Evidence map›Paper›PMID 28381205›Full record

ArticleBMC bioinformatics2017

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

Garrett W Birkel, Amit Ghosh, Vinay S Kumar, Daniel Weaver, David Ando, Tyler W H Backman, Adam P Arkin, Jay D Keasling, Héctor García Martín

Erratum issuedAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Review
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  3. Biotechnology Data Analysis Training with Jupyter Notebooks.Journal of microbiology & biology education · 2023
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  4. Review
  5. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Garrett W BirkelBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Amit GhoshBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Vinay S KumarBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Daniel WeaverBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
David AndoBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Tyler W H BackmanBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Adam P ArkinBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Jay D KeaslingBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Héctor García MartínBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. hgmartin@lbl.gov.ORCID http://orcid.org/0000-0002-4556-9685

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

User-Computer InterfaceBiofuelsCarbon IsotopesEscherichia coliInternetMetabolic Flux AnalysisMetabolomicsModels, BiologicalPrincipal Component AnalysisProteomicsBiofuelsCarbon Isotopes13 C Metabolic Flux AnalysisFlux analysis-omics dataPredictive biology

Identifiers

PMID28381205
PMCPMC5382524

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.