title: Predicting growth conditions from internal metabolic fluxes in an in-silico model of E. coli.
abstract: A widely studied problem in systems biology is to predict bacterial phenotype from growth conditions, using mechanistic models such as flux balance analysis (FBA). However, the inverse prediction of growth conditions from phenotype is rarely considered. Here we develop a computational framework to carry out this inverse prediction on a computational model of bacterial metabolism. We use FBA to calculate bacterial phenotypes from growth conditions in E. coli, and then we assess how accurately we can predict the original growth conditions from the phenotypes. Prediction is carried out via regularized multinomial regression. Our analysis provides several important physiological and statistical insights. First, we show that by analyzing metabolic end products we can consistently predict growth conditions. Second, prediction is reliable even in the presence of small amounts of impurities. Third, flux through a relatively small number of reactions per growth source (∼10) is sufficient for accurate prediction. Fourth, combining the predictions from two separate models, one trained only on carbon sources and one only on nitrogen sources, performs better than models trained to perform joint prediction. Finally, that separate predictions perform better than a more sophisticated joint prediction scheme suggests that carbon and nitrogen utilization pathways, despite jointly affecting cellular growth, may be fairly decoupled in terms of their dependence on specific assortments of molecular precursors.
authors.affiliation: Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, Texas, United States of America.
authors.forename: Viswanadham
authors.initials: V
authors.lastname: Sridhara
authors.affiliation: Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, Texas, United States of America; Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, United States of America.
authors.forename: Austin G
authors.initials: AG
authors.lastname: Meyer
authors.affiliation: Department of Computer Science, The University of Texas at Austin, Austin, Texas, United States of America.
authors.forename: Piyush
authors.initials: P
authors.lastname: Rai
authors.affiliation: Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, Texas, United States of America; Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, United States of America; Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, Texas, United States of America; Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas, United States of America.
authors.forename: Jeffrey E
authors.initials: JE
authors.lastname: Barrick
authors.affiliation: Department of Computer Science, The University of Texas at Austin, Austin, Texas, United States of America.
authors.forename: Pradeep
authors.initials: P
authors.lastname: Ravikumar
authors.affiliation: Department of Biology and Bioinformatics Program, Boston University, Boston, Massachusetts, United States of America.
authors.forename: Daniel
authors.initials: D
authors.lastname: Segrè
authors.affiliation: Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, Texas, United States of America; Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, United States of America; Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, Texas, United States of America; Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America.
authors.forename: Claus O
authors.initials: CO
authors.lastname: Wilke
mesh_terms: D002244:Carbon / Q000378:metabolism; D003198:Computer Simulation; D004926:Escherichia coli / Q000166:cytology / Q000254:growth & development* / Q000378:metabolism*; D064688:Metabolic Flux Analysis; D008954:Models, Biological; D009584:Nitrogen / Q000378:metabolism
chemical_list: D002244:Carbon; D009584:Nitrogen