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Probabilistic Modeling of Microbial Metabolic Networks for Integrating Partial Quantitative Knowledge Within the Nitrogen Cycle.
Eveillard, Damien; Bouskill, Nicholas J; Vintache, Damien; Gras, Julien; Ward, Bess B; Bourdon, Jérémie.
Afiliação
  • Eveillard D; LS2N, UMR6004 CNRS, Université de Nantes, Centrale Nantes, IMTA, Nantes, France.
  • Bouskill NJ; Research Federation (FR2022) Tara Oceans GO-SEE, Paris, France.
  • Vintache D; Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States.
  • Gras J; LS2N, UMR6004 CNRS, Université de Nantes, Centrale Nantes, IMTA, Nantes, France.
  • Ward BB; Research Federation (FR2022) Tara Oceans GO-SEE, Paris, France.
  • Bourdon J; LS2N, UMR6004 CNRS, Université de Nantes, Centrale Nantes, IMTA, Nantes, France.
Front Microbiol ; 9: 3298, 2018.
Article em En | MEDLINE | ID: mdl-30745899
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Qualitative_research Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Qualitative_research Idioma: En Ano de publicação: 2018 Tipo de documento: Article