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 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.
Highlights
The availability of genome sequences and omic data sets has led to significant advances in metabolic modeling at the genome scale, resulting in the rapid expansion of available genomescale metabolic reconstructions [1]
Mass Action Stoichiometric Simulation Python (MASSpy): Modeling dynamic biological processes in Python freely available for academic use, with solvers and installation instructions found at their respective websites
The data, scripts, and instructions needed to reproduce results of the presented examples are available on GitHub and in the supplement (S3 File)
Summary
The availability of genome sequences and omic data sets has led to significant advances in metabolic modeling at the genome scale, resulting in the rapid expansion of available genomescale metabolic reconstructions [1]. One of the most broadly used metabolic modeling software suites, COnstraint-Based Reconstruction and Analysis (COBRA) [5], provides a scalable framework that is invaluable for the contextualization and analysis of multi-omic data, as well as for understanding, predicting, and engineering metabolism [6,7,8,9,10,11,12,13,14,15,16]. Additional issues arise when integrating incomplete experimental data into metabolic reconstructions, necessitating the need for approximation methods to gap fill missing values that satisfy the thermodynamic constraints imposed by the system [21, 22]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
