IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v7y2016i1d10.1038_ncomms13806.html
   My bibliography  Save this article

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

Author

Listed:
  • Ali Khodayari

    (The Pennsylvania State University, University Park)

  • Costas D. Maranas

    (The Pennsylvania State University, University Park)

Abstract

Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). The Pearson correlation coefficient between experimental data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47 (k-ecoli457 is available for download at http://www.maranasgroup.com ).

Suggested Citation

  • Ali Khodayari & Costas D. Maranas, 2016. "A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains," Nature Communications, Nature, vol. 7(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms13806
    DOI: 10.1038/ncomms13806
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/ncomms13806
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/ncomms13806?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lukas Bromig & Andreas Kremling & Alberto Marin-Sanguino, 2020. "Understanding biochemical design principles with ensembles of canonical non-linear models," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-27, April.
    2. Gaoyang Li & Li Liu & Wei Du & Huansheng Cao, 2023. "Local flux coordination and global gene expression regulation in metabolic modeling," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    3. Ljubisa Miskovic & Jonas Béal & Michael Moret & Vassily Hatzimanikatis, 2019. "Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-29, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms13806. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.