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Systems biologist building reproducible computational workflows for transcriptomics and high-dimensional data analysis.

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Hi, I’m Simo

Senior computational biologist working at the intersection of systems biology, transcriptomics, and drug discovery.

I design and implement reproducible computational workflows for multi-omics integration, network inference, and mechanism-driven data interpretation. My work focuses on extracting robust biological signal from high-dimensional datasets and translating systems-level insights into drug prioritisation and biomarker strategies.

I am a licensed pharmacist in Finland with clinical and pharmacological training that informs my systems pharmacology and translational modelling work. I also bring a background in evolutionary population ecology, shaping my perspective on biological variation and selection.


Selected Technical Work

scRNAseq-pbmc-workflow

Production-style RNA-seq workflow (Docker + Snakemake + explicit Python CLI wrapper) demonstrating:

  • FASTQ-level QC and reference preparation
  • STARsolo alignment
  • Donor-aware differential analysis (DESeq2 + TOST)
  • Co-expression and network-based downstream analysis
  • Explicit execution control via wrapper interface
  • Fully containerized, reproducible execution

Repository:
https://github.com/inkasimo/scRNAseq-pbmc-workflow


Selected Publications

Inkala, S., Fratello, M., del Giudice, G. et al. MUUMI: an R package for statistical and network-based meta-analysis for multi-omics data integration. BMC Bioinformatics (2026). https://doi.org/10.1186/s12859-026-06394-3

Repository: https://github.com/fhaive/muumi


Focus Areas

  • Systems biology and network-based modelling
  • Multi-omics data integration and transcriptomics
  • Systems pharmacology and mechanism driven drug discovery
  • Reproducible computational workflows (Snakemake, Docker, HPC)
  • Biologically interpretable modelling and validation strategies

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Systems biologist building reproducible computational workflows for transcriptomics and high-dimensional data analysis.

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