[data:8.1]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0082.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0082.sim\n",
       "[[[data:8.2]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0083.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0083.sim\n",
       "[[[data:8.3]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0084.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0084.sim\n",
       "[[[data:8.4]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0085.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0085.sim\n",
       "[[[data:8.5]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0086.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0086.sim\n",
       "[[[data:8.6]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0087.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0087.sim\n",
       "[[[data:8.7]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0088.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0088.sim\n",
       "[[[data:8.8]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0089.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0089.sim\n",
       "[[[data:8.9]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0090.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0090.sim\n",
       "[[[data:9]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0091.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0091.sim\n",
       "[[[data:9.1]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0092.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0092.sim\n",
       "[[[data:9.2]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0093.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0093.sim\n",
       "[[[data:9.3]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0094.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0094.sim\n",
       "[[[data:9.4]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0095.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0095.sim\n",
       "[[[data:9.5]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0096.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0096.sim\n",
       "[[[data:9.6]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0097.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0097.sim\n",
       "[[[data:9.7]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0098.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0098.sim\n",
       "[[[data:9.8]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0099.sim\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0099.sim\n",
       "[[[data:9.9]]]\n",
       "Final Statistics.. \n",
       "\n",
@@ -316,85 +338,93 @@
       "last step  = 0.100000\n",
       "\n",
       "temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
-      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_208355207_0_0100.sim\n",
-      "[[[data:10]]][[[progress:100%]]]"
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_2113910508_0_0100.sim\n",
+      "[[[data:10]]][[[progress:100%]]]--------------- sim summary ----------------\n",
+      "Cartesian Mesh: Domain    x:[0,10] y:[0,10] z:[0,10]\n",
+      "                Elements  numX=20 numY=20 numZ=20\n",
+      "\n",
+      "\ttime = 10 sec\n",
+      "\tdT   = 0.1 sec\n",
+      "--------------------------------------------\n",
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-28T13:22:38.499281Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Please remove the `packages` attribute from your configuration file.\n",
+      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
+      "2026-03-28 09:22:38,500  INFO (Entrypoints.java:274) - Returning from vcellInfixToNumExprInfix: {\"success\":true,\"message\":\"Success\"}\n",
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-28T13:22:38.512211Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Please remove the `packages` attribute from your configuration file.\n",
+      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
+      "2026-03-28 09:22:38,513  INFO (Entrypoints.java:274) - Returning from vcellInfixToNumExprInfix: {\"success\":true,\"message\":\"Success\"}\n",
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-28T13:22:38.524720Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Please remove the `packages` attribute from your configuration file.\n",
+      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
+      "2026-03-28 09:22:38,525  INFO (Entrypoints.java:274) - Returning from vcellInfixToNumExprInfix: {\"success\":true,\"message\":\"Success\"}\n",
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-28T13:22:38.538238Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Please remove the `packages` attribute from your configuration file.\n",
+      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
+      "2026-03-28 09:22:38,539  INFO (Entrypoints.java:274) - Returning from vcellInfixToNumExprInfix: {\"success\":true,\"message\":\"Success\"}\n",
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-28T13:22:38.553598Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Please remove the `packages` attribute from your configuration file.\n",
+      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
+      "2026-03-28 09:22:38,554  INFO (Entrypoints.java:274) - Returning from vcellInfixToNumExprInfix: {\"success\":true,\"message\":\"Success\"}\n",
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-28T13:22:38.568202Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Please remove the `packages` attribute from your configuration file.\n",
+      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
+      "2026-03-28 09:22:38,569  INFO (Entrypoints.java:274) - Returning from vcellInfixToNumExprInfix: {\"success\":true,\"message\":\"Success\"}\n",
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-28T13:22:38.581304Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Please remove the `packages` attribute from your configuration file.\n",
+      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
+      "2026-03-28 09:22:38,582  INFO (Entrypoints.java:274) - Returning from vcellInfixToNumExprInfix: {\"success\":true,\"message\":\"Success\"}\n",
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-28T13:22:38.593667Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Please remove the `packages` attribute from your configuration file.\n",
+      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
+      "2026-03-28 09:22:38,594  INFO (Entrypoints.java:274) - Returning from vcellInfixToNumExprInfix: {\"success\":true,\"message\":\"Success\"}\n",
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-28T13:22:38.605724Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Please remove the `packages` attribute from your configuration file.\n",
+      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
+      "2026-03-28 09:22:38,606  INFO (Entrypoints.java:274) - Returning from vcellInfixToNumExprInfix: {\"success\":true,\"message\":\"Success\"}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Simulation Complete in Main() ... \n"
      ]
     }
    ],
-   "source": [
-    "# ----- make a workspace\n",
-    "workspace_dir = Path(os.getcwd()) / \"workspace\"\n",
-    "sim1_dir = workspace_dir / \"sim1_dir\"\n",
-    "if sim1_dir.exists():\n",
-    "    shutil.rmtree(sim1_dir)\n",
-    "sim2_dir = workspace_dir / \"sim2_dir\"\n",
-    "if sim2_dir.exists():\n",
-    "    shutil.rmtree(sim2_dir)\n",
-    "\n",
-    "# ---- read in VCML file\n",
-    "model_fp = Path(os.getcwd()).parent / \"models\" / \"SmallSpatialProject_3D.vcml\"\n",
-    "bio_model1 = vc.load_vcml_file(model_fp)\n",
-    "\n",
-    "# ---- get the application and the species mappings for species \"s0\" and \"s1\"\n",
-    "app = bio_model1.applications[0]\n",
-    "s1_mapping = next(s for s in app.species_mappings if s.species_name == \"s1\")\n",
-    "s0_mapping = next(s for s in app.species_mappings if s.species_name == \"s0\")\n",
-    "\n",
-    "# ---- add a simulation to the first application in the biomodel (didn't already have a simulation in the VCML file)\n",
-    "new_sim = vc.Simulation(name=\"new_sim\", duration=10.0, output_time_step=0.1, mesh_size=(20, 20, 20))\n",
-    "app.simulations.append(new_sim)\n",
-    "\n",
-    "# ---- set the initial concentration of species \"s0\" and \"s1\" in the first application\n",
-    "s0_mapping.init_conc = \"3+sin(x)+cos(y)+sin(z)\"\n",
-    "s1_mapping.init_conc = \"3+sin(x+y+z)\"\n",
-    "\n",
-    "# ---- run simulation, store in sim1_dir, and plot results\n",
-    "# >>>>> This forms the data for the \"Field Data\" identified by 'sim1_dir' <<<<<<\n",
-    "sim1_result = vc.simulate(biomodel=bio_model1, simulation=new_sim.name)\n",
-    "# print([c.label for c in sim1_result.channel_data])\n",
-    "# print(sim1_result.time_points[::11])\n",
-    "# sim1_result.plotter.plot_slice_3d(time_index=0, channel_id=\"s0\")\n",
-    "# sim1_result.plotter.plot_slice_3d(time_index=0, channel_id=\"s1\")\n",
-    "# sim1_result.plotter.plot_concentrations()"
-   ]
+   "execution_count": 2
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
    "id": "f139cbdd98786c82",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-13T19:23:31.964708Z",
-     "start_time": "2025-04-13T19:23:31.261662Z"
+     "end_time": "2026-03-28T13:24:22.438053Z",
+     "start_time": "2026-03-28T13:24:22.301854Z"
     }
    },
+   "source": "import pyvcell.sim_results.widget as widget\n\nviewer = widget.App(sim1_result.vtk_data)\nlayout = await viewer.run()",
    "outputs": [],
-   "source": [
-    "import pyvcell.sim_results.widget as widget\n",
-    "from trame.app.jupyter import show\n",
-    "\n",
-    "app = widget.App(sim1_result.vtk_data)\n",
-    "layout = await app.run()\n",
-    "show(layout, open_browser=False)\n",
-    "\n",
-    "# vtk_data1 = sim1_result.vtk_data\n",
-    "#\n",
-    "# app = App(vtk_data1)\n",
-    "# # asyncio.get_event_loop().run_until_complete(app.run())\n",
-    "# await app.run()"
-   ]
+   "execution_count": 5
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
    "id": "initial_id",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-13T19:23:31.973545Z",
-     "start_time": "2025-04-13T19:23:31.971919Z"
+     "end_time": "2026-03-28T13:22:40.059215Z",
+     "start_time": "2026-03-28T13:22:40.054395Z"
     }
    },
-   "outputs": [],
    "source": [
     "\n",
     "\n",
@@ -407,46 +437,48 @@
     "# # sim2_result.plotter.plot_slice_3d(time_index=0, channel_id=\"s0\")\n",
     "# # sim2_result.plotter.plot_slice_3d(time_index=0, channel_id=\"s1\")\n",
     "# # sim2_result.plotter.plot_concentrations()\n"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 4
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "fe9a9ec55e853f21",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-13T19:23:31.980863Z",
-     "start_time": "2025-04-13T19:23:31.979364Z"
+     "end_time": "2026-03-28T13:22:40.062662Z",
+     "start_time": "2026-03-28T13:22:40.059595Z"
     }
    },
+   "source": [],
    "outputs": [],
-   "source": []
+   "execution_count": 4
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "9adf3b911df4b628",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-13T19:23:31.988565Z",
-     "start_time": "2025-04-13T19:23:31.987299Z"
+     "end_time": "2026-03-28T13:22:40.065805Z",
+     "start_time": "2026-03-28T13:22:40.063Z"
     }
    },
+   "source": [],
    "outputs": [],
-   "source": []
+   "execution_count": 4
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "52fef2ac53b66666",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-13T19:23:31.995756Z",
-     "start_time": "2025-04-13T19:23:31.994581Z"
+     "end_time": "2026-03-28T13:22:40.071822Z",
+     "start_time": "2026-03-28T13:22:40.066167Z"
     }
    },
+   "source": [],
    "outputs": [],
-   "source": []
+   "execution_count": 4
   }
  ],
  "metadata": {
diff --git a/examples/notebooks/image_field_data_demo.ipynb b/examples/notebooks/image_field_data_demo.ipynb
new file mode 100644
index 0000000..7c56cac
--- /dev/null
+++ b/examples/notebooks/image_field_data_demo.ipynb
@@ -0,0 +1,411 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cell-0",
+   "metadata": {},
+   "source": [
+    "# Image Field Data Demo\n",
+    "\n",
+    "Use synthetic microscopy image data as initial conditions for a spatial simulation,\n",
+    "then visualize results with an interactive 3D Trame widget."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cell-1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-28T22:48:54.833270Z",
+     "start_time": "2026-03-28T22:48:52.977296Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.ndimage import gaussian_filter\n",
+    "import pyvcell.vcml as vc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-2",
+   "metadata": {},
+   "source": [
+    "## Load model and set up simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cell-3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-28T22:48:54.842961Z",
+     "start_time": "2026-03-28T22:48:54.834107Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from pathlib import Path\n",
+    "\n",
+    "model_path = Path(os.getcwd()).parent / \"models\" / \"Tutorial_MultiApp_PDE.vcml\"\n",
+    "biomodel = vc.load_vcml_file(model_path)\n",
+    "app = biomodel.applications[0]\n",
+    "sim = app.simulations[0]\n",
+    "sim.mesh_size = (100, 100, 36)\n",
+    "sim.duration = 2.0\n",
+    "sim.output_time_step = 0.2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-4",
+   "metadata": {},
+   "source": [
+    "## Build synthetic microscopy image from geometry domain mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cell-5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-28T22:50:34.583705Z",
+     "start_time": "2026-03-28T22:50:34.478413Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Image shape: (256, 256, 34), Mesh size: (100, 100, 36)\n"
+     ]
+    }
+   ],
+   "source": [
+    "seg = app.geometry.to_segmented_image(resolution=64)\n",
+    "mask = seg.get_mask(\"cytosol\").astype(float)\n",
+    "image_data = gaussian_filter(mask, sigma=3) + 0.05 * np.random.randn(*mask.shape)\n",
+    "\n",
+    "print(f\"Image shape: {image_data.shape}, Mesh size: {sim.mesh_size}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-6",
+   "metadata": {},
+   "source": [
+    "## Create field and use as initial condition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cell-7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-28T22:50:37.217035Z",
+     "start_time": "2026-03-28T22:50:37.186563Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial condition: vcField('microscopy', 'channel_1', 0.0, 'Volume')\n"
+     ]
+    }
+   ],
+   "source": [
+    "field = vc.Field(data_name=\"microscopy\", data_nD=image_data)\n",
+    "app.get_species_mapping(\"Ran_cyt\").init_conc = field.expression\n",
+    "\n",
+    "print(f\"Initial condition: {field.expression}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-8",
+   "metadata": {},
+   "source": [
+    "## Simulate\n",
+    "\n",
+    "The solver resamples the 64x64x64 image data to the 20x20x20 simulation mesh via trilinear interpolation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cell-9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-28T22:50:48.251904Z",
+     "start_time": "2026-03-28T22:50:46.903946Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-29T20:03:27.134719Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Please remove the `packages` attribute from your configuration file.\n",
+      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
+      "2026-03-29T20:03:27.139446Z main WARN The Logger cbit.vcell.model.Kinetics was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@369b79f7 and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
+      "2026-03-29T20:03:27.463309Z main WARN The Logger cbit.vcell.mapping.AbstractMathMapping was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@369b79f7 and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
+      "2026-03-29 16:03:27,468  INFO (Entrypoints.java:202) - Returning from vcellToVcml: {\"success\":true,\"message\":\"Success\"}\n",
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-29T20:03:27.491579Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Please remove the `packages` attribute from your configuration file.\n",
+      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
+      "2026-03-29T20:03:27.492418Z main WARN The Logger org.vcell.libvcell.Entrypoints was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@2308f230 and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
+      "2026-03-29T20:03:27.496158Z main WARN The Logger cbit.vcell.model.Kinetics was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@2308f230 and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
+      "2026-03-29T20:03:27.513091Z main WARN The Logger cbit.vcell.mapping.AbstractMathMapping was created with the message factory org.apache.logging.log4j.message.ReusableMessageFactory@2308f230 and is now requested with a null message factory (defaults to org.apache.logging.log4j.message.ParameterizedMessageFactory), which may create log events with unexpected formatting.\n",
+      "DataSet.read() open '/Users/jimschaff/Documents/workspace/pyvcell/examples/notebooks/workspace/out_dir_qir12gq4/SimID_1289029706_0_microscopy_channel_1_0_0_Volume.fdat'\n",
+      "2026-03-29 16:03:27,621  INFO (Entrypoints.java:85) - Returning from vcmlToFiniteVolumeInput: {\"success\":true,\"message\":\"Success\"}\n",
+      "Setting Base file name to: `\"/Users/jimschaff/Documents/workspace/pyvcell/examples/notebooks/workspace/out_dir_qir12gq4/SimID_1289029706_0_\"`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Simulation Complete in Main() ... \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initializing mesh\n",
+      "numVolume=360000\n",
+      "\n",
+      "CartesianMesh::computeNormalsFromNeighbors(), compute normals from neighbors\n",
+      "Membrane Elements -> N=16661\n",
+      "==============================================\n",
+      "qhull precision error: initial simplex is not convex. Distance=-1.2e-16\n",
+      "559 has 0 neighbors !\n",
+      "qhull precision warning: \n",
+      "1132 has 0 neighbors !\n",
+      "qhull precision warning: \n",
+      "2202 has 0 neighbors !\n",
+      "13777 has 0 neighbors !\n",
+      "--------Num of points that have zero neighbors 4\n",
+      "--------Num Neighbors before symmetrize 99119\n",
+      "--------Num Neighbors after symmetrize 108070\n",
+      "Total volume=6062.500738\n",
+      "Total FluxArea =47.45975686\n",
+      "Total FluxAreaXM =0\n",
+      "Total FluxAreaXP =0\n",
+      "Total FluxAreaYM =32.00361766\n",
+      "Total FluxAreaYP =15.45613919\n",
+      "Total FluxAreaZM =0\n",
+      "Total FluxAreaZP =0\n",
+      "mesh initialized\n",
+      "preprocessing finished\n",
+      "**This is a little endian machine.**\n",
+      "\n",
+      "read data for field 'microscopy'.\n",
+      "pdeCount=4, odeCount=0\n",
+      "No log-file found at constructed path `/Users/jimschaff/Documents/workspace/pyvcell/examples/notebooks/workspace/out_dir_qir12gq4/SimID_1289029706_0_.log`.simulation [SimID_1289029706_0_] started\n",
+      "temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_1289029706_0_0000.sim\n",
+      "[[[data:0]]]\n",
+      "numVolRegions=4\n",
+      "Region 0: size=315016, offset=0\n",
+      "Region 1: size=36021, offset=315016\n",
+      "Region 2: size=1, offset=351037\n",
+      "Region 3: size=8962, offset=351038\n",
+      "# of active points = 360000\n",
+      "Constant diffusion/advection in region cytosol1\n",
+      "Constant diffusion/advection in region cytosol2\n",
+      "Constant diffusion/advection in region Nucleus3\n",
+      "numUnknowns = 117028\n",
+      "\n",
+      "****** using Sundials CVode with PREC_LEFT, relTol=1e-07, absTol=1e-09, maxStep=0.1\n",
+      "\n",
+      "----------------------------------\n",
+      "sundials pde solver is starting from time 0\n",
+      "----------------------------------\n",
+      "temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_1289029706_0_0001.sim\n",
+      "[[[data:0.2]]]SimTool.start1() sending JOB_PROGRESS to SimulationMessaging: percentile=0.1, time=0.2\n",
+      "[[[progress:10%]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_1289029706_0_0002.sim\n",
+      "[[[data:0.4]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_1289029706_0_0003.sim\n",
+      "[[[data:0.6]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_1289029706_0_0004.sim\n",
+      "[[[data:0.8]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_1289029706_0_0005.sim\n",
+      "[[[data:1]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_1289029706_0_0006.sim\n",
+      "[[[data:1.2]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_1289029706_0_0007.sim\n",
+      "[[[data:1.4]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_1289029706_0_0008.sim\n",
+      "[[[data:1.6]]]temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_1289029706_0_0009.sim\n",
+      "[[[data:1.8]]]\n",
+      "Final Statistics.. \n",
+      "\n",
+      "lenrw   = 1170369     leniw   =    50\n",
+      "lenrwLS = 1170326     leniwLS =    10\n",
+      "nst     =   238\n",
+      "nfe     =   282     nfeLS   =   299\n",
+      "nni     =   278     nli     =   299\n",
+      "nsetups =    48     netf    =     6\n",
+      "npe     =     5     nps     =   542\n",
+      "ncfn    =     0     ncfl    =     1\n",
+      "last step  = 0.007572\n",
+      "\n",
+      "temporary directory used is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/\n",
+      "sim file name is /var/folders/zz/gcfcvgtd5v1cdgj2sw4bjzdr0000gr/T/SimID_1289029706_0_0010.sim\n",
+      "[[[data:2]]][[[progress:100%]]]Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-29T20:03:33.107014Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Please remove the `packages` attribute from your configuration file.\n",
+      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
+      "2026-03-29 16:03:33,107  INFO (Entrypoints.java:274) - Returning from vcellInfixToNumExprInfix: {\"success\":true,\"message\":\"Success\"}\n",
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-29T20:03:33.121532Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Please remove the `packages` attribute from your configuration file.\n",
+      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
+      "2026-03-29 16:03:33,122  INFO (Entrypoints.java:274) - Returning from vcellInfixToNumExprInfix: {\"success\":true,\"message\":\"Success\"}\n",
+      "Time points: [0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0]\n",
+      "Channels: ['region_mask', 't', 'x', 'y', 'z', 'C_cyt', 'Ran_cyt', 'RanC_cyt', 'RanC_nuc', 'J_r0']\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = vc.simulate(biomodel, sim.name, fields=[field])\n",
+    "\n",
+    "print(f\"Time points: {result.time_points}\")\n",
+    "print(f\"Channels: {[c.label for c in result.channel_data]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-10",
+   "metadata": {},
+   "source": [
+    "## Visualize results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cell-11",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-28T22:50:51.702909Z",
+     "start_time": "2026-03-28T22:50:51.483014Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.plotter.plot_concentrations()\n",
+    "result.plotter.plot_slice_3d(time_index=0, channel_id=\"Ran_cyt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-12",
+   "metadata": {},
+   "source": [
+    "## Interactive 3D visualization with Trame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cell-13",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-28T22:51:05.188503Z",
+     "start_time": "2026-03-28T22:51:04.297786Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "128798dce17441e48e009950ab3dd909",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HTML(value='<iframe id=\"trame_trame__template_main\" src=\"http://localhost:8889/trame-jupyter-server/servers/tr…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pyvcell.sim_results.widget as widget\n",
+    "\n",
+    "viewer = widget.App(result.vtk_data)\n",
+    "layout = await viewer.run()\n",
+    "display(layout.ipywidget)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81bcb5c2-7567-4e8c-94df-0a67467b9e94",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/notebooks/widget.ipynb b/examples/notebooks/widget.ipynb
index a03f94f..5813192 100644
--- a/examples/notebooks/widget.ipynb
+++ b/examples/notebooks/widget.ipynb
@@ -2,30 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
    "id": "92ac6f0eb409f41",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-03-21T23:23:46.759746Z",
-     "start_time": "2025-03-21T23:23:44.598316Z"
+     "end_time": "2026-03-28T12:34:05.406755Z",
+     "start_time": "2026-03-28T12:34:01.042170Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4617d56774e5457f91d214895456ece2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HTML(value='<iframe id=\"trame_trame__template_main\" src=\"http://localhost:8889/trame-jupyter-server/servers/tr…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "import os\n",
     "from pathlib import Path\n",
@@ -43,7 +26,598 @@
     "# asyncio.get_event_loop().run_until_complete(app.run())\n",
     "\n",
     "await app.run()"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-28T12:34:02.170994Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Please remove the `packages` attribute from your configuration file.\n",
+      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
+      "2026-03-28 08:34:02,172  INFO (Entrypoints.java:274) - Returning from vcellInfixToNumExprInfix: {\"success\":true,\"message\":\"Success\"}\n",
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-28T12:34:02.188231Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Please remove the `packages` attribute from your configuration file.\n",
+      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
+      "2026-03-28 08:34:02,189  INFO (Entrypoints.java:274) - Returning from vcellInfixToNumExprInfix: {\"success\":true,\"message\":\"Success\"}\n",
+      "Found shared library: /Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/libvcell/lib/libvcell.dylib\n",
+      "2026-03-28T12:34:02.203978Z main WARN The use of package scanning to locate Log4j plugins is deprecated.\n",
+      "Please remove the `packages` attribute from your configuration file.\n",
+      "See https://logging.apache.org/log4j/2.x/faq.html#package-scanning for details.\n",
+      "2026-03-28 08:34:02,204  INFO (Entrypoints.java:274) - Returning from vcellInfixToNumExprInfix: {\"success\":true,\"message\":\"Success\"}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "HTML(value='<iframe id=\"trame_trame__template_main\" src=\"http://localhost:51656/index.html?ui=main&reconnect=a…"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "736e3e8139994843b0e00ac7e3a7cec6"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Exception raised\n",
+      "ValueError('2 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 2 is not in list\n",
+      "\n",
+      "Exception raised\n",
+      "ValueError('3 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 3 is not in list\n",
+      "\n",
+      "Exception raised\n",
+      "ValueError('3 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 3 is not in list\n",
+      "\n",
+      "Exception raised\n",
+      "ValueError('3 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 3 is not in list\n",
+      "\n",
+      "Exception raised\n",
+      "ValueError('3 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 3 is not in list\n",
+      "\n",
+      "Exception raised\n",
+      "ValueError('3 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 3 is not in list\n",
+      "\n",
+      "Exception raised\n",
+      "ValueError('3 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 3 is not in list\n",
+      "\n",
+      "Exception raised\n",
+      "ValueError('3 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 3 is not in list\n",
+      "\n",
+      "Exception raised\n",
+      "ValueError('3 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 3 is not in list\n",
+      "\n",
+      "Exception raised\n",
+      "ValueError('3 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 3 is not in list\n",
+      "\n",
+      "Exception raised\n",
+      "ValueError('3 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 3 is not in list\n",
+      "\n",
+      "Exception raised\n",
+      "ValueError('3 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 3 is not in list\n",
+      "\n",
+      "Exception raised\n",
+      "ValueError('3 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 3 is not in list\n",
+      "\n",
+      "Exception raised\n",
+      "ValueError('3 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 3 is not in list\n",
+      "\n",
+      "Exception raised\n",
+      "ValueError('3 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 3 is not in list\n",
+      "\n",
+      "Exception raised\n",
+      "ValueError('3 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 3 is not in list\n",
+      "\n",
+      "Exception raised\n",
+      "ValueError('2 is not in list')\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/wslink/protocol.py\", line 317, in onCompleteMessage\n",
+      "    results = func(*args, **kwargs)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/protocol.py\", line 236, in update_state\n",
+      "    with self.server.state:\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 354, in __exit__\n",
+      "    self.flush()\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 339, in flush\n",
+      "    keys |= self._flush_pending_keys()\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/.venv/lib/python3.11/site-packages/trame_server/state.py\", line 319, in _flush_pending_keys\n",
+      "    coroutine = callback(**reverse_translated_state)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 48, in update_time\n",
+      "    self.plot_data(self.state.variable, time_index, self.state.clip_level)\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/sim_results/widget.py\", line 58, in plot_data\n",
+      "    dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)\n",
+      "                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 336, in get_data\n",
+      "    zip_file_entry: DataZipFileMetadata = self._get_data_zip_file_metadata(time)\n",
+      "                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 328, in _get_data_zip_file_metadata\n",
+      "    time_index = self.time_index(time)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/jimschaff/Documents/workspace/pyvcell/pyvcell/_internal/simdata/simdata_models.py\", line 310, in time_index\n",
+      "    return self.data_times.index(time)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: 2 is not in list\n",
+      "\n"
+     ]
+    }
+   ],
+   "execution_count": 1
   },
   {
    "cell_type": "code",
diff --git a/examples/scripts/image_field_data_demo.py b/examples/scripts/image_field_data_demo.py
new file mode 100644
index 0000000..a885692
--- /dev/null
+++ b/examples/scripts/image_field_data_demo.py
@@ -0,0 +1,36 @@
+"""Demo: use synthetic microscopy image data as initial conditions for a spatial simulation."""
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+import numpy as np
+from scipy.ndimage import gaussian_filter
+
+import pyvcell.vcml as vc
+
+# Load model — Tutorial_MultiApp_PDE has ec, cytosol, and Nucleus domains
+biomodel = vc.load_vcml_file("examples/models/Tutorial_MultiApp_PDE.vcml")
+app = biomodel.applications[0]
+sim = app.simulations[0]
+sim.mesh_size = (100, 100, 36)
+sim.duration = 2.0
+sim.output_time_step = 0.2
+
+# Build a synthetic "microscopy image" from the geometry's cytosol domain mask
+seg = app.geometry.to_segmented_image(resolution=64)
+mask = seg.get_mask("cytosol").astype(float)
+image_data = gaussian_filter(mask, sigma=3) + 0.05 * np.random.randn(*mask.shape)
+
+# Create a field and use it as initial condition for Ran_cyt
+field = vc.Field(data_name="microscopy", data_nD=image_data)
+app.get_species_mapping("Ran_cyt").init_conc = field.expression
+
+# Simulate — solver resamples 64^3 field data to 30^3 simulation mesh via trilinear interpolation
+result = vc.simulate(biomodel, sim.name, fields=[field])
+
+# Show results
+print(f"Time points: {result.time_points}")
+print(f"Channels: {[c.label for c in result.channel_data]}")
+result.plotter.plot_concentrations()
+result.plotter.plot_slice_3d(time_index=0, channel_id="Ran_cyt")
diff --git a/poetry.lock b/poetry.lock
index d712e94..9e48221 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -782,7 +782,7 @@ version = "1.3.3"
 description = "Python library for calculating contours of 2D quadrilateral grids"
 optional = false
 python-versions = ">=3.11"
-groups = ["main"]
+groups = ["main", "dev"]
 files = [
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@@ -868,6 +868,33 @@ mypy = ["bokeh", "contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.17.0)", "
 test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
 test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "wurlitzer"]
 
+[[package]]
+name = "copasi-basico"
+version = "0.86"
+description = "Simplified COPASI interface for python"
+optional = false
+python-versions = "*"
+groups = ["dev"]
+files = [
+    {file = "copasi_basico-0.86-py3-none-any.whl", hash = "sha256:d92b93ad28edcd84b83962913d1fc973906ca612c01e954007d23a9031167058"},
+    {file = "copasi_basico-0.86.tar.gz", hash = "sha256:de3090ee3bb46a64953ab35b759023d20081a29d518eb3a5d0377fe23765f336"},
+]
+
+[package.dependencies]
+lxml = "*"
+matplotlib = "*"
+numpy = "*"
+pandas = "*"
+python-copasi = "*"
+PyYAML = "*"
+scipy = "*"
+
+[package.extras]
+docs = ["sphinx", "sphinx_rtd_theme", "sphinxcontrib", "sphinxcontrib-bibtex", "sphinxcontrib-mermaid", "sphinxcontrib-plantuml", "sphinxcontrib-programoutput"]
+petab = ["copasi-petab-importer", "petab", "petab-select"]
+ssr = ["libssr", "python-libsbml"]
+tqdm = ["tqdm"]
+
 [[package]]
 name = "coverage"
 version = "7.13.5"
@@ -1065,7 +1092,7 @@ version = "0.12.1"
 description = "Composable style cycles"
 optional = false
 python-versions = ">=3.8"
-groups = ["main"]
+groups = ["main", "dev"]
 files = [
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     {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"},
@@ -1255,7 +1282,7 @@ version = "4.62.1"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.10"
-groups = ["main"]
+groups = ["main", "dev"]
 files = [
     {file = "fonttools-4.62.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:ad5cca75776cd453b1b035b530e943334957ae152a36a88a320e779d61fc980c"},
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@@ -2262,7 +2289,7 @@ version = "1.5.0"
 description = "A fast implementation of the Cassowary constraint solver"
 optional = false
 python-versions = ">=3.10"
-groups = ["main"]
+groups = ["main", "dev"]
 files = [
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@@ -2533,7 +2560,7 @@ version = "5.4.0"
 description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
 optional = false
 python-versions = ">=3.6"
-groups = ["main"]
+groups = ["main", "dev"]
 files = [
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@@ -2836,7 +2863,7 @@ version = "3.10.8"
 description = "Python plotting package"
 optional = false
 python-versions = ">=3.10"
-groups = ["main"]
+groups = ["main", "dev"]
 files = [
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@@ -3805,7 +3832,7 @@ version = "2.4.3"
 description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.11"
-groups = ["main"]
+groups = ["main", "dev"]
 files = [
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@@ -4021,6 +4048,98 @@ files = [
 dev = ["pytest", "tox"]
 lint = ["black"]
 
+[[package]]
+name = "pandas"
+version = "3.0.1"
+description = "Powerful data structures for data analysis, time series, and statistics"
+optional = false
+python-versions = ">=3.11"
+groups = ["dev"]
+files = [
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+
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+timezone = ["pytz (>=2024.2)"]
+xml = ["lxml (>=5.3.0)"]
+
 [[package]]
 name = "pandocfilters"
 version = "1.5.1"
@@ -4089,7 +4208,7 @@ version = "12.1.1"
 description = "Python Imaging Library (fork)"
 optional = false
 python-versions = ">=3.10"
-groups = ["main"]
+groups = ["main", "dev"]
 files = [
     {file = "pillow-12.1.1-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:1f1625b72740fdda5d77b4def688eb8fd6490975d06b909fd19f13f391e077e0"},
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@@ -4500,7 +4619,7 @@ files = [
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 ]
-markers = {main = "platform_python_implementation != \"PyPy\" and implementation_name != \"PyPy\"", dev = "implementation_name != \"PyPy\"", docs = "implementation_name == \"pypy\""}
+markers = {main = "implementation_name != \"PyPy\" and platform_python_implementation != \"PyPy\"", dev = "implementation_name != \"PyPy\"", docs = "implementation_name == \"pypy\""}
 
 [[package]]
 name = "pydantic"
@@ -4698,7 +4817,7 @@ version = "3.3.2"
 description = "pyparsing - Classes and methods to define and execute parsing grammars"
 optional = false
 python-versions = ">=3.9"
-groups = ["main"]
+groups = ["main", "dev"]
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 [package.extras]
 testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtualenv"]
 
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+
 [[package]]
 name = "python-dateutil"
 version = "2.9.0.post0"
@@ -5397,6 +5562,85 @@ files = [
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 ]
 
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+groups = ["dev"]
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+    {file = "scipy-1.17.1-cp314-cp314t-macosx_10_14_x86_64.whl", hash = "sha256:1cc682cea2ae55524432f3cdff9e9a3be743d52a7443d0cba9017c23c87ae2f6"},
+    {file = "scipy-1.17.1-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:2040ad4d1795a0ae89bfc7e8429677f365d45aa9fd5e4587cf1ea737f927b4a1"},
+    {file = "scipy-1.17.1-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:131f5aaea57602008f9822e2115029b55d4b5f7c070287699fe45c661d051e39"},
+    {file = "scipy-1.17.1-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:9cdc1a2fcfd5c52cfb3045feb399f7b3ce822abdde3a193a6b9a60b3cb5854ca"},
+    {file = "scipy-1.17.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6e3dcd57ab780c741fde8dc68619de988b966db759a3c3152e8e9142c26295ad"},
+    {file = "scipy-1.17.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a9956e4d4f4a301ebf6cde39850333a6b6110799d470dbbb1e25326ac447f52a"},
+    {file = "scipy-1.17.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:a4328d245944d09fd639771de275701ccadf5f781ba0ff092ad141e017eccda4"},
+    {file = "scipy-1.17.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:a77cbd07b940d326d39a1d1b37817e2ee4d79cb30e7338f3d0cddffae70fcaa2"},
+    {file = "scipy-1.17.1-cp314-cp314t-win_amd64.whl", hash = "sha256:eb092099205ef62cd1782b006658db09e2fed75bffcae7cc0d44052d8aa0f484"},
+    {file = "scipy-1.17.1-cp314-cp314t-win_arm64.whl", hash = "sha256:200e1050faffacc162be6a486a984a0497866ec54149a01270adc8a59b7c7d21"},
+    {file = "scipy-1.17.1.tar.gz", hash = "sha256:95d8e012d8cb8816c226aef832200b1d45109ed4464303e997c5b13122b297c0"},
+]
+
+[package.dependencies]
+numpy = ">=1.26.4,<2.7"
+
+[package.extras]
+dev = ["click (<8.3.0)", "cython-lint (>=0.12.2)", "mypy (==1.10.0)", "pycodestyle", "ruff (>=0.12.0)", "spin", "types-psutil", "typing_extensions"]
+doc = ["intersphinx_registry", "jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.19.1)", "jupytext", "linkify-it-py", "matplotlib (>=3.5)", "myst-nb (>=1.2.0)", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<8.2.0)", "sphinx-copybutton", "sphinx-design (>=0.4.0)", "tabulate"]
+test = ["Cython", "array-api-strict (>=2.3.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja ; sys_platform != \"emscripten\"", "pooch", "pytest (>=8.0.0)", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
 [[package]]
 name = "scooby"
 version = "0.11.0"
@@ -6372,4 +6616,4 @@ jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.11,<4.0"
-content-hash = "f46b382dbfd14dce0832a5dcdbdb22dfa1dc2860b47f4bf61c8bc1a173af5abb"
+content-hash = "e691fc1fdfafb6e3b99e14a8c35eac6869e2bd86c81770312ccd3bd918c259e8"
diff --git a/pyproject.toml b/pyproject.toml
index 7ea2a9b..e134cf6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pyvcell"
-version = "0.1.22"
+version = "0.2.0"
 description = "This is the python wrapper for vcell modeling and simulation"
 authors = ["Jim Schaff <schaff@uchc.edu>"]
 repository = "https://github.com/virtualcell/pyvcell"
@@ -53,6 +53,7 @@ notebook = "^7.3.2"
 lxml-stubs = "^0.5.1"
 trame-jupyter-extension = "^2.1.4"
 ipywidgets = "^8.1.5"
+copasi-basico = ">=0.65"
 
 [tool.poetry.group.docs.dependencies]
 mkdocs = "^1.6.1"
@@ -84,6 +85,13 @@ disable_error_code = [
     "no-any-return", "return"
 ]
 
+[[tool.mypy.overrides]]
+module = "scipy.*"
+ignore_missing_imports = true
+disable_error_code = [
+    "import-untyped"
+]
+
 [[tool.mypy.overrides]]
 module = "trame.*"
 ignore_missing_imports = true
diff --git a/pyvcell/_internal/geometry/segmented_image_geometry.py b/pyvcell/_internal/geometry/segmented_image_geometry.py
index c4b5a00..2de8b6f 100644
--- a/pyvcell/_internal/geometry/segmented_image_geometry.py
+++ b/pyvcell/_internal/geometry/segmented_image_geometry.py
@@ -48,6 +48,21 @@ def __init__(
         self.spacing = spacing
         self.label_names = label_names
 
+    def get_mask(self, subvolume_name: str) -> npt.NDArray[np.bool_]:
+        """Return a boolean mask for the named subvolume.
+
+        Args:
+            subvolume_name: Name of the subvolume (e.g. ``"cell_domain"``).
+
+        Returns:
+            Boolean array with the same shape as :attr:`labels`.
+        """
+        for label, name in self.label_names.items():
+            if name == subvolume_name:
+                return self.labels == label  # type: ignore[no-any-return]
+        available = list(self.label_names.values())
+        raise ValueError(f"Subvolume '{subvolume_name}' not found. Available: {available}")
+
     def to_pyvista(self) -> pv.ImageData:
         """Convert to a PyVista ImageData with cell data 'subvolume'."""
         import pyvista as pv
diff --git a/pyvcell/sim_results/widget.py b/pyvcell/sim_results/widget.py
index 127d1fe..4e2b947 100644
--- a/pyvcell/sim_results/widget.py
+++ b/pyvcell/sim_results/widget.py
@@ -1,3 +1,4 @@
+import time as _time
 from typing import Any
 
 import pyvista as pv
@@ -37,29 +38,84 @@ def __init__(self, vtk_data: VtkData, notebook: bool = True):
 
         self.pl = pv.Plotter(notebook=notebook)
 
-        self.server.state.change("clip_level")(self.update_clipping)
-        self.server.state.change("variable")(self.update_variable)
-        self.server.state.change("time_index")(self.update_time)
-
-    def update_variable(self, variable: str, **kwargs: Any) -> None:
-        self.plot_data(variable, self.state.time_index, self.state.clip_level)
-
-    def update_time(self, time_index: int, **kwargs: Any) -> None:
-        self.plot_data(self.state.variable, time_index, self.state.clip_level)
-
-    def update_clipping(self, clip_level: float | int, **kwargs: Any) -> None:
-        self.plot_data(self.state.variable, self.state.time_index, clip_level)
-
-    def plot_data(self, variable: str, time_index: int, clip_level: float | int) -> None:
-        # choose empty vtu file
+        self._init_state()
+        self._bind_state_changes()
+
+    def _init_state(self) -> None:
+        """Initialize or update state, preserving valid selections from prior runs."""
+        state_dict: dict[str, Any] = self.state.to_dict()
+
+        self.state.variables = self.variables
+
+        # Preserve variable if still valid
+        prev_variable = state_dict.get("variable")
+        self.state.variable = prev_variable if prev_variable in self.variables else self.variables[0]
+
+        # Preserve time_index if still in range
+        max_time_index = len(self.time_points) - 1
+        prev_time_index = state_dict.get("time_index")
+        if isinstance(prev_time_index, (int, float)) and 0 <= int(prev_time_index) <= max_time_index:
+            self.state.time_index = int(prev_time_index)
+        else:
+            self.state.time_index = 0
+
+        # Preserve clip_level (always valid — it's a 0-1 ratio)
+        prev_clip = state_dict.get("clip_level")
+        if isinstance(prev_clip, (int, float)):
+            self.state.clip_level = float(prev_clip)
+        else:
+            self.state.clip_level = 0.5
+
+        self.state.time_max = max_time_index
+        self.state.status = "Ready"
+
+    def _bind_state_changes(self) -> None:
+        """Bind UI state changes to update callbacks."""
+        self.server.state.change("clip_level")(self._on_clip_change)
+        self.server.state.change("variable")(self._on_variable_change)
+        self.server.state.change("time_index")(self._on_time_change)
+
+    def _on_variable_change(self, variable: str, **kwargs: Any) -> None:
+        self.state.status = f"Updating variable: {variable}..."
+        try:
+            self._render_current()
+        except Exception as e:
+            self.state.status = f"Error: {e}"
+
+    def _on_time_change(self, time_index: int, **kwargs: Any) -> None:
+        self.state.status = f"Updating time: {time_index}..."
+        try:
+            self._render_current()
+        except Exception as e:
+            self.state.status = f"Error: {e}"
+
+    def _on_clip_change(self, clip_level: float | int, **kwargs: Any) -> None:
+        self.state.status = f"Updating clip: {clip_level}..."
+        try:
+            self._render_current()
+        except Exception as e:
+            self.state.status = f"Error: {e}"
+
+    def _render_current(self) -> None:
+        """Render using current state values."""
+        variable = str(self.state.variable)
+        time_index = int(self.state.time_index)
+        clip_level = float(self.state.clip_level)
+        self._render(variable, time_index, clip_level)
+
+    def _render(self, variable: str, time_index: int, clip_level: float) -> None:
+        """Load data and render the 3D view."""
+        t0 = _time.monotonic()
+        time_value = self.time_points[time_index]
+
+        # Load mesh and data
         domain_name: str = variable.split("::")[0]
         empty_mesh: vtk.vtkUnstructuredGrid = self.vtk_data.get_vtk_grid(domain_name=domain_name)
-        # get cell data
-        dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_index)
+        dense_cell_data: NDArray1D = self.vtk_data.pde_dataset.get_data(variable, time_value)
         index_map: NDArray1Du32 = self.vtk_data.global_index_map[domain_name]
-        # resample dense_cell_data using index_map to get cell_data
         cell_data = dense_cell_data[index_map]
-        # create pyvista mesh from empty mesh and cell data
+
+        # Build clipped mesh
         mesh = pv.wrap(empty_mesh)
         mesh.cell_data[variable] = cell_data
         self.source = mesh
@@ -68,6 +124,7 @@ def plot_data(self, variable: str, time_index: int, clip_level: float | int) ->
         clip_position = bounds[0] + clip_level * (bounds[1] - bounds[0])
         clipped = self.source.clip_box(bounds=(clip_position,) + bounds[1:])
 
+        # Render
         self.pl.clear()
         self.pl.add_mesh(clipped, name=variable, show_scalar_bar=True)
         self.pl.show_bounds()
@@ -76,7 +133,10 @@ def plot_data(self, variable: str, time_index: int, clip_level: float | int) ->
         self.pl.render()
         self.ctrl.view_update()
 
-    async def run(self) -> SinglePageLayout:
+        elapsed = _time.monotonic() - t0
+        self.state.status = f"{variable} t={time_value:.2f} clip={clip_level:.2f} ({elapsed:.2f}s)"
+
+    async def run(self, height: int = 1000) -> SinglePageLayout:
         with SinglePageLayout(self.server) as layout:
             with layout.toolbar:
                 with vuetify3.VRow(
@@ -85,8 +145,8 @@ async def run(self) -> SinglePageLayout:
                     classes="w-100",
                 ):
                     vuetify3.VSelect(
-                        v_model=("variable", self.variables[0]),
-                        items=("variables", self.variables),
+                        v_model=("variable",),
+                        items=("variables",),
                         label="Variable",
                         dense=True,
                         hide_details=True,
@@ -94,9 +154,9 @@ async def run(self) -> SinglePageLayout:
                     )
                     vuetify3.VSpacer()
                     vuetify3.VSlider(
-                        v_model=("time_index", 0),
+                        v_model=("time_index",),
                         min=0,
-                        max=len(self.time_points) - 1,
+                        max=("time_max",),
                         step=1,
                         label="Time",
                         dense=True,
@@ -105,14 +165,14 @@ async def run(self) -> SinglePageLayout:
                     )
                     vuetify3.VSpacer()
                     vuetify3.VSlider(
-                        v_model=("clip_level", 0.5),
+                        v_model=("clip_level",),
                         min=0.0,
                         max=1.0,
                         step=0.05,
                         label="Clip",
                         hide_details=False,
                         density="compact",
-                        style="max-width: 300px; margin: auto;",  # "flex-grow: 1;",
+                        style="max-width: 300px; margin: auto;",
                     )
                     vuetify3.VSpacer()
 
@@ -123,6 +183,9 @@ async def run(self) -> SinglePageLayout:
                     active=("trame__busy",),
                 )
 
+            with layout.footer:
+                vuetify3.VLabel("{{ status }}", classes="text-caption pa-1")
+
             with (
                 layout.content,
                 vuetify3.VContainer(
@@ -133,7 +196,9 @@ async def run(self) -> SinglePageLayout:
                 view: PyVistaLocalView = plotter_ui(self.pl)  # type: ignore[no-untyped-call]
                 self.ctrl.view_update = view.update
 
+        layout.iframe_style = f"width: 100%; height: {height}px; border: none;"
         await layout.ready
+        self._render_current()
         return layout
 
 
diff --git a/pyvcell/vcml/__init__.py b/pyvcell/vcml/__init__.py
index 793dd78..d9a389b 100644
--- a/pyvcell/vcml/__init__.py
+++ b/pyvcell/vcml/__init__.py
@@ -24,12 +24,11 @@
     VCMLDocument,
     Version,
 )
+from pyvcell.vcml.session import SimulationJob, VCellSession
 from pyvcell.vcml.utils import (
     field_data_refs,
-    list_biomodels,
     load_antimony_file,
     load_antimony_str,
-    load_biomodel,
     load_sbml_file,
     load_sbml_str,
     load_sbml_url,
@@ -47,7 +46,7 @@
     write_vcml_file,
 )
 from pyvcell.vcml.vcml_reader import VcmlReader
-from pyvcell.vcml.vcml_remote import export_n5, run_remote, save_and_start, wait_for_simulation
+from pyvcell.vcml.vcml_remote import connect, logout
 from pyvcell.vcml.vcml_simulation import simulate
 from pyvcell.vcml.vcml_writer import VcmlWriter
 from pyvcell.vcml.workspace import get_workspace_dir, set_workspace_dir
@@ -95,13 +94,11 @@
     "get_workspace_dir",
     "set_workspace_dir",
     "simulate",
-    "run_remote",
-    "save_and_start",
-    "wait_for_simulation",
-    "export_n5",
+    "connect",
+    "logout",
+    "VCellSession",
+    "SimulationJob",
     "Field",
-    "list_biomodels",
-    "load_biomodel",
     "load_vcml_url",
     "load_sbml_url",
     "suppress_stdout",
diff --git a/pyvcell/vcml/field.py b/pyvcell/vcml/field.py
index 72f265a..7ea2311 100644
--- a/pyvcell/vcml/field.py
+++ b/pyvcell/vcml/field.py
@@ -19,11 +19,35 @@ class Field:
     time: float
     data_nD: NDArrayND
 
+    def __init__(
+        self,
+        data_name: str = "",
+        var_name: str = "channel_1",
+        time: float = 0.0,
+        data_nD: NDArrayND | None = None,
+    ) -> None:
+        self.data_name = data_name
+        self.var_name = var_name
+        self.time = time
+        self.data_nD = data_nD if data_nD is not None else np.zeros(0, dtype=np.float64)
+
     def __repr__(self) -> str:
         return (
             f"Field(data_name={self.data_name}, var_name={self.var_name}, time={self.time}, shape={self.data_nD.shape})"
         )
 
+    @property
+    def expression(self) -> str:
+        """Return the ``vcField()`` expression string for use in initial condition formulas.
+
+        Example::
+
+            field = fields[0]
+            species.init_conc = field.expression
+            species.init_conc = f"4.0 * {field.expression} / 7.0"
+        """
+        return f"vcField('{self.data_name}', '{self.var_name}', {self.time}, 'Volume')"
+
     def write(self, file_path: Path) -> None:
         var_info = VariableInfo(var_name=self.var_name, variable_type=VariableType.VOLUME)
         field_data_file = FieldDataFile.from_image(data_nD=self.data_nD, var_info=var_info)
diff --git a/pyvcell/vcml/models.py b/pyvcell/vcml/models.py
index 6937303..d04dbdf 100644
--- a/pyvcell/vcml/models.py
+++ b/pyvcell/vcml/models.py
@@ -324,7 +324,14 @@ def to_segmented_image(self, resolution: int = 50) -> SegmentedImageGeometry:
                 .reshape((sz, sy, sx))  # [z, y, x]
                 .transpose((2, 1, 0))  # [x, y, z] = (nx, ny, nz)
             )
-            label_names = {pc.pixel_value: pc.name for pc in self.image.pixel_classes}
+            # Map pixel values to subvolume names (subvolumes have image_pixel_value matching pixel classes)
+            pixel_to_subvolume = {
+                sv.image_pixel_value: sv.name for sv in self.subvolumes if sv.image_pixel_value is not None
+            }
+            label_names = {
+                pv: pixel_to_subvolume.get(pv, pc_name)
+                for pv, pc_name in ((pc.pixel_value, pc.name) for pc in self.image.pixel_classes)
+            }
         else:
             nx = ny = nz = resolution
             x = np.linspace(ox + ex / (2 * nx), ox + ex - ex / (2 * nx), nx)
@@ -462,6 +469,13 @@ def map_species(self, species: Species | str, init_conc: float | str, diff_coef:
         self.species_mappings.append(species_mapping)
         return species_mapping
 
+    def get_species_mapping(self, species_name: str) -> SpeciesMapping:
+        """Get a species mapping by name."""
+        for sm in self.species_mappings:
+            if sm.species_name == species_name:
+                return sm
+        raise ValueError(f"Species mapping '{species_name}' not found in application '{self.name}'")
+
     def map_compartment(self, compartment: Compartment | str, domain: GeometryClass | str) -> CompartmentMapping:
         compartment_name = compartment.name if isinstance(compartment, Compartment) else compartment
         domain_name = domain.name if isinstance(domain, GeometryClass) else domain
diff --git a/pyvcell/vcml/session.py b/pyvcell/vcml/session.py
new file mode 100644
index 0000000..30e8de1
--- /dev/null
+++ b/pyvcell/vcml/session.py
@@ -0,0 +1,310 @@
+from __future__ import annotations
+
+from collections.abc import Callable
+
+from tensorstore._tensorstore import TensorStore  # type: ignore[import-not-found]
+
+from pyvcell._internal.api.vcell_client.api.simulation_resource_api import SimulationResourceApi
+from pyvcell._internal.api.vcell_client.api_client import ApiClient
+from pyvcell._internal.api.vcell_client.models.status import Status
+from pyvcell.vcml.models import Biomodel, Simulation
+
+
+class SimulationJob:
+    """Handle to a running remote simulation.
+
+    Returned by :meth:`VCellSession.start_sim`. Provides methods to check
+    status, wait for completion, and export results.
+    """
+
+    def __init__(
+        self,
+        api_client: ApiClient,
+        saved_biomodel: Biomodel,
+        saved_simulation: Simulation,
+        variable_names: list[str] | None = None,
+        dataset_name: str | None = None,
+        poll_interval: float = 5.0,
+        timeout: float | None = None,
+        on_progress: Callable[[str], None] | None = None,
+    ) -> None:
+        self._api_client = api_client
+        self._saved_biomodel = saved_biomodel
+        self._saved_simulation = saved_simulation
+        self._variable_names = variable_names
+        self._dataset_name = dataset_name
+        self._poll_interval = poll_interval
+        self._timeout = timeout
+        self._on_progress = on_progress
+
+    @property
+    def biomodel(self) -> Biomodel:
+        """The saved biomodel with server-assigned version keys."""
+        return self._saved_biomodel
+
+    @property
+    def simulation(self) -> Simulation:
+        """The saved simulation with server-assigned version keys."""
+        return self._saved_simulation
+
+    @property
+    def status(self) -> Status:
+        """Poll the server once and return the current simulation status."""
+        if self._saved_biomodel.version is None or self._saved_biomodel.version.key is None:
+            raise ValueError("biomodel is missing a version key")
+        if self._saved_simulation.version is None or self._saved_simulation.version.key is None:
+            raise ValueError("simulation is missing a version key")
+
+        sim_api = SimulationResourceApi(self._api_client)
+        record = sim_api.get_simulation_status(
+            sim_id=self._saved_simulation.version.key,
+            bio_model_id=self._saved_biomodel.version.key,
+        )
+        status: Status = record.status  # type: ignore[assignment]
+        return status
+
+    def wait(self, poll_interval: float | None = None, timeout: float | None = None) -> None:
+        """Block until the simulation reaches a terminal state.
+
+        Args:
+            poll_interval: Override the default poll interval (seconds).
+            timeout: Override the default timeout (seconds).
+
+        Raises:
+            RuntimeError: If the simulation fails, is stopped, or times out.
+        """
+        from pyvcell.vcml.vcml_remote import wait_for_simulation
+
+        wait_for_simulation(
+            self._saved_biomodel,
+            self._saved_simulation,
+            poll_interval=poll_interval or self._poll_interval,
+            timeout=timeout if timeout is not None else self._timeout,
+            on_progress=self._on_progress,
+            api_client=self._api_client,
+        )
+
+    def export(
+        self,
+        variable_names: list[str] | None = None,
+        dataset_name: str | None = None,
+        poll_interval: float | None = None,
+        timeout: float | None = None,
+    ) -> TensorStore:
+        """Export simulation results as N5 and open as a TensorStore.
+
+        Args:
+            variable_names: Override the variables to export.
+            dataset_name: Override the N5 dataset name.
+            poll_interval: Override the default poll interval (seconds).
+            timeout: Override the default timeout (seconds).
+
+        Returns:
+            A TensorStore pointing to the exported N5 data.
+        """
+        from pyvcell.vcml.vcml_remote import export_n5
+
+        return export_n5(
+            self._saved_simulation,
+            biomodel=self._saved_biomodel,
+            variable_names=variable_names if variable_names is not None else self._variable_names,
+            dataset_name=dataset_name if dataset_name is not None else self._dataset_name,
+            poll_interval=poll_interval or self._poll_interval,
+            timeout=timeout if timeout is not None else self._timeout,
+            on_progress=self._on_progress,
+            api_client=self._api_client,
+        )
+
+    def result(
+        self,
+        variable_names: list[str] | None = None,
+        dataset_name: str | None = None,
+        poll_interval: float | None = None,
+        timeout: float | None = None,
+    ) -> TensorStore:
+        """Wait for completion, then export and return results.
+
+        Convenience method combining :meth:`wait` and :meth:`export`.
+
+        Returns:
+            A TensorStore pointing to the exported N5 data.
+        """
+        self.wait(poll_interval=poll_interval, timeout=timeout)
+        return self.export(
+            variable_names=variable_names, dataset_name=dataset_name, poll_interval=poll_interval, timeout=timeout
+        )
+
+
+class VCellSession:
+    """Session for remote VCell operations.
+
+    Created by :func:`~pyvcell.vcml.login` (authenticated) or
+    :func:`~pyvcell.vcml.connect` (anonymous, public data only).
+
+    Example::
+
+        import pyvcell.vcml as vc
+
+        # Anonymous — browse public models
+        session = vc.connect()
+        biomodel = session.load_biomodel("279851639")
+
+        # Authenticated — full access
+        session = vc.connect(login=True)
+        store = session.run_sim(biomodel, "sim1")
+    """
+
+    def __init__(self, api_client: ApiClient, authenticated: bool = True) -> None:
+        self._api_client = api_client
+        self._authenticated = authenticated
+
+    def _require_auth(self, operation: str) -> None:
+        """Raise if this session is not authenticated."""
+        if not self._authenticated:
+            raise RuntimeError(
+                f"{operation} requires authentication. Use vc.connect(login=True) instead of vc.connect()."
+            )
+
+    def run_sim(
+        self,
+        biomodel: Biomodel,
+        simulation: Simulation | str,
+        model_name: str | None = None,
+        variable_names: list[str] | None = None,
+        dataset_name: str | None = None,
+        poll_interval: float = 5.0,
+        timeout: float | None = None,
+        on_progress: Callable[[str], None] | None = None,
+    ) -> TensorStore:
+        """Save, run, and export a simulation in one blocking call.
+
+        Args:
+            biomodel: The biomodel to save and simulate.
+            simulation: Simulation object or name string.
+            model_name: Name for the saved model on the server.
+            variable_names: Variables to export. ``None`` exports all.
+            dataset_name: Name for the N5 dataset.
+            poll_interval: Seconds between status polls.
+            timeout: Maximum seconds to wait for each phase.
+            on_progress: Optional callback receiving status strings.
+
+        Returns:
+            A TensorStore pointing to the exported N5 results.
+        """
+        self._require_auth("run_sim")
+        from pyvcell.vcml.vcml_remote import run_remote
+
+        return run_remote(
+            biomodel,
+            simulation,
+            model_name=model_name,
+            variable_names=variable_names,
+            dataset_name=dataset_name,
+            poll_interval=poll_interval,
+            timeout=timeout,
+            on_progress=on_progress,
+            api_client=self._api_client,
+        )
+
+    def start_sim(
+        self,
+        biomodel: Biomodel,
+        simulation: Simulation | str,
+        model_name: str | None = None,
+        variable_names: list[str] | None = None,
+        dataset_name: str | None = None,
+        poll_interval: float = 5.0,
+        timeout: float | None = None,
+        on_progress: Callable[[str], None] | None = None,
+    ) -> SimulationJob:
+        """Save a biomodel and start a simulation without blocking.
+
+        Args:
+            biomodel: The biomodel to save and simulate.
+            simulation: Simulation object or name string.
+            model_name: Name for the saved model on the server.
+            variable_names: Variables to export when calling :meth:`SimulationJob.export`.
+            dataset_name: Name for the N5 dataset.
+            poll_interval: Default seconds between status polls.
+            timeout: Default maximum seconds to wait.
+            on_progress: Optional callback receiving status strings.
+
+        Returns:
+            A :class:`SimulationJob` that can be used to monitor, wait, and export results.
+        """
+        self._require_auth("start_sim")
+        from pyvcell.vcml.vcml_remote import save_and_start
+
+        saved_bm, saved_sim = save_and_start(
+            biomodel,
+            simulation,
+            model_name=model_name,
+            on_progress=on_progress,
+            api_client=self._api_client,
+        )
+        return SimulationJob(
+            api_client=self._api_client,
+            saved_biomodel=saved_bm,
+            saved_simulation=saved_sim,
+            variable_names=variable_names,
+            dataset_name=dataset_name,
+            poll_interval=poll_interval,
+            timeout=timeout,
+            on_progress=on_progress,
+        )
+
+    def save_biomodel(self, biomodel: Biomodel, name: str | None = None) -> Biomodel:
+        """Save a biomodel to the VCell server.
+
+        Args:
+            biomodel: The biomodel to save.
+            name: Name for the saved model. Defaults to ``biomodel.name``.
+
+        Returns:
+            The saved biomodel with server-assigned version keys.
+        """
+        self._require_auth("save_biomodel")
+        from pyvcell._internal.api.vcell_client.api.bio_model_resource_api import BioModelResourceApi
+        from pyvcell.vcml.utils import load_vcml_str, to_vcml_str
+
+        vcml_str = to_vcml_str(biomodel)
+        bm_api = BioModelResourceApi(self._api_client)
+        saved_vcml = bm_api.save_bio_model(body=vcml_str, new_name=name or biomodel.name)
+        return load_vcml_str(saved_vcml)
+
+    def list_biomodels(self) -> list[dict[str, str | None]]:
+        """Return a list of accessible BioModels from the VCell server.
+
+        Requires an authenticated session (temporary — the VCell API
+        should support anonymous listing of public models but currently
+        returns an error for unauthenticated requests).
+
+        Returns:
+            A list of dictionaries with ``"id"``, ``"name"``, and ``"owner"`` keys.
+        """
+        # TODO: remove auth guard once VCell API supports anonymous listing
+        self._require_auth("list_biomodels")
+        from pyvcell.vcml.utils import list_biomodels
+
+        return list_biomodels(api_client=self._api_client)
+
+    def load_biomodel(
+        self,
+        biomodel_id: str | None = None,
+        *,
+        name: str | None = None,
+        owner: str | None = None,
+    ) -> Biomodel:
+        """Load a VCell BioModel by ID or by name/owner lookup.
+
+        Args:
+            biomodel_id: The BioModel database key.
+            name: BioModel name to search for (case-insensitive substring).
+            owner: Owner username to narrow the search.
+
+        Returns:
+            A parsed Biomodel instance.
+        """
+        from pyvcell.vcml.utils import load_biomodel
+
+        return load_biomodel(biomodel_id, name=name, owner=owner, api_client=self._api_client)
diff --git a/pyvcell/vcml/utils.py b/pyvcell/vcml/utils.py
index 958bca2..b1bee08 100644
--- a/pyvcell/vcml/utils.py
+++ b/pyvcell/vcml/utils.py
@@ -219,7 +219,9 @@ def list_biomodels(api_client: ApiClient | None = None) -> list[dict[str, str |
     from pyvcell._internal.api.vcell_client.configuration import Configuration
 
     if api_client is None:
-        api_client = ApiClient(configuration=Configuration())
+        from pyvcell.vcml.vcml_remote import _cached_api_client
+
+        api_client = _cached_api_client if _cached_api_client is not None else ApiClient(configuration=Configuration())
 
     host = api_client.configuration.host
     headers: dict[str, str] = {"Accept": "application/json"}
@@ -290,7 +292,9 @@ def load_biomodel(
         raise ValueError("Provide either biomodel_id or name, not both")
 
     if api_client is None:
-        api_client = ApiClient(configuration=Configuration())
+        from pyvcell.vcml.vcml_remote import _cached_api_client
+
+        api_client = _cached_api_client if _cached_api_client is not None else ApiClient(configuration=Configuration())
 
     bm_api = BioModelResourceApi(api_client)
 
diff --git a/pyvcell/vcml/vcml_remote.py b/pyvcell/vcml/vcml_remote.py
index 24f71a2..611778f 100644
--- a/pyvcell/vcml/vcml_remote.py
+++ b/pyvcell/vcml/vcml_remote.py
@@ -2,8 +2,12 @@
 
 import time
 from collections.abc import Callable
+from typing import TYPE_CHECKING
 from urllib.parse import parse_qs, urlparse
 
+if TYPE_CHECKING:
+    from pyvcell.vcml.session import VCellSession
+
 from tensorstore._tensorstore import TensorStore  # type: ignore[import-not-found]
 
 from pyvcell._internal.api.vcell_client.api.bio_model_resource_api import BioModelResourceApi
@@ -24,6 +28,74 @@
 
 _TERMINAL_STATUSES = {Status.COMPLETED, Status.FAILED, Status.STOPPED}
 
+_cached_api_client: ApiClient | None = None
+
+
+def _resolve_api_client(api_client: ApiClient | None) -> ApiClient:
+    """Return the given client, or fall back to the cached client from login()."""
+    if api_client is not None:
+        return api_client
+    if _cached_api_client is not None:
+        return _cached_api_client
+    raise RuntimeError("No API client provided and not logged in. Call vc.login() first or pass api_client explicitly.")
+
+
+def connect(
+    api_base_url: str = "https://vcell.cam.uchc.edu",
+    login: bool = False,
+    client_id: str = "cjoWhd7W8A8znf7Z7vizyvKJCiqTgRtf",
+    issuer_url: str = "https://dev-dzhx7i2db3x3kkvq.us.auth0.com",
+    insecure: bool = False,
+) -> VCellSession:
+    """Connect to the VCell server and return a session.
+
+    By default, creates an anonymous session that can browse and load
+    public BioModels. Pass ``login=True`` to authenticate via OAuth2
+    (opens a browser window), which enables running simulations and
+    saving models.
+
+    Args:
+        api_base_url: VCell server URL.
+        login: If ``True``, open a browser for interactive OAuth2 login.
+        client_id: OAuth2 client ID (only used when ``login=True``).
+        issuer_url: OAuth2 issuer URL (only used when ``login=True``).
+        insecure: Disable SSL verification (only used when ``login=True``).
+
+    Returns:
+        A :class:`VCellSession` — anonymous or authenticated depending on *login*.
+    """
+    from pyvcell.vcml.session import VCellSession as _VCellSession
+
+    if login:
+        global _cached_api_client
+        from pyvcell._internal.api.vcell_client.auth.auth_utils import login_interactive
+
+        client = login_interactive(
+            api_base_url=api_base_url,
+            client_id=client_id,
+            issuer_url=issuer_url,
+            insecure=insecure,
+        )
+        _cached_api_client = client
+        ApiClient.set_default(client)  # type: ignore[no-untyped-call]
+        return _VCellSession(api_client=client, authenticated=True)
+    else:
+        from pyvcell._internal.api.vcell_client.configuration import Configuration
+
+        client = ApiClient(configuration=Configuration(host=api_base_url))
+        return _VCellSession(api_client=client, authenticated=False)
+
+
+def logout() -> None:
+    """Clear the cached API client.
+
+    After calling this, authenticated session features will be unavailable
+    until :func:`connect` is called again with ``login=True``.
+    """
+    global _cached_api_client
+    _cached_api_client = None
+    ApiClient.set_default(None)  # type: ignore[no-untyped-call]
+
 
 def _find_simulation(biomodel: Biomodel, sim_name: str) -> tuple[Application, Simulation]:
     """Find a simulation by name across all applications."""
@@ -43,6 +115,40 @@ def _find_app_for_simulation(biomodel: Biomodel, simulation: Simulation) -> Appl
     raise ValueError(f"Simulation '{simulation.name}' not found in biomodel '{biomodel.name}'")
 
 
+def _submit_export(export_api: ExportResourceApi, request: N5ExportRequest) -> int:
+    """Submit an N5 export request and return the job ID."""
+    job_id: int = export_api.export_n5(n5_export_request=request)
+    return job_id
+
+
+def _await_export(
+    export_api: ExportResourceApi,
+    job_id: int,
+    poll_interval: float = 5.0,
+    timeout: float | None = None,
+    on_progress: Callable[[str], None] | None = None,
+) -> TensorStore:
+    """Poll for export completion and return the opened TensorStore."""
+    start_time = time.monotonic()
+    while True:
+        events = export_api.export_status()
+        for event in events:
+            if event.job_id == job_id:
+                if event.event_type == "EXPORT_COMPLETE":
+                    if event.location is None:
+                        raise RuntimeError("Export completed but no location was returned")
+                    if on_progress:
+                        on_progress(f"Export complete: {event.location}")
+                    return _open_n5_from_export_url(event.location)
+                elif event.event_type == "EXPORT_FAILURE":
+                    raise RuntimeError(f"Export failed: {event}")
+
+        if timeout is not None and (time.monotonic() - start_time) >= timeout:
+            raise RuntimeError(f"Export timed out after {timeout}s")
+
+        time.sleep(poll_interval)
+
+
 def _open_n5_from_export_url(url: str) -> TensorStore:
     """Parse an N5 export URL and open via TensorStore."""
     parsed = urlparse(url)
@@ -54,24 +160,25 @@ def _open_n5_from_export_url(url: str) -> TensorStore:
 
 
 def save_and_start(
-    api_client: ApiClient,
     biomodel: Biomodel,
     simulation: Simulation | str,
     model_name: str | None = None,
     on_progress: Callable[[str], None] | None = None,
+    api_client: ApiClient | None = None,
 ) -> tuple[Biomodel, Simulation]:
     """Save a biomodel to the VCell server and start a simulation.
 
     Args:
-        api_client: Authenticated API client from ``login_interactive()``.
         biomodel: The biomodel to save.
         simulation: Simulation object or name string identifying the simulation to start.
         model_name: Name for the saved model on the server. Defaults to ``biomodel.name``.
         on_progress: Optional callback for status messages.
+        api_client: Authenticated API client. If ``None``, uses the client cached by :func:`login`.
 
     Returns:
         A tuple of (saved_biomodel, saved_simulation) with server-assigned version keys.
     """
+    api_client = _resolve_api_client(api_client)
     sim_name = simulation if isinstance(simulation, str) else simulation.name
 
     # Serialize and save
@@ -99,27 +206,28 @@ def save_and_start(
 
 
 def wait_for_simulation(
-    api_client: ApiClient,
     biomodel: Biomodel,
     simulation: Simulation,
     poll_interval: float = 5.0,
     timeout: float | None = None,
     on_progress: Callable[[str], None] | None = None,
+    api_client: ApiClient | None = None,
 ) -> None:
     """Poll simulation status until it reaches a terminal state.
 
     Args:
-        api_client: Authenticated API client.
         biomodel: Saved biomodel (must have ``version.key``).
         simulation: Saved simulation (must have ``version.key``).
         poll_interval: Seconds between status polls.
         timeout: Maximum seconds to wait. ``None`` means wait indefinitely.
         on_progress: Optional callback receiving status strings.
+        api_client: Authenticated API client. If ``None``, uses the client cached by :func:`login`.
 
     Raises:
         RuntimeError: If the simulation fails, is stopped, or times out.
         ValueError: If the biomodel or simulation is missing version keys.
     """
+    api_client = _resolve_api_client(api_client)
     if biomodel.version is None or biomodel.version.key is None:
         raise ValueError("biomodel must have a version key (save it to the server first)")
     if simulation.version is None or simulation.version.key is None:
@@ -149,7 +257,6 @@ def wait_for_simulation(
 
 
 def export_n5(
-    api_client: ApiClient,
     simulation: Simulation,
     biomodel: Biomodel | None = None,
     variable_names: list[str] | None = None,
@@ -157,11 +264,11 @@ def export_n5(
     poll_interval: float = 5.0,
     timeout: float | None = None,
     on_progress: Callable[[str], None] | None = None,
+    api_client: ApiClient | None = None,
 ) -> TensorStore:
     """Export simulation results as N5 and open as a TensorStore.
 
     Args:
-        api_client: Authenticated API client.
         simulation: Saved simulation (must have ``version.key``).
         biomodel: Saved biomodel. Required when ``variable_names`` is ``None``
             so that species names can be derived from the application.
@@ -171,6 +278,7 @@ def export_n5(
         poll_interval: Seconds between export status polls.
         timeout: Maximum seconds to wait for the export. ``None`` means wait indefinitely.
         on_progress: Optional callback receiving status strings.
+        api_client: Authenticated API client. If ``None``, uses the client cached by :func:`login`.
 
     Returns:
         A TensorStore pointing to the exported N5 data.
@@ -180,6 +288,7 @@ def export_n5(
             ``variable_names`` is ``None`` and ``biomodel`` is not provided.
         RuntimeError: If the export fails or times out.
     """
+    api_client = _resolve_api_client(api_client)
     if simulation.version is None or simulation.version.key is None:
         raise ValueError("simulation must have a version key (save and run it first)")
 
@@ -219,34 +328,15 @@ def export_n5(
         on_progress("Starting N5 export...")
 
     export_api = ExportResourceApi(api_client)
-    job_id = export_api.export_n5(n5_export_request=request)
+    job_id = _submit_export(export_api, request)
 
     if on_progress:
         on_progress(f"Export job started: {job_id}")
 
-    # Poll for completion
-    start_time = time.monotonic()
-    while True:
-        events = export_api.export_status()
-        for event in events:
-            if event.job_id == job_id:
-                if event.event_type == "EXPORT_COMPLETE":
-                    if event.location is None:
-                        raise RuntimeError("Export completed but no location was returned")
-                    if on_progress:
-                        on_progress(f"Export complete: {event.location}")
-                    return _open_n5_from_export_url(event.location)
-                elif event.event_type == "EXPORT_FAILURE":
-                    raise RuntimeError(f"Export failed: {event}")
-
-        if timeout is not None and (time.monotonic() - start_time) >= timeout:
-            raise RuntimeError(f"Export timed out after {timeout}s")
-
-        time.sleep(poll_interval)
+    return _await_export(export_api, job_id, poll_interval=poll_interval, timeout=timeout, on_progress=on_progress)
 
 
 def run_remote(
-    api_client: ApiClient,
     biomodel: Biomodel,
     simulation: Simulation | str,
     model_name: str | None = None,
@@ -255,6 +345,7 @@ def run_remote(
     poll_interval: float = 5.0,
     timeout: float | None = None,
     on_progress: Callable[[str], None] | None = None,
+    api_client: ApiClient | None = None,
 ) -> TensorStore:
     """Save, run, and export a simulation in one call.
 
@@ -262,7 +353,6 @@ def run_remote(
     :func:`export_n5` into a single convenience function.
 
     Args:
-        api_client: Authenticated API client from ``login_interactive()``.
         biomodel: The biomodel to save and simulate.
         simulation: Simulation object or name string.
         model_name: Name for the saved model on the server.
@@ -271,18 +361,24 @@ def run_remote(
         poll_interval: Seconds between status polls.
         timeout: Maximum seconds to wait for each phase (simulation and export).
         on_progress: Optional callback receiving status strings.
+        api_client: Authenticated API client. If ``None``, uses the client cached by :func:`login`.
 
     Returns:
         A TensorStore pointing to the exported N5 results.
     """
+    api_client = _resolve_api_client(api_client)
     saved_bm, saved_sim = save_and_start(
-        api_client, biomodel, simulation, model_name=model_name, on_progress=on_progress
+        biomodel, simulation, model_name=model_name, on_progress=on_progress, api_client=api_client
     )
     wait_for_simulation(
-        api_client, saved_bm, saved_sim, poll_interval=poll_interval, timeout=timeout, on_progress=on_progress
+        saved_bm,
+        saved_sim,
+        poll_interval=poll_interval,
+        timeout=timeout,
+        on_progress=on_progress,
+        api_client=api_client,
     )
     return export_n5(
-        api_client,
         saved_sim,
         biomodel=saved_bm,
         variable_names=variable_names,
@@ -290,4 +386,5 @@ def run_remote(
         poll_interval=poll_interval,
         timeout=timeout,
         on_progress=on_progress,
+        api_client=api_client,
     )
diff --git a/tests/_internal/simdata/vtk_test.py b/tests/_internal/simdata/vtk_test.py
index 6f1ff6d..7b1b97c 100644
--- a/tests/_internal/simdata/vtk_test.py
+++ b/tests/_internal/simdata/vtk_test.py
@@ -24,7 +24,8 @@
 
 @pytest.mark.skipif(IN_GITHUB_ACTIONS, reason="Test doesn't work in Github Actions.")
 def test_vtk(solver_output_path: Path, solver_output_simid_jobid: tuple[int, int], zarr_path: Path) -> None:
-    with tempfile.TemporaryDirectory() as tmpdirname, Path(tmpdirname) as tmp_dir:
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        tmp_dir = Path(tmpdirname)
         sim_id, job_id = solver_output_simid_jobid
         result = Result(solver_output_dir=solver_output_path, sim_id=sim_id, job_id=job_id, zarr_dir=tmp_dir)
         mesh: CartesianMesh = result.mesh
diff --git a/tests/conftest.py b/tests/conftest.py
index e9df8e8..8fb0822 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -27,6 +27,15 @@
 )
 
 
+def pytest_addoption(parser: pytest.Parser) -> None:
+    parser.addoption(
+        "--run-remote", action="store_true", default=False, help="Run authenticated remote integration tests"
+    )
+    parser.addoption(
+        "--run-interactive", action="store_true", default=False, help="Run notebooks requiring trame/display server"
+    )
+
+
 @pytest.fixture
 def test_root_dir() -> Path:
     return Path(__file__).parent
diff --git a/tests/examples/__init__.py b/tests/examples/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/examples/test_example_notebooks.py b/tests/examples/test_example_notebooks.py
new file mode 100644
index 0000000..7f0dd7c
--- /dev/null
+++ b/tests/examples/test_example_notebooks.py
@@ -0,0 +1,154 @@
+"""Lint and optionally execute example notebooks.
+
+Static checks (ruff) run on all notebooks by default.
+Execution tests are split into tiers:
+
+- Default: notebooks that run headless without auth or display server
+- --run-interactive: notebooks requiring trame/display server
+- --run-remote: notebooks requiring VCell server auth
+
+Usage:
+    poetry run pytest tests/examples/test_example_notebooks.py -v                    # lint all, execute safe ones
+    poetry run pytest tests/examples/test_example_notebooks.py -v --run-interactive  # also run trame notebooks
+    poetry run pytest tests/examples/test_example_notebooks.py -v --run-remote       # also run auth notebooks
+"""
+
+from __future__ import annotations
+
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+
+NOTEBOOKS_DIR = Path(__file__).resolve().parents[2] / "examples" / "notebooks"
+
+# Notebooks that need interactive trame/display server
+INTERACTIVE_NOTEBOOKS = {"fielddata_trame", "widget", "image_field_data_demo"}
+
+# Notebooks that need VCell server auth
+REMOTE_NOTEBOOKS = {"_internal_vcell_publications", "_internal_n5_download_demo"}
+
+# Notebooks that can't be executed in CI
+NO_EXECUTE_NOTEBOOKS: set[str] = set()
+
+notebook_paths = sorted(NOTEBOOKS_DIR.glob("*.ipynb"))
+
+
+def pytest_configure(config: pytest.Config) -> None:
+    config.addinivalue_line("markers", "interactive: requires trame/display server")
+    config.addinivalue_line("markers", "remote: requires VCell server auth")
+
+
+# ---------------------------------------------------------------------------
+# Static lint checks (all notebooks)
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "notebook",
+    notebook_paths,
+    ids=[p.stem for p in notebook_paths],
+)
+def test_notebook_lint(notebook: Path, tmp_path: Path) -> None:
+    """Export notebook to .py and run mypy type checks."""
+    # Export to Python script
+    result = subprocess.run(  # noqa: S603
+        [
+            sys.executable,
+            "-m",
+            "jupyter",
+            "nbconvert",
+            "--to",
+            "script",
+            "--output-dir",
+            str(tmp_path),
+            str(notebook),
+        ],
+        capture_output=True,
+        text=True,
+        timeout=30,
+    )
+    assert result.returncode == 0, f"nbconvert failed for {notebook.name}:\n{result.stderr}"
+
+    # Find the exported .py file
+    py_files = list(tmp_path.glob("*.py"))
+    assert len(py_files) == 1, f"Expected 1 .py file, got {len(py_files)}"
+    py_file = py_files[0]
+
+    # Run mypy (same approach as scripts/check_notebooks.sh)
+    result = subprocess.run(  # noqa: S603
+        [
+            sys.executable,
+            "-m",
+            "mypy",
+            "--ignore-missing-imports",
+            str(py_file),
+        ],
+        capture_output=True,
+        text=True,
+        timeout=60,
+    )
+    # Filter out errors inherent to notebook-as-script conversion:
+    # - top-level-await: valid in Jupyter kernels
+    # - name-defined: Jupyter builtins like display, In
+    # - union-attr: biomodel.model is Model | None, notebooks don't guard
+    # - no-redef: notebooks reuse variable names across cells
+    # - no-untyped-call: Jupyter builtins like display are untyped
+    # - attr-defined on generated API code: not explicitly exported
+    notebook_noise = {"top-level-await", "name-defined", "union-attr", "no-redef", "no-untyped-call"}
+    errors = [
+        line
+        for line in result.stdout.strip().splitlines()
+        if "error:" in line and not any(f"[{code}]" in line for code in notebook_noise)
+    ]
+    assert not errors, f"Type errors in {notebook.name}:\n" + "\n".join(errors)
+
+
+# ---------------------------------------------------------------------------
+# Execution checks (tiered)
+# ---------------------------------------------------------------------------
+
+
+def _should_execute(notebook_stem: str, request: pytest.FixtureRequest) -> None:
+    """Skip notebooks based on their tier and available flags."""
+    if notebook_stem in NO_EXECUTE_NOTEBOOKS:
+        pytest.skip(f"{notebook_stem} requires local data files not available in CI")
+    if notebook_stem in INTERACTIVE_NOTEBOOKS and not request.config.getoption("--run-interactive", default=False):
+        pytest.skip(f"{notebook_stem} requires --run-interactive flag (needs trame/display)")
+    if notebook_stem in REMOTE_NOTEBOOKS and not request.config.getoption("--run-remote", default=False):
+        pytest.skip(f"{notebook_stem} requires --run-remote flag (needs VCell auth)")
+
+
+@pytest.mark.parametrize(
+    "notebook",
+    notebook_paths,
+    ids=[p.stem for p in notebook_paths],
+)
+def test_notebook_executes(notebook: Path, tmp_path: Path, request: pytest.FixtureRequest) -> None:
+    """Run a notebook with nbconvert --execute and assert zero exit code."""
+    _should_execute(notebook.stem, request)
+
+    output = tmp_path / notebook.name
+    result = subprocess.run(  # noqa: S603
+        [
+            sys.executable,
+            "-m",
+            "jupyter",
+            "nbconvert",
+            "--to",
+            "notebook",
+            "--execute",
+            "--output",
+            str(output),
+            str(notebook),
+        ],
+        capture_output=True,
+        text=True,
+        timeout=300,
+        env={**os.environ, "MPLBACKEND": "Agg"},
+    )
+    assert (
+        result.returncode == 0
+    ), f"Notebook {notebook.name} failed:\nSTDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}"
diff --git a/tests/guides/test_notebooks.py b/tests/guides/test_notebooks.py
index 3db74db..0230aad 100644
--- a/tests/guides/test_notebooks.py
+++ b/tests/guides/test_notebooks.py
@@ -10,7 +10,7 @@
 
 NOTEBOOKS_DIR = Path(__file__).resolve().parents[2] / "docs" / "guides" / "notebooks"
 
-SKIP_NOTEBOOKS = {"remote-simulations"}
+REMOTE_NOTEBOOKS = {"remote-simulations"}
 
 notebook_paths = sorted(NOTEBOOKS_DIR.glob("*.ipynb"))
 
@@ -20,11 +20,13 @@
     notebook_paths,
     ids=[p.stem for p in notebook_paths],
 )
-def test_notebook_executes(notebook: Path, tmp_path: Path) -> None:
+def test_notebook_executes(notebook: Path, tmp_path: Path, request: pytest.FixtureRequest) -> None:
     """Run a notebook with nbconvert --execute and assert zero exit code."""
-    if notebook.stem in SKIP_NOTEBOOKS:
-        pytest.skip(f"{notebook.name} requires interactive auth and a live server")
+    if notebook.stem in REMOTE_NOTEBOOKS and not request.config.getoption("--run-remote", default=False):
+        pytest.skip(f"{notebook.name} requires --run-remote flag (needs VCell auth)")
     output = tmp_path / notebook.name
+    # Remote notebooks run full simulations on the VCell cluster — need longer timeout
+    timeout = 600 if notebook.stem in REMOTE_NOTEBOOKS else 300
     result = subprocess.run(  # noqa: S603
         [
             sys.executable,
@@ -40,7 +42,7 @@ def test_notebook_executes(notebook: Path, tmp_path: Path) -> None:
         ],
         capture_output=True,
         text=True,
-        timeout=300,
+        timeout=timeout,
         env={**__import__("os").environ, "MPLBACKEND": "Agg"},
     )
     assert (
diff --git a/tests/sim_results/test_result.py b/tests/sim_results/test_result.py
index 712a9fb..5084f24 100644
--- a/tests/sim_results/test_result.py
+++ b/tests/sim_results/test_result.py
@@ -17,7 +17,8 @@
 
 @pytest.mark.skipif(IN_GITHUB_ACTIONS, reason="Test doesn't work in Github Actions.")
 def test_plot_slice_2D(solver_output_path: Path, solver_output_simid_jobid: tuple[int, int], zarr_path: Path) -> None:
-    with tempfile.TemporaryDirectory() as dirname, Path(dirname) as tmp_dir:
+    with tempfile.TemporaryDirectory() as dirname:
+        tmp_dir = Path(dirname)
         sim_id, job_id = solver_output_simid_jobid
         result = Result(solver_output_dir=solver_output_path, sim_id=sim_id, job_id=job_id, zarr_dir=tmp_dir)
 
diff --git a/tests/vcml/test_load_biomodel.py b/tests/vcml/test_load_biomodel.py
index 9f5bcb1..c74fbb1 100644
--- a/tests/vcml/test_load_biomodel.py
+++ b/tests/vcml/test_load_biomodel.py
@@ -1,20 +1,19 @@
 import pyvcell.vcml as vc
-from pyvcell._internal.api.vcell_client import ApiClient, Configuration
 
 
 def test_load_biomodel_public() -> None:
-    """Load a public BioModel by ID using the default anonymous client."""
-    # Dolgitzer 2025 — a published, public model
-    bm = vc.load_biomodel("279851639")
+    """Load a public BioModel by ID using an anonymous session."""
+    session = vc.connect()
+    bm = session.load_biomodel("279851639")
     assert bm.name == "Dolgitzer 2025 A Continuum Model of Mechanosensation Based on Contractility Kit Assembly"
     assert len(bm.applications) > 0
     assert bm.version is not None
     assert bm.version.key == "279851639"
 
 
-def test_load_biomodel_with_explicit_client() -> None:
-    """Load a public BioModel using an explicitly provided ApiClient."""
-    client = ApiClient(configuration=Configuration(host="https://vcell.cam.uchc.edu"))
-    bm = vc.load_biomodel("279851639", api_client=client)
+def test_load_biomodel_with_explicit_url() -> None:
+    """Load a public BioModel using a session with an explicit server URL."""
+    session = vc.connect(api_base_url="https://vcell.cam.uchc.edu")
+    bm = session.load_biomodel("279851639")
     assert bm.name == "Dolgitzer 2025 A Continuum Model of Mechanosensation Based on Contractility Kit Assembly"
     assert len(bm.applications) > 0
diff --git a/tests/vcml/test_remote_integration.py b/tests/vcml/test_remote_integration.py
new file mode 100644
index 0000000..e6c86bf
--- /dev/null
+++ b/tests/vcml/test_remote_integration.py
@@ -0,0 +1,165 @@
+"""Integration tests for remote VCell operations via VCellSession.
+
+Anonymous tests (vc.connect()) run by default.
+Authenticated tests (vc.connect(login=True)) require interactive login
+and are skipped unless --run-remote is passed to pytest.
+
+Usage:
+    poetry run pytest tests/vcml/test_remote_integration.py -v              # anonymous only
+    poetry run pytest tests/vcml/test_remote_integration.py -v --run-remote # all tests
+"""
+
+from __future__ import annotations
+
+import pytest
+
+import pyvcell.vcml as vc
+from pyvcell.vcml.models import Biomodel
+from pyvcell.vcml.session import VCellSession
+
+
+def _build_test_biomodel() -> Biomodel:
+    """Build a minimal 3D biomodel for remote simulation tests.
+
+    Uses 3D geometry because the VCell server N5 export does not
+    support 1D mesh to image conversions.
+    """
+    antimony_str = """
+        compartment cell = 1;
+        species A in cell;
+        species B in cell;
+        J0: A -> B; cell * (k1*A - k2*B)
+        J0 in cell;
+        k1 = 5.0; k2 = 2.0
+        A = 10
+    """
+    biomodel = vc.load_antimony_str(antimony_str)
+    geo = vc.Geometry(name="geo", origin=(0, 0, 0), extent=(10, 10, 10), dim=3)
+    geo.add_background(name="cell_domain")
+    app = biomodel.add_application("app1", geometry=geo)
+    app.map_compartment("cell", "cell_domain")
+    app.map_species("A", init_conc="10", diff_coef=1.0)
+    app.map_species("B", init_conc="0", diff_coef=1.0)
+    app.add_sim(name="sim1", duration=0.5, output_time_step=0.1, mesh_size=(10, 10, 10))
+    return biomodel
+
+
+# ---------------------------------------------------------------------------
+# Fixtures
+# ---------------------------------------------------------------------------
+
+
+@pytest.fixture(scope="module")
+def anonymous_session() -> VCellSession:
+    """Anonymous session for public data access."""
+    return vc.connect()
+
+
+@pytest.fixture(scope="module")
+def authenticated_session(request: pytest.FixtureRequest) -> VCellSession:
+    """Authenticated session — skipped unless --run-remote is passed."""
+    if not request.config.getoption("--run-remote", default=False):
+        pytest.skip("Authenticated remote tests require --run-remote flag")
+    return vc.connect(login=True)
+
+
+# ---------------------------------------------------------------------------
+# Anonymous tests
+# ---------------------------------------------------------------------------
+
+
+class TestAnonymousSession:
+    """Tests that use an anonymous (unauthenticated) session."""
+
+    def test_connect_returns_session(self, anonymous_session: VCellSession) -> None:
+        assert isinstance(anonymous_session, VCellSession)
+
+    def test_load_biomodel_by_id(self, anonymous_session: VCellSession) -> None:
+        bm = anonymous_session.load_biomodel("279851639")
+        assert bm.name == "Dolgitzer 2025 A Continuum Model of Mechanosensation Based on Contractility Kit Assembly"
+        assert len(bm.applications) > 0
+        assert bm.version is not None
+        assert bm.version.key == "279851639"
+
+    def test_list_biomodels_requires_auth(self, anonymous_session: VCellSession) -> None:
+        """Listing biomodels currently requires auth due to VCell API bug.
+
+        TODO: Once VCell API supports anonymous listing, change this test to
+        verify that list_biomodels() works on an anonymous session.
+        """
+        with pytest.raises(RuntimeError, match="requires authentication"):
+            anonymous_session.list_biomodels()
+
+    def test_run_sim_requires_auth(self, anonymous_session: VCellSession) -> None:
+        bm = anonymous_session.load_biomodel("279851639")
+        with pytest.raises(RuntimeError, match="requires authentication"):
+            anonymous_session.run_sim(bm, bm.applications[0].simulations[0].name)
+
+    def test_save_biomodel_requires_auth(self, anonymous_session: VCellSession) -> None:
+        bm = anonymous_session.load_biomodel("279851639")
+        with pytest.raises(RuntimeError, match="requires authentication"):
+            anonymous_session.save_biomodel(bm)
+
+    def test_start_sim_requires_auth(self, anonymous_session: VCellSession) -> None:
+        bm = anonymous_session.load_biomodel("279851639")
+        with pytest.raises(RuntimeError, match="requires authentication"):
+            anonymous_session.start_sim(bm, bm.applications[0].simulations[0].name)
+
+
+# ---------------------------------------------------------------------------
+# Authenticated tests (require --run-remote)
+# ---------------------------------------------------------------------------
+
+
+class TestAuthenticatedSession:
+    """Tests that require interactive login and a live VCell server."""
+
+    def test_list_biomodels_authenticated(self, authenticated_session: VCellSession) -> None:
+        models = authenticated_session.list_biomodels()
+        assert len(models) > 0
+
+    def test_load_biomodel_by_name(self, authenticated_session: VCellSession) -> None:
+        bm = authenticated_session.load_biomodel(name="Dolgitzer 2025 A Continuum Model")
+        assert "Dolgitzer 2025" in bm.name
+
+    def test_save_biomodel(self, authenticated_session: VCellSession) -> None:
+        from datetime import datetime
+
+        bm = authenticated_session.load_biomodel("279851639")
+        name = f"test_save_pyvcell_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
+        saved = authenticated_session.save_biomodel(bm, name=name)
+        assert saved.version is not None
+        assert saved.version.key is not None
+
+    def test_run_sim_blocking(self, authenticated_session: VCellSession) -> None:
+        """Full end-to-end: build model, run remotely, get results."""
+        biomodel = _build_test_biomodel()
+
+        from datetime import datetime
+
+        name = f"test_run_sim_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
+        store = authenticated_session.run_sim(biomodel, "sim1", model_name=name, on_progress=print)
+        assert store.shape is not None
+        assert len(store.shape) > 0
+
+    def test_start_sim_nonblocking(self, authenticated_session: VCellSession) -> None:
+        """Start a simulation, check status, wait, export."""
+        biomodel = _build_test_biomodel()
+
+        from datetime import datetime
+
+        name = f"test_start_sim_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
+        job = authenticated_session.start_sim(biomodel, "sim1", model_name=name, on_progress=print)
+
+        # Job should have saved biomodel/simulation with version keys
+        assert job.biomodel.version is not None
+        assert job.simulation.version is not None
+
+        # Status should be queryable
+        status = job.status
+        assert status is not None
+
+        # Wait and export
+        store = job.result()
+        assert store.shape is not None
+        assert len(store.shape) > 0