title: Improving flux predictions by integrating data from multiple strains.
abstract: 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.
  
  
  
  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.
  
  
  
  Code is available in the Supplementary data available at Bioinformatics online.
  
  
  
  reed@engr.wisc.edu.
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.
authors.forename: Matthew R
authors.initials: MR
authors.lastname: Long
authors.affiliation: Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Reed
mesh_terms: D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D055786:Gene Knockout Techniques; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D012984:Software; D049490:Systems Biology / Q000379:methods*