ArticleBMC bioinformatics2017
The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Advances in functional analysis of the microbiome: Integrating metabolic modeling, metabolite prediction, and pathway inference with Next-Generation Sequencing data.Journal of microbiology (Seoul, Korea) · 2025Review
- An integrated systems biology approach reveals differences in formate metabolism in the genusiScience · 2023Article
- Biotechnology Data Analysis Training with Jupyter Notebooks.Journal of microbiology & biology education · 2023Article
- Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering.Metabolic engineering communications · 2020Review
- Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics.Metabolites · 2020Article
- Parallel isotope differential modeling for instationary 13C fluxomics at the genome scale.Biotechnology for biofuels · 2020Article
- Review
- The Design of FluxML: A Universal Modeling Language forFrontiers in microbiology · 2019Article
- Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism forMetabolites · 2018Article
- Article
- Erratum to: The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism.BMC bioinformatics · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundModeling 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.
resultsThe 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
conclusionsjQMM 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.
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