The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

• Published in: BMC bioinformatics Vol. 18; no. 1
• Main Authors: Birkel, Garrett W., Ghosh, Amit, Kumar, Vinay S., Weaver, Daniel, Ando, David, Backman, Tyler W. H., Arkin, Adam P., Keasling, Jay D., Martín, Héctor García
• Format: Journal Article
• Language: English
• Published: United Kingdom Springer Science + Business Media 05.04.2017
• Subjects: 13C Metabolic Flux Analysis; 60 APPLIED LIFE SCIENCES; BASIC BIOLOGICAL SCIENCES; Flux analysis; MATHEMATICS AND COMPUTING; omics data; Predictive biology
• DOI: 10.1186/s12859-017-1615-y
• ISSN: 1471-2105

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed. The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and 13C Metabolic Flux Analysis. Moreover, it introduces the capability to use 13C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13C Metabolic Flux Analysis (2S- 13C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user's specific needs. jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.