Abstract
BackgroundPredicting 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.ResultsSimulations 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.ConclusionThe evoFBA framework represents a promising new way to model biochemical evolution, one that can generate testable predictions about evolutionary and ecosystem-level outcomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-016-0733-x) contains supplementary material, which is available to authorized users.
Highlights
Predicting adaptive trajectories is a major goal of evolutionary biology and useful for practical applications
To model the long-term evolution experiment (LTEE), we ran Evolutionary flux balance analysis (evoFBA) simulations starting with a metabolic model of E. coli that accounts for 14 carbon sources including glucose and byproducts that can be scavenged from the environment to produce biomass and fuel associated core metabolic reactions
In each evoFBA simulation, we allowed the metabolic model to change by random mutations under global constraints that must be obeyed
Summary
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 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. Understanding whether and how these dynamics lead to the splitting and divergence of lineages is of central interest, as these processes represent the initial steps towards speciation. To this end, several theoretical studies have shown that cellular tradeoffs can promote lineage divergence [10,11,12,13,14,15,16]. If there were no tradeoffs, one would predict that cells should maximize their expression of transporters and their surface area to achieve the highest
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