Predicting metabolic adaptation from networks of mutational paths

Christos Josephides, Peter S. Swain*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Competition for substrates is a ubiquitous selection pressure faced by microbes, yet intracellular trade-offs can prevent cells from metabolizing every type of available substrate. Adaptive evolution is constrained by these trade-offs, but their consequences for the repeatability and predictability of evolution are unclear. Here we develop an eco-evolutionary model with a metabolic trade-off to generate networks of mutational paths in microbial communities and show that these networks have descriptive and predictive information about the evolution of microbial communities. We find that long-term outcomes, including community collapse, diversity, and cycling, have characteristic evolutionary dynamics that determine the entropy, or repeatability, of mutational paths. Although reliable prediction of evolutionary outcomes from environmental conditions is difficult, graph-theoretic properties of the mutational networks enable accurate prediction even from incomplete observations. In conclusion, we present a novel methodology for analyzing adaptive evolution and report that the dynamics of adaptation are a key variable for predictive success.

Original languageEnglish
Article number685
Number of pages15
JournalNature Communications
Volume8
Issue number1
Early online date25 Sep 2017
DOIs
Publication statusPublished - 1 Dec 2017

Keywords

  • computational models
  • ecological modelling
  • evolutionary theory
  • microbial ecology

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