title: Metabolic modelling in a dynamic evolutionary framework predicts adaptive diversification of bacteria in a long-term evolution experiment.
abstract: Predicting adaptive trajectories is a major goal of evolutionary biology and useful for practical applications. Systems biology has enabled the development of genome-scale metabolic models. However, analysing these models via flux balance analysis (FBA) cannot predict many evolutionary outcomes including adaptive diversification, whereby an ancestral lineage diverges to fill multiple niches. Here we combine in silico evolution with FBA and apply this modelling framework, evoFBA, to a long-term evolution experiment with Escherichia coli.
  
  
  
  Simulations predicted the adaptive diversification that occurred in one experimental population and generated hypotheses about the mechanisms that promoted coexistence of the diverged lineages. We experimentally tested and, on balance, verified these mechanisms, showing that diversification involved niche construction and character displacement through differential nutrient uptake and altered metabolic regulation.
  
  
  
  The evoFBA framework represents a promising new way to model biochemical evolution, one that can generate testable predictions about evolutionary and ecosystem-level outcomes.
authors.affiliation: School of Life Sciences, University of Warwick, Coventry, UK.
authors.forename: Tobias
authors.initials: T
authors.lastname: Großkopf
authors.affiliation: University of Grenoble Alpes, Laboratoire Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble (TIMC-IMAG), F-38000, Grenoble, France.|Centre National de la Recherche Scientifique (CNRS), TIMC-IMAG, F-38000, Grenoble, France.
authors.forename: Jessika
authors.initials: J
authors.lastname: Consuegra
authors.affiliation: University of Grenoble Alpes, Laboratoire Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble (TIMC-IMAG), F-38000, Grenoble, France.|Centre National de la Recherche Scientifique (CNRS), TIMC-IMAG, F-38000, Grenoble, France.
authors.forename: Joël
authors.initials: J
authors.lastname: Gaffé
authors.affiliation: University of Grenoble Alpes, Institut de recherches en technologies et sciences pour le vivant - Laboratoire de chimie et biologie des métaux (iRTSV-LCBM), Grenoble, F-38000, France.|CNRS, iRTSV-LCBM, F-38000, Grenoble, France.|Commissariat à l'énergie atomique (CEA), iRTSV-LCBM, F-38000, Grenoble, France.
authors.forename: John C
authors.initials: JC
authors.lastname: Willison
authors.affiliation: Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, 48824, USA.|BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI, 48824, USA.
authors.forename: Richard E
authors.initials: RE
authors.lastname: Lenski
authors.affiliation: School of Life Sciences, University of Warwick, Coventry, UK. o.soyer@warwick.ac.uk.
authors.forename: Orkun S
authors.initials: OS
authors.lastname: Soyer
authors.affiliation: University of Grenoble Alpes, Laboratoire Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble (TIMC-IMAG), F-38000, Grenoble, France. dominique.schneider@ujf-grenoble.fr.|Centre National de la Recherche Scientifique (CNRS), TIMC-IMAG, F-38000, Grenoble, France. dominique.schneider@ujf-grenoble.fr.
authors.forename: Dominique
authors.initials: D
authors.lastname: Schneider
mesh_terms: D005075:Biological Evolution; D003198:Computer Simulation; D017753:Ecosystem; D004926:Escherichia coli / Q000235:genetics* / Q000254:growth & development / Q000378:metabolism*; D008954:Models, Biological
keywords: Adaptive diversification; Experimental evolution; FBA; In silico evolution; Tradeoffs