Literature DB >> 12700149

Modeling microbial consortiums as distributed metabolic networks.

Joseph J Vallino1.   

Abstract

Biogeochemistry is the study of how living systems in combination with abiotic reactions process and cycle mass and energy on local, regional, and global scales (Schlesinger, 1997). Understanding how these biogeochemical cycles function and respond to perturbations has become increasingly important, as anthropogenic impacts have significantly altered many of these cycles (Galloway and Cowling, 2002; Houghton et al., 2002). Biogeochemistry is strongly governed by microbial processes, and it appears to closely follow thermodynamic constraints in that electron acceptor (O(2), NO(3)(-), SO(4)(2-), etc.) utilization closely follows a priori expectations based on energetics (Vallino et al., 1996; Hoehler et al., 1998; Jakobsen and Postma, 1999; Amend and Shock, 2001). Consortiums of microorganisms seem to have evolved to exploit chemical potentials wherever they exist in the environment, as manifested by the recent discovery of anaerobic methane oxidation by sulfate (Boetius et al., 2000) or sulfide oxidation by nitrate (Schulz et al., 1999). Three and a half billion years of natural selection have produced living systems capable of degrading most chemical potentials. We may therefore ask: If all ecosystem niche space is filled, is the biogeochemistry we observe in the environment dependent on the organisms that occupy that environment, or is the biogeochemistry determined by fundamental forces, with the evolution of living systems being the implementation of those forces? Recent developments in nonequilibrium thermodynamics (NET) are beginning to support the latter alternative, and advances in genomics are allowing us to explore microbial consortiums in detail. Taking advantage of ideas being suggested by NET, we have developed a modeling framework that views microbial consortiums as an inter-species distributed metabolic network. When combined with experimental observations, the model should help us test hypotheses that govern how living systems function.

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Year:  2003        PMID: 12700149     DOI: 10.2307/1543554

Source DB:  PubMed          Journal:  Biol Bull        ISSN: 0006-3185            Impact factor:   1.818


  8 in total

Review 1.  Origins and evolution of antibiotic resistance.

Authors:  Julian Davies; Dorothy Davies
Journal:  Microbiol Mol Biol Rev       Date:  2010-09       Impact factor: 11.056

2.  Ecosystem biogeochemistry considered as a distributed metabolic network ordered by maximum entropy production.

Authors:  Joseph J Vallino
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2010-05-12       Impact factor: 6.237

Review 3.  Unraveling interactions in microbial communities - from co-cultures to microbiomes.

Authors:  Justin Tan; Cristal Zuniga; Karsten Zengler
Journal:  J Microbiol       Date:  2015-05-03       Impact factor: 3.422

4.  Microbial catabolic activities are naturally selected by metabolic energy harvest rate.

Authors:  Rebeca González-Cabaleiro; Irina D Ofiţeru; Juan M Lema; Jorge Rodríguez
Journal:  ISME J       Date:  2015-07-10       Impact factor: 10.302

5.  Investigation of microbial community interactions between Lake Washington methanotrophs using -------genome-scale metabolic modeling.

Authors:  Mohammad Mazharul Islam; Tony Le; Shardhat R Daggumati; Rajib Saha
Journal:  PeerJ       Date:  2020-06-30       Impact factor: 2.984

6.  OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities.

Authors:  Ali R Zomorrodi; Costas D Maranas
Journal:  PLoS Comput Biol       Date:  2012-02-02       Impact factor: 4.475

7.  Metabolic energy-based modelling explains product yielding in anaerobic mixed culture fermentations.

Authors:  Rebeca González-Cabaleiro; Juan M Lema; Jorge Rodríguez
Journal:  PLoS One       Date:  2015-05-18       Impact factor: 3.240

8.  Microbial Communities Are Well Adapted to Disturbances in Energy Input.

Authors:  Nuria Fernandez-Gonzalez; Julie A Huber; Joseph J Vallino
Journal:  mSystems       Date:  2016-09-13       Impact factor: 6.496

  8 in total

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