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

Zachary B. Haiman, Daniel C. Zielinski, Yuko Koike, James T. Yurkovich, Bernhard O. Palsson*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

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 constraintbased 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.

Original languageEnglish
Article numbere1008208
JournalPLOS Computational Biology
Volume17
Issue number1
Number of pages20
ISSN1553-734X
DOIs
Publication statusPublished - 28 Jan 2021

Bibliographical note

Funding Information:
Funding for this work and support for ZBH, DZ, and BOP was provided by the Novo Nordisk Foundation through the Center for Biosustainability (https://www.biosustain.dtu.dk/) at the Technical University of Denmark [NNF10CC1016517]. YK and JTY were supported by the Institute for Systems Biology's Translational Research Fellows Program (https://isbscience.org/ training/trp/translational-research-fellowsprogram/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2021 Haiman et al.

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