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

Abstract

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 amore » 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.« less

Authors:
 [1];  [2];  [3];  [3];  [3];  [1];  [4];  [5]; ORCiD logo [6]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Joint BioEnergy Institute, Emeryville, CA (United States); DOE Agile BioFoundry, Emeryville, CA (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Joint BioEnergy Institute, Emeryville, CA (United States); Indian Institute of Technology (IIT), Kharagpur (India)
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Joint BioEnergy Institute, Emeryville, CA (United States)
  4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
  5. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Joint BioEnergy Institute, Emeryville, CA (United States); Univ. of California, Berkeley, CA (United States); Technical Univ. of Denmark, Horsholm (Denmark)
  6. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Joint BioEnergy Institute, Emeryville, CA (United States); DOE Agile BioFoundry, Emeryville, CA (United States); Basque Center for Applied Mathematics, Bilbao (Spain)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1379799
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
BMC Bioinformatics
Additional Journal Information:
Journal Volume: 18; Journal Issue: 1; Journal ID: ISSN 1471-2105
Publisher:
BioMed Central
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; 60 APPLIED LIFE SCIENCES; Flux analysis; 13C Metabolic Flux Analysis; -omics data; Predictive biology

Citation Formats

Birkel, Garrett W., Ghosh, Amit, Kumar, Vinay S., Weaver, Daniel, Ando, David, Backman, Tyler W. H., Arkin, Adam P., Keasling, Jay D., and Martín, Hector Garcia. The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism. United States: N. p., 2017. Web. doi:10.1186/s12859-017-1615-y.
Birkel, Garrett W., Ghosh, Amit, Kumar, Vinay S., Weaver, Daniel, Ando, David, Backman, Tyler W. H., Arkin, Adam P., Keasling, Jay D., & Martín, Hector Garcia. The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism. United States. doi:10.1186/s12859-017-1615-y.
Birkel, Garrett W., Ghosh, Amit, Kumar, Vinay S., Weaver, Daniel, Ando, David, Backman, Tyler W. H., Arkin, Adam P., Keasling, Jay D., and Martín, Hector Garcia. Wed . "The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism". United States. doi:10.1186/s12859-017-1615-y. https://www.osti.gov/servlets/purl/1379799.
@article{osti_1379799,
title = {The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism},
author = {Birkel, Garrett W. and Ghosh, Amit and Kumar, Vinay S. and Weaver, Daniel and Ando, David and Backman, Tyler W. H. and Arkin, Adam P. and Keasling, Jay D. and Martín, Hector Garcia},
abstractNote = {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.},
doi = {10.1186/s12859-017-1615-y},
journal = {BMC Bioinformatics},
number = 1,
volume = 18,
place = {United States},
year = {2017},
month = {4}
}

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Table 1 Table 1: Table of iPython Notebooks

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      Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.