skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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:
Journal Article: 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 = {Wed Apr 05 00:00:00 EDT 2017},
month = {Wed Apr 05 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 1work
Citation information provided by
Web of Science

Save / Share:
  • Metabolic network modeling of microbial communities provides an in-depth understanding of community-wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high-quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community-level data as a critical input for the networkmore » reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph-heterotroph consortium that was used to provide data needed for a community-level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources.« less
  • The Python ARM Radar Toolkit is a package for reading, visualizing, correcting and analysing data from weather radars. Development began to meet the needs of the Atmospheric Radiation Measurement Climate Research Facility and has since expanded to provide a general-purpose framework for working with data from weather radars in the Python programming language. The toolkit is built on top of libraries in the Scientific Python ecosystem including NumPy, SciPy, and matplotlib, and makes use of Cython for interfacing with existing radar libraries written in C and to speed up computationally demanding algorithms. As a result, the source code for themore » toolkit is available on GitHub and is distributed under a BSD license.« less
  • A model is introduced for microbial kinetics in porous media that includes effects of transients in the metabolic activity of subsurface microorganisms. The model represents the microbial metabolic activity as a functional of the history of aqueous phase substrates; this dependence is represented as a temporally nonlocal convolution integral. Conceptually, this convolution represents the activity of a microbial component as a fraction of its maximum activity, and it is conventionally known as the metabolic potential. The metabolic potential is used to scale the kinetic expressions to account for the metabolic state of the organisms and allows the representation of delayedmore » response in the microbial kinetic equations. Calculation of the convolution requires the definition of a memory (or kernel) function that upon integration over the substrate history represents the microbial metabolic response. A simple piecewise-linear metabolic potential functional is developed here; however, the approach can be generalized to fit the observed behavior of specific systems of interest. The convolution that results form the general from of this model is nonlinear; these nonlinearities are handled by using two separate memory functions and by scaling the domains of the convolution integrals. The model is applied to describe the aerobic degradation of benzene in saturated porous media. Comparative simulations show that metabolic lag can be used to consistently describe observations and that a convolution form can effectively represent microbial lag for this system. Simulations also show that disregarding metabolic lag when it exists can lead to overestimation of the amount of substrate degraded. 45 refs., 4 figs., 1 tab.« less
  • The rate of production of methane in many environmentsdepends upon mutualistic interactions between sulfate-reducing bacteriaand methanogens. To enhance our understanding of these relationships, wetook advantage of the fully sequenced genomes of Desulfovibrio vulgarisand Methanococcus maripaludis to produce and analyze the firstmultispecies stoichiometric metabolic model. Model results were comparedto data on growth of the co-culture on lactate in the absence of sulfate.The model accurately predicted several ecologically relevantcharacteristics, including the flux of metabolites and the ratio of D.vulgaris to M. maripaludis cells during growth. In addition, the modeland our data suggested that it was possible to eliminate formate as aninterspecies electronmore » shuttle, but hydrogen transfer was essential forsyntrophic growth. Our work demonstrated that reconstructed metabolicnetworks and stoichiometric models can serve not only to predictmetabolic fluxes and growth phenotypes of single organisms, but also tocapture growth parameters and community composition of simple bacterialcommunities.« less
  • Microbial electrosynthesis is a renewable energy and chemical production platform that relies on microbial cells to capture electrons from a cathode and fix carbon. Yet despite the promise of this technology, the metabolic capacity of the microbes that inhabit the electrode surface and catalyze electron transfer in these systems remains largely unknown. Here, we assembled thirteen draft genomes from a microbial electrosynthesis system producing primarily acetate from carbon dioxide, and their transcriptional activity was mapped to genomes from cells on the electrode surface and in the supernatant. This allowed us to create a metabolic model of the predominant community membersmore » belonging to Acetobacterium, Sulfurospirillum, and Desulfovibrio. According to the model, the Acetobacterium was the primary carbon fixer, and a keystone member of the community. Transcripts of soluble hydrogenases and ferredoxins from Acetobacterium and hydrogenases, formate dehydrogenase, and cytochromes of Desulfovibrio were found in high abundance near the electrode surface. Cytochrome c oxidases of facultative members of the community were highly expressed in the supernatant despite completely sealed reactors and constant flushing with anaerobic gases. The resulting molecular discoveries and metabolic modeling now serve as a foundation for future examination and development of electrosynthetic microbial communities.« less