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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Microbiol. | doi: 10.3389/fmicb.2018.03298

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

  • 1LS2N UMR6004, University of Nantes, France
  • 2Climate and Ecosystem Science division, Lawrence Berkeley National Laboratory (LBNL), United States
  • 3Geoscience, Princeton University, United States

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 physicochemical 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

Keywords: Modeling and simulations, microbial ecology, ammonia-oxidizing bacteria, Probabilistic models, Nitrogen

Received: 11 Jun 2018; Accepted: 18 Dec 2018.

Edited by:

Jorge L. Rodrigues, University of California, Davis, United States

Reviewed by:

Christopher Blackwood, Kent State University, United States
Md Abdul Wadud Khan, University of Texas MD Anderson Cancer Center, United States  

Copyright: © 2018 Eveillard, Bouskill, Vintache, Gras, Ward and Bourdon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Damien Eveillard, University of Nantes, LS2N UMR6004, Nantes, 44322, France, damien.eveillard@univ-nantes.fr