skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: SteadyCom: Predicting microbial abundances while ensuring community stability

Abstract

Genome-scale metabolic modeling has become widespread for analyzing microbial metabolism. Extending this established paradigm to more complex microbial communities is emerging as a promising way to unravel the interactions and biochemical repertoire of these omnipresent systems. While several modeling techniques have been developed for microbial communities, little emphasis has been placed on the need to impose a time-averaged constant growth rate across all members for a community to ensure co-existence and stability. In the absence of this constraint, the faster growing organism will ultimately displace all other microbes in the community. This is particularly important for predicting steady-state microbiota composition as it imposes significant restrictions on the allowable community membership, composition and phenotypes. In this study, we introduce the SteadyCom optimization framework for predicting metabolic flux distributions consistent with the steady-state requirement. SteadyCom can be rapidly converged by iteratively solving linear programming (LP) problem and the number of iterations is independent of the number of organisms. A significant advantage of SteadyCom is compatibility with flux variability analysis. SteadyCom is first demonstrated for a community of four E. coli double auxotrophic mutants and is then applied to a gut microbiota model consisting of nine species, with representatives from the phyla Bacteroidetes,more » Firmicutes, Actinobacteria and Proteobacteria. In contrast to the direct use of FBA, SteadyCom is able to predict the change in species abundance in response to changes in diets with minimal additional imposed constraints on the model. Furthermore, by randomizing the uptake rates of microbes, an abundance profile with a good agreement to experimental gut microbiota is inferred. SteadyCom provides an important step towards the cross-cutting task of predicting the composition of a microbial community in a given environment.« less

Authors:
ORCiD logo [1];  [1];  [1];  [2]
  1. The Pennsylvania State Univ., University Park, PA (United States)
  2. Institute for Systems Biology, Seattle, WA (United States)
Publication Date:
Research Org.:
Pennsylvania State Univ., University Park, PA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1360660
Grant/Contract Number:
SC0008091
Resource Type:
Journal Article: Published Article
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 13; Journal Issue: 5; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES; gut bacteria; diet; carbohydrates; metabolites; enterococcus faecalis; nutrients; microbiome; clostridium

Citation Formats

Chan, Siu Hung Joshua, Simons, Margaret N., Maranas, Costas D., and Price, Nathan D. SteadyCom: Predicting microbial abundances while ensuring community stability. United States: N. p., 2017. Web. doi:10.1371/journal.pcbi.1005539.
Chan, Siu Hung Joshua, Simons, Margaret N., Maranas, Costas D., & Price, Nathan D. SteadyCom: Predicting microbial abundances while ensuring community stability. United States. doi:10.1371/journal.pcbi.1005539.
Chan, Siu Hung Joshua, Simons, Margaret N., Maranas, Costas D., and Price, Nathan D. 2017. "SteadyCom: Predicting microbial abundances while ensuring community stability". United States. doi:10.1371/journal.pcbi.1005539.
@article{osti_1360660,
title = {SteadyCom: Predicting microbial abundances while ensuring community stability},
author = {Chan, Siu Hung Joshua and Simons, Margaret N. and Maranas, Costas D. and Price, Nathan D.},
abstractNote = {Genome-scale metabolic modeling has become widespread for analyzing microbial metabolism. Extending this established paradigm to more complex microbial communities is emerging as a promising way to unravel the interactions and biochemical repertoire of these omnipresent systems. While several modeling techniques have been developed for microbial communities, little emphasis has been placed on the need to impose a time-averaged constant growth rate across all members for a community to ensure co-existence and stability. In the absence of this constraint, the faster growing organism will ultimately displace all other microbes in the community. This is particularly important for predicting steady-state microbiota composition as it imposes significant restrictions on the allowable community membership, composition and phenotypes. In this study, we introduce the SteadyCom optimization framework for predicting metabolic flux distributions consistent with the steady-state requirement. SteadyCom can be rapidly converged by iteratively solving linear programming (LP) problem and the number of iterations is independent of the number of organisms. A significant advantage of SteadyCom is compatibility with flux variability analysis. SteadyCom is first demonstrated for a community of four E. coli double auxotrophic mutants and is then applied to a gut microbiota model consisting of nine species, with representatives from the phyla Bacteroidetes, Firmicutes, Actinobacteria and Proteobacteria. In contrast to the direct use of FBA, SteadyCom is able to predict the change in species abundance in response to changes in diets with minimal additional imposed constraints on the model. Furthermore, by randomizing the uptake rates of microbes, an abundance profile with a good agreement to experimental gut microbiota is inferred. SteadyCom provides an important step towards the cross-cutting task of predicting the composition of a microbial community in a given environment.},
doi = {10.1371/journal.pcbi.1005539},
journal = {PLoS Computational Biology (Online)},
number = 5,
volume = 13,
place = {United States},
year = 2017,
month = 5
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1371/journal.pcbi.1005539

Save / Share:
  • Genome-scale metabolic modeling has become widespread for analyzing microbial metabolism. Extending this established paradigm to more complex microbial communities is emerging as a promising way to unravel the interactions and biochemical repertoire of these omnipresent systems. While several modeling techniques have been developed for microbial communities, little emphasis has been placed on the need to impose a time-averaged constant growth rate across all members for a community to ensure co-existence and stability. In the absence of this constraint, the faster growing organism will ultimately displace all other microbes in the community. This is particularly important for predicting steady-state microbiota compositionmore » as it imposes significant restrictions on the allowable community membership, composition and phenotypes. In this study, we introduce the SteadyCom optimization framework for predicting metabolic flux distributions consistent with the steady-state requirement. SteadyCom can be rapidly converged by iteratively solving linear programming (LP) problem and the number of iterations is independent of the number of organisms. A significant advantage of SteadyCom is compatibility with flux variability analysis. SteadyCom is first demonstrated for a community of four E. coli double auxotrophic mutants and is then applied to a gut microbiota model consisting of nine species, with representatives from the phyla Bacteroidetes, Firmicutes, Actinobacteria and Proteobacteria. In contrast to the direct use of FBA, SteadyCom is able to predict the change in species abundance in response to changes in diets with minimal additional imposed constraints on the model. Furthermore, by randomizing the uptake rates of microbes, an abundance profile with a good agreement to experimental gut microbiota is inferred. SteadyCom provides an important step towards the cross-cutting task of predicting the composition of a microbial community in a given environment.« less
  • ‘Bioimmobilization’ of redox-sensitive heavy metals and radionuclides is being investigated as a way to remediate contaminated groundwater and sediments. In one approach, growth-limiting substrates are added to the subsurface to stimulate the activity of targeted groups of indigenous microorganisms and create conditions favorable for the microbially-mediated reductive precipitation (‘bioreduction’) of targeted contaminants. We present a theoretical framework for modeling this process that modifies conventional geochemical reaction path modeling to include thermodynamic descriptions for microbial growth and may be called biogeochemical reaction path modeling. In this approach, the actual microbial community is represented by a synthetic microbial community consisting of amore » collection of microbial groups; each with a unique growth equation that couples a specific pair of energy yielding redox reactions. The growth equations and their computed standard-state free energy yields are appended to the thermodynamic databasse used in conventional geochemical reaction path modeling, providing a direct coupling between chemical species participating in both microbial growth and geochemical reactions. To compute the biogeochemical reaction paths, growth substrates are added incrementally to a defined geochemical environment and the coupled equations are solved simultaneously to predict microbial biomass, community composition (i.e. the fraction of total biomass in each microbial group), and the aqueous and mineral composition of the system, including aqueous speciation and oxidation state of the targeted contaminants. The approach, with growth equations derived from the literature using well known bioenergetics principles, was used to predict the results of a laboratory microcosm experiment and an in situ field experiment that investigated the bioreduction of uranium. Predicted effects of ethanol or acetate addition on uranium concentration and speciation, major ion geochemistry, mineralogy, microbial biomass and community composition were in general agreement with experimental observations although the available experimental data precluded rigorous model testing. While originally developed for use in better understanding bioimmobilization of heavy metals and radionuclides, the modeling approach is potentially useful for exploring the coupling of microbial growth and geochemical reactions in a variety of other basic and applied biotechnology research settings.« less
  • An in-situ incubation device (ISI) was developed in order to investigate the stability and dynamics of sediment associated microbial communities to prevailing subsurface oxidizing or reducing conditions. Here we describe the use of these devices at the Old Rifle Uranium Mill Tailings Remedial Action (UMTRA) site. During the 7 month deployment oxidized Rifle aquifer background sediments (RABS) were deployed in previously biostimulated wells under iron reducing conditions, cell densities of known iron reducing bacteria including Geobacteraceae increased significantly showing the microbial community response to local subsurface conditions. PLFA profiles of RABS following in situ deployment were strikingly similar to thosemore » of adjacent sediment cores suggesting ISI results could be extrapolated to the native material of the test plots. Results for ISI deployed reduced sediments showed only slight changes in community composition and pointed toward the ability of the ISIs to monitor microbial community stability and response to subsurface conditions.« less