MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics

• Published in: PLoS computational biology Vol. 17; no. 1; p. e1008208
• Main Authors: Haiman, Zachary B., Zielinski, Daniel C., Koike, Yuko, Yurkovich, James T., Palsson, Bernhard O.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 28.01.2021; Public Library of Science (PLoS)
• Subjects: Algorithms; Biological models (mathematics); Biology and Life Sciences; Computer and Information Sciences; Computer programs; Computer Simulation; Engineering and Technology; Enzyme kinetics; Enzymes; Format; Genomes; High level languages; Interoperability; Kinetics; Metabolic Networks and Pathways; Metabolism; Metabolites; Methods; Models, Biological; Object oriented programming; Object-oriented languages; Ordinary differential equations; Physical Sciences; Programming languages; Regulation; Research and Analysis Methods; Science; Semantics; Simulation; Software; Source code; Systems Biology - methods; Technology application; User requirements; Visualization; United States
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1008208

Mathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulating dynamic models from such reconstructions continues to be a key challenge. Here, we present the Mass Action Stoichiometric Simulation Python (MASSpy) package, an open-source computational framework for dynamic modeling of metabolism. MASSpy utilizes mass action kinetics and detailed chemical mechanisms to build dynamic models of complex biological processes. MASSpy adds dynamic modeling tools to the COnstraint-Based Reconstruction and Analysis Python (COBRApy) package to provide an unified framework for constraint-based and kinetic modeling of metabolic networks. MASSpy supports high-performance dynamic simulation through its implementation of libRoadRunner: the Systems Biology Markup Language (SBML) simulation engine. Three examples are provided to demonstrate how to use MASSpy: (1) a validation of the MASSpy modeling tool through dynamic simulation of detailed mechanisms of enzyme regulation; (2) a feature demonstration using a workflow for generating ensemble of kinetic models using Monte Carlo sampling to approximate missing numerical values of parameters and to quantify biological uncertainty, and (3) a case study in which MASSpy is utilized to overcome issues that arise when integrating experimental data with the computation of functional states of detailed biological mechanisms. MASSpy represents a powerful tool to address challenges that arise in dynamic modeling of metabolic networks, both at small and large scales.