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Related Experiment Videos

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

Garrett W Birkel1,2,3, Amit Ghosh1,2,4, Vinay S Kumar1,2

  • 1Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.

BMC Bioinformatics
|April 7, 2017
PubMed
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The jQMM Python library enables integrated modeling of microbial metabolic fluxes and -omics data for biotechnology. This tool aids in designing organisms for biofuels and chemicals, advancing metabolic engineering research.

Area of Science:

  • Biotechnology and Systems Biology
  • Computational Biology and Bioinformatics

Background:

  • Mathematical modeling of microbial metabolism is crucial for understanding cellular processes and guiding bioengineering.
  • Quantifying intracellular metabolic fluxes is key for metabolic engineering, revealing carbon and energy flow.
  • There is a growing need for methods to integrate diverse omics data (transcriptomics, proteomics, metabolomics) into metabolic models.

Purpose of the Study:

  • To present jQMM, an open-source Python framework for modeling intracellular metabolic fluxes and integrating omics data.
  • To provide a unified platform for advanced metabolic flux analysis and omics data utilization in cellular metabolism studies.
  • To facilitate the metabolic engineering of organisms for biofuel and chemical production.

Main Methods:

Keywords:
-omics data13 C Metabolic Flux AnalysisFlux analysisPredictive biology

Related Experiment Videos

  • Developed jQMM, a Python library integrating Flux Balance Analysis (FBA) and 13C Metabolic Flux Analysis (13C MFA).
  • Implemented two-scale 13C Metabolic Flux Analysis (2S-13C MFA) to constrain genome-scale models using 13C labeling data.
  • Incorporated methods for using proteomics data to optimize cellular functions, such as enhancing biofuel production.

    • Main Results:

    • jQMM offers a comprehensive toolbox for simultaneous FBA and 13C MFA.
    • The library enables the use of 13C labeling data to constrain genome-scale models via 2S-13C MFA.
    • Demonstrated utility with proteomics data for improving biofuel production and provided reproducible Jupyter notebooks.

    Conclusions:

    • jQMM is a valuable open-source tool for metabolic engineering, organism design, and cellular metabolism research.
    • The library facilitates the integration of omics data for enhanced insights and applications in biotechnology.
    • jQMM aims to foster community contributions to advance the field of metabolic engineering.