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

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 continu...

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Veröffentlicht in:PLoS computational biology Jg. 17; H. 1; S. e1008208
Hauptverfasser: Haiman, Zachary B., Zielinski, Daniel C., Koike, Yuko, Yurkovich, James T., Palsson, Bernhard O.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 28.01.2021
Public Library of Science (PLoS)
Schlagworte:
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
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Zusammenfassung: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.
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The authors have declared that no competing interests exist.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1008208