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Modelling microbial communities using biochemical resource allocation analysis

To understand the functioning and dynamics of microbial communities is a fundamental challenge in current biology. To tackle this challenge, the construction of computational models of interacting microbes is an indispensable tool. There is, however, a large chasm between ecologically motivated desc...

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Detalles Bibliográficos
Autores principales: Sharma, Suraj, Steuer, Ralf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6893496/
https://www.ncbi.nlm.nih.gov/pubmed/31690234
http://dx.doi.org/10.1098/rsif.2019.0474
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author Sharma, Suraj
Steuer, Ralf
author_facet Sharma, Suraj
Steuer, Ralf
author_sort Sharma, Suraj
collection PubMed
description To understand the functioning and dynamics of microbial communities is a fundamental challenge in current biology. To tackle this challenge, the construction of computational models of interacting microbes is an indispensable tool. There is, however, a large chasm between ecologically motivated descriptions of microbial growth used in many current ecosystems simulations, and detailed metabolic pathway and genome-based descriptions developed in the context of systems and synthetic biology. Here, we seek to demonstrate how resource allocation models of microbial growth offer the potential to advance ecosystem simulations and their parametrization. In particular, recent work on quantitative resource allocation allow us to formulate mechanistic models of microbial growth that are physiologically meaningful while remaining computationally tractable. These models go beyond Michaelis–Menten and Monod-type growth models, and are capable of accounting for emergent properties that underlie the remarkable plasticity of microbial growth. We outline the utility and advantages of using biochemical resource allocation models by considering a coarse-grained model of cyanobacterial growth and demonstrate how the model allows us to address specific questions of relevance for the simulation of marine microbial ecosystems, including the physiological acclimation of protein expression to different environments, the description of co-limitation by several nutrients and the differential use of alternative nutrient sources, as well as the description of metabolic diversity based on our increasing knowledge about quantitative cell physiology.
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spelling pubmed-68934962019-12-09 Modelling microbial communities using biochemical resource allocation analysis Sharma, Suraj Steuer, Ralf J R Soc Interface Life Sciences–Mathematics interface To understand the functioning and dynamics of microbial communities is a fundamental challenge in current biology. To tackle this challenge, the construction of computational models of interacting microbes is an indispensable tool. There is, however, a large chasm between ecologically motivated descriptions of microbial growth used in many current ecosystems simulations, and detailed metabolic pathway and genome-based descriptions developed in the context of systems and synthetic biology. Here, we seek to demonstrate how resource allocation models of microbial growth offer the potential to advance ecosystem simulations and their parametrization. In particular, recent work on quantitative resource allocation allow us to formulate mechanistic models of microbial growth that are physiologically meaningful while remaining computationally tractable. These models go beyond Michaelis–Menten and Monod-type growth models, and are capable of accounting for emergent properties that underlie the remarkable plasticity of microbial growth. We outline the utility and advantages of using biochemical resource allocation models by considering a coarse-grained model of cyanobacterial growth and demonstrate how the model allows us to address specific questions of relevance for the simulation of marine microbial ecosystems, including the physiological acclimation of protein expression to different environments, the description of co-limitation by several nutrients and the differential use of alternative nutrient sources, as well as the description of metabolic diversity based on our increasing knowledge about quantitative cell physiology. The Royal Society 2019-11 2019-11-06 /pmc/articles/PMC6893496/ /pubmed/31690234 http://dx.doi.org/10.1098/rsif.2019.0474 Text en © 2019 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Life Sciences–Mathematics interface
Sharma, Suraj
Steuer, Ralf
Modelling microbial communities using biochemical resource allocation analysis
title Modelling microbial communities using biochemical resource allocation analysis
title_full Modelling microbial communities using biochemical resource allocation analysis
title_fullStr Modelling microbial communities using biochemical resource allocation analysis
title_full_unstemmed Modelling microbial communities using biochemical resource allocation analysis
title_short Modelling microbial communities using biochemical resource allocation analysis
title_sort modelling microbial communities using biochemical resource allocation analysis
topic Life Sciences–Mathematics interface
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6893496/
https://www.ncbi.nlm.nih.gov/pubmed/31690234
http://dx.doi.org/10.1098/rsif.2019.0474
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