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Long MR, Reed JL. Improving flux predictions by integrating data from multiple strains. Bioinformatics. 2017;33(6):893-900doi: 10.1093/bioinformatics/btw706.
Long, M. R., & Reed, J. L. (2017). Improving flux predictions by integrating data from multiple strains. Bioinformatics (Oxford, England), 33(6), 893-900. https://doi.org/10.1093/bioinformatics/btw706
Long, Matthew R, and Reed, Jennifer L. "Improving flux predictions by integrating data from multiple strains." Bioinformatics (Oxford, England) vol. 33,6 (2017): 893-900. doi: https://doi.org/10.1093/bioinformatics/btw706
Long MR, Reed JL. Improving flux predictions by integrating data from multiple strains. Bioinformatics. 2017 Mar 15;33(6):893-900. doi: 10.1093/bioinformatics/btw706. PMID: 27998937.
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Bioinformatics. 2017 Mar 15;33(6):893-900. doi: 10.1093/bioinformatics/btw706.

Improving flux predictions by integrating data from multiple strains.

Bioinformatics (Oxford, England)

Matthew R Long, Jennifer L Reed

Affiliations

  1. Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.

PMID: 27998937 DOI: 10.1093/bioinformatics/btw706

Abstract

MOTIVATION: Incorporating experimental data into constraint-based models can improve the quality and accuracy of their metabolic flux predictions. Unfortunately, routinely and easily measured experimental data such as growth rates, extracellular fluxes, transcriptomics and even proteomics are not always sufficient to significantly improve metabolic flux predictions.

RESULTS: We developed a new method (called REPPS) for incorporating experimental measurements of growth rates and extracellular fluxes from a set of perturbed reference strains (RSs) and a parental strain (PS) to substantially improve the predicted flux distribution of the parental strain. Using data from five single gene knockouts and the wild type strain, we decrease the mean squared error of predicted central metabolic fluxes by ∼47% compared to parsimonious flux balance analysis (pFBA). This decrease in error further improves flux predictions for new knockout strains. Furthermore, REPPS is less sensitive to the completeness of the metabolic network than pFBA.

AVAILABILITY AND IMPLEMENTATION: Code is available in the Supplementary data available at Bioinformatics online.

CONTACT: [email protected].

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: [email protected]

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