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Title: 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:
 [1];  [2];  [1];  [3];  [4];  [3]
  1. Univ. de Nantes, Nantes (France). Centrale Nantes; Research Federation (FR2022) Tara Oceans Global Ocean System Ecology & Evolution (GO-SEE), Paris (France)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. Univ. de Nantes, Nantes (France). Centrale Nantes
  4. Princeton Univ., NJ (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
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. doi: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. doi: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 = {2019},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Figures / Tables:

FIGURE 1 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 » each reaction involved in (A). (C) depicts 11 time point nutrient concentrations (red dots) as described in Bouskill et al. (2011) station CB100 in Chesapeake Bay, as well as nine probabilistic simulations of the ETG model trained on ammonia and nitrite concentrations between 2001 and 2004. Corresponding probabilities are depicted in Figure 2. Bold lines depict averages of quantities over 10 simulations, whereas gray areas show the corresponding standard deviations.« less

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    Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.