Metabolite-mediated modelling of microbial community dynamics captures emergent behaviour more effectively than species–species modelling

J. D. Brunner, N. Chia

Research output: Contribution to journalArticle

Abstract

Personalized models of the gut microbiome are valuable for disease prevention and treatment. For this, one requires a mathematical model that predicts microbial community composition and the emergent behaviour of microbial communities. We seek a modelling strategy that can capture emergent behaviour when built from sets of universal individual interactions. Our investigation reveals that species–metabolite interaction (SMI) modelling is better able to capture emergent behaviour in community composition dynamics than direct species–species modelling. Using publicly available data, we examine the ability of species–species models and species–metabolite models to predict trio growth experiments from the outcomes of pair growth experiments. We compare quadratic species–species interaction models and quadratic SMI models and conclude that only species–metabolite models have the necessary complexity to explain a wide variety of interdependent growth outcomes. We also show that general species–species interaction models cannot match the patterns observed in community growth dynamics, whereas species–metabolite models can. We conclude that species–metabolite modelling will be important in the development of accurate, clinically useful models of microbial communities.

Original languageEnglish (US)
Article number20190423
JournalJournal of the Royal Society Interface
Volume16
Issue number159
DOIs
StatePublished - Oct 1 2019

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Metabolites
Growth
Theoretical Models
Chemical analysis
Experiments
Mathematical models

Keywords

  • Metabolite-mediated modelling
  • Microbial ecology
  • Microbiome
  • Pairwise modelling

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Bioengineering
  • Biomaterials
  • Biochemistry
  • Biomedical Engineering

Cite this

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abstract = "Personalized models of the gut microbiome are valuable for disease prevention and treatment. For this, one requires a mathematical model that predicts microbial community composition and the emergent behaviour of microbial communities. We seek a modelling strategy that can capture emergent behaviour when built from sets of universal individual interactions. Our investigation reveals that species–metabolite interaction (SMI) modelling is better able to capture emergent behaviour in community composition dynamics than direct species–species modelling. Using publicly available data, we examine the ability of species–species models and species–metabolite models to predict trio growth experiments from the outcomes of pair growth experiments. We compare quadratic species–species interaction models and quadratic SMI models and conclude that only species–metabolite models have the necessary complexity to explain a wide variety of interdependent growth outcomes. We also show that general species–species interaction models cannot match the patterns observed in community growth dynamics, whereas species–metabolite models can. We conclude that species–metabolite modelling will be important in the development of accurate, clinically useful models of microbial communities.",
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