The Mendoza project involves simulating a network of biochemical reactions using the Mendoza equation to study the dynamics of a system of nodes under various activation and inhibition conditions. This repository contains scripts for simulation, SBML model generation, and associated data files necessary for running these simulations.
The Mendoza equation is a mathematical model used to describe the dynamics of nodes in a network. It is implemented in Python and uses ordinary differential equations (ODEs) to compute the behavior of each node over time based on activation and inhibition interactions among them.
The differential equation for the Mendoza model is defined as follows:
def ODESysFun(t, X, NumOfNodes, Mact, Minh, Clamped):
gamma = np.ones(NumOfNodes) # Decay constant of each node
h = 10 # Steepness of activation
f = np.zeros(NumOfNodes)
for i in range(NumOfNodes):
Ract = Mact[i, :]
Rinh = Minh[i, :]
sum_alpha_X = np.dot(Ract, X)
sum_beta_X = np.dot(Rinh, X)
sum_alpha = np.sum(Ract)
sum_beta = np.sum(Rinh)
if np.any(Rinh == 0) and np.any(Ract):
w = ((1 + sum_alpha) / sum_alpha) * (sum_alpha_X / (1 + sum_alpha_X))
elif np.any(Ract == 0) and np.any(Rinh):
w = 1 - ((1 + sum_beta) / sum_beta) * (sum_beta_X / (1 + sum_beta_X))
elif np.any(Ract) and np.any(Rinh):
w = (((1 + sum_alpha) / sum_alpha) * (sum_alpha_X / (1 + sum_alpha_X))) * (1 - ((1 + sum_beta) / sum_beta) * (sum_beta_X / (1 + sum_beta_X)))
else:
w = 0
f[i] = (-np.exp(0.5 * h) + np.exp(-h * (w - 0.5))) / ((1 - np.exp(0.5 * h)) * (1 + np.exp(-h * (w - 0.5)))) - gamma[i] * X[i]
if Clamped[i] == 1:
f[i] = 0
return fThis script contains the implementation of the Mendoza equation for the simulation of the network dynamics.
Generates an SBML (Systems Biology Markup Language) model from the network data. This model can then be used in various bioinformatics tools to further analyze the network dynamics.
Adds annotations to the generated SBML model, enriching it with metadata for better understanding and integration with other data sources.
Contains the data defining nodes, activators, and inhibitors for the network, which are used as input for the simulations.
Contains annotations that are added to the SBML model to provide context and metadata about the model components and their interactions.
This image shows the reaction graph generated by sbmlgenerator2.py, visualizing the interactions among different nodes within the model.
