Probabilistic modeling of microbial metabolic networks for integrating partial quantitative knowledge within the nitrogen cycle

Damien Eveillard, Nicholas J. Bouskill, Damien Vintache, Julien Gras, Bettie Ward, Jérémie Bourdon

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Understanding the interactions between microbial communities and their environment sufficiently to predict diversity on the basis of physicochemical parameters is a fundamental pursuit of microbial ecology that still eludes us. However, modeling microbial communities is problematic, because (i) communities are complex, (ii) most descriptions are qualitative, and (iii) quantitative understanding of the way communities interact with their surroundings remains incomplete. One approach to overcoming such complications is the integration of partial qualitative and quantitative descriptions into more complex networks. Here we outline the development of a probabilistic framework, based on Event Transition Graph (ETG) theory, to predict microbial community structure across observed chemical data. Using reverse engineering, we derive probabilities from the ETG that accurately represent observations from experiments and predict putative constraints on communities within dynamic environments. These predictions can feedback into the future development of field experiments by emphasizing the most important functional reactions, and associated microbial strains, required to characterize microbial ecosystems.

Original languageEnglish (US)
Article number3298
JournalFrontiers in Microbiology
Volume10
Issue numberJAN
DOIs
StatePublished - 2019

All Science Journal Classification (ASJC) codes

  • Microbiology
  • Microbiology (medical)

Keywords

  • Ammonia oxidizing bacteria
  • Microbial ecology
  • Modeling
  • Nitrogen
  • Probabilistic simulation

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