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

Publication: Research - peer-reviewJournal article – Annual report year: 2017

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  • Author: Birkel, Garrett W.

    Lawrence Berkeley National Laboratory, United States

  • Author: Ghosh, Amit

    Lawrence Berkeley National Laboratory, United States

  • Author: Kumar, Vinay S.

    Lawrence Berkeley National Laboratory, United States

  • Author: Weaver, Daniel

    Lawrence Berkeley National Laboratory, United States

  • Author: Ando, David

    Lawrence Berkeley National Laboratory, United States

  • Author: Backman, Tyler W. H.

    Lawrence Berkeley National Laboratory, United States

  • Author: Arkin, Adam Paul

    Lawrence Berkeley National Laboratory, United States

  • Author: Keasling, Jay D.

    Synthetic Biology Tools for Yeast, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Denmark

  • Author: Martin, Hector Garcia

    Lawrence Berkeley National Laboratory, United States

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Background: 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.Results: 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 C-13 Metabolic Flux Analysis. Moreover, it introduces the capability to use C-13 labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale C-13 Metabolic Flux Analysis (2S-C-13 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.Conclusions: 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.
Original languageEnglish
Article number205
JournalB M C Bioinformatics
Volume18
Number of pages11
ISSN1471-2105
DOIs
StatePublished - 2017
CitationsWeb of Science® Times Cited: 1

    Keywords

  • Flux analysis, 13 C Metabolic Flux Analysis, -omics data, Predictive biology
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