Probabilistic Modeling of Microbial Metabolic Networks for Integrating Partial Quantitative Knowledge Within the Nitrogen Cycle
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.
- Authors:
-
- Univ. de Nantes, Nantes (France). Centrale Nantes; Research Federation (FR2022) Tara Oceans Global Ocean System Ecology & Evolution (GO-SEE), Paris (France)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Univ. de Nantes, Nantes (France). Centrale Nantes
- Princeton Univ., NJ (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1561879
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Frontiers in Microbiology
- Additional Journal Information:
- Journal Volume: 9; Journal Issue: JAN; Journal ID: ISSN 1664-302X
- Publisher:
- Frontiers Research Foundation
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; modeling; microbial ecology; ammonia oxidizing bacteria; probabilistic simulation; nitrogen
Citation Formats
Eveillard, Damien, Bouskill, Nicholas J., Vintache, Damien, Gras, Julien, Ward, Bess B., and Bourdon, Jérémie. Probabilistic Modeling of Microbial Metabolic Networks for Integrating Partial Quantitative Knowledge Within the Nitrogen Cycle. United States: N. p., 2019.
Web. doi:10.3389/fmicb.2018.03298.
Eveillard, Damien, Bouskill, Nicholas J., Vintache, Damien, Gras, Julien, Ward, Bess B., & Bourdon, Jérémie. Probabilistic Modeling of Microbial Metabolic Networks for Integrating Partial Quantitative Knowledge Within the Nitrogen Cycle. United States. https://doi.org/10.3389/fmicb.2018.03298
Eveillard, Damien, Bouskill, Nicholas J., Vintache, Damien, Gras, Julien, Ward, Bess B., and Bourdon, Jérémie. Mon .
"Probabilistic Modeling of Microbial Metabolic Networks for Integrating Partial Quantitative Knowledge Within the Nitrogen Cycle". United States. https://doi.org/10.3389/fmicb.2018.03298. https://www.osti.gov/servlets/purl/1561879.
@article{osti_1561879,
title = {Probabilistic Modeling of Microbial Metabolic Networks for Integrating Partial Quantitative Knowledge Within the Nitrogen Cycle},
author = {Eveillard, Damien and Bouskill, Nicholas J. and Vintache, Damien and Gras, Julien and Ward, Bess B. and Bourdon, Jérémie},
abstractNote = {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.},
doi = {10.3389/fmicb.2018.03298},
journal = {Frontiers in Microbiology},
number = JAN,
volume = 9,
place = {United States},
year = {Mon Jan 28 00:00:00 EST 2019},
month = {Mon Jan 28 00:00:00 EST 2019}
}
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FIGURE 1: Network of the nitrogen cycle and its probabilistic simulation. (A) represents the nitrogen cycle where nodes are reactions as described in KEGG, and edges putative transitions between reactions when a product of a reaction is a substrate of another. (B) depicts in the simplistic metabolic effect asociated tomore »
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