MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics
Fig 3
A MASSpy workflow for ensemble creation and modeling using MCMC sampling.
A typical ensemble modeling workflow using MASSpy to generate and assemble an ensemble of stable kinetic models for dynamic simulation and analysis. (A) The solution spaces for fluxes and concentrations are sampled using MCMC sampling to generate data for candidate model states. Rate constants are obtained through parameter fitting for elementary rate constants and computation of PERCs. (B) Sampling data is integrated into the candidate models, and models are subsequently filtered based on their stability to assemble the ensemble of stable dynamic models. Once assembled, the ensemble is used to study biological variability in the network of interest through (C) dynamic simulation and (D) analysis.