Phenomenological model for predicting the catabolic potential of an arbitrary nutrient.

The ability of microbial species to consume compounds found in the environment to generate commercially-valuable products has long been exploited by humanity. The untapped, staggering diversity of microbial organisms offers a wealth of potential resources for tackling medical, environmental, and ene...

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Autores principales:	Samuel M D Seaver, Marta Sales-Pardo, Roger Guimerà, Luís A Nunes Amaral
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/702bec38a82144eea5ea175913fed40f
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spelling	oai:doaj.org-article:702bec38a82144eea5ea175913fed40f2021-11-18T05:52:43ZPhenomenological model for predicting the catabolic potential of an arbitrary nutrient.1553-734X1553-735810.1371/journal.pcbi.1002762https://doaj.org/article/702bec38a82144eea5ea175913fed40f2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23133365/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The ability of microbial species to consume compounds found in the environment to generate commercially-valuable products has long been exploited by humanity. The untapped, staggering diversity of microbial organisms offers a wealth of potential resources for tackling medical, environmental, and energy challenges. Understanding microbial metabolism will be crucial to many of these potential applications. Thermodynamically-feasible metabolic reconstructions can be used, under some conditions, to predict the growth rate of certain microbes using constraint-based methods. While these reconstructions are powerful, they are still cumbersome to build and, because of the complexity of metabolic networks, it is hard for researchers to gain from these reconstructions an understanding of why a certain nutrient yields a given growth rate for a given microbe. Here, we present a simple model of biomass production that accurately reproduces the predictions of thermodynamically-feasible metabolic reconstructions. Our model makes use of only: i) a nutrient's structure and function, ii) the presence of a small number of enzymes in the organism, and iii) the carbon flow in pathways that catabolize nutrients. When applied to test organisms, our model allows us to predict whether a nutrient can be a carbon source with an accuracy of about 90% with respect to in silico experiments. In addition, our model provides excellent predictions of whether a medium will produce more or less growth than another (p<10(-6)) and good predictions of the actual value of the in silico biomass production.Samuel M D SeaverMarta Sales-PardoRoger GuimeràLuís A Nunes AmaralPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 11, p e1002762 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Samuel M D Seaver Marta Sales-Pardo Roger Guimerà Luís A Nunes Amaral Phenomenological model for predicting the catabolic potential of an arbitrary nutrient.
description	The ability of microbial species to consume compounds found in the environment to generate commercially-valuable products has long been exploited by humanity. The untapped, staggering diversity of microbial organisms offers a wealth of potential resources for tackling medical, environmental, and energy challenges. Understanding microbial metabolism will be crucial to many of these potential applications. Thermodynamically-feasible metabolic reconstructions can be used, under some conditions, to predict the growth rate of certain microbes using constraint-based methods. While these reconstructions are powerful, they are still cumbersome to build and, because of the complexity of metabolic networks, it is hard for researchers to gain from these reconstructions an understanding of why a certain nutrient yields a given growth rate for a given microbe. Here, we present a simple model of biomass production that accurately reproduces the predictions of thermodynamically-feasible metabolic reconstructions. Our model makes use of only: i) a nutrient's structure and function, ii) the presence of a small number of enzymes in the organism, and iii) the carbon flow in pathways that catabolize nutrients. When applied to test organisms, our model allows us to predict whether a nutrient can be a carbon source with an accuracy of about 90% with respect to in silico experiments. In addition, our model provides excellent predictions of whether a medium will produce more or less growth than another (p<10(-6)) and good predictions of the actual value of the in silico biomass production.
format	article
author	Samuel M D Seaver Marta Sales-Pardo Roger Guimerà Luís A Nunes Amaral
author_facet	Samuel M D Seaver Marta Sales-Pardo Roger Guimerà Luís A Nunes Amaral
author_sort	Samuel M D Seaver
title	Phenomenological model for predicting the catabolic potential of an arbitrary nutrient.
title_short	Phenomenological model for predicting the catabolic potential of an arbitrary nutrient.
title_full	Phenomenological model for predicting the catabolic potential of an arbitrary nutrient.
title_fullStr	Phenomenological model for predicting the catabolic potential of an arbitrary nutrient.
title_full_unstemmed	Phenomenological model for predicting the catabolic potential of an arbitrary nutrient.
title_sort	phenomenological model for predicting the catabolic potential of an arbitrary nutrient.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/702bec38a82144eea5ea175913fed40f
work_keys_str_mv	AT samuelmdseaver phenomenologicalmodelforpredictingthecatabolicpotentialofanarbitrarynutrient AT martasalespardo phenomenologicalmodelforpredictingthecatabolicpotentialofanarbitrarynutrient AT rogerguimera phenomenologicalmodelforpredictingthecatabolicpotentialofanarbitrarynutrient AT luisanunesamaral phenomenologicalmodelforpredictingthecatabolicpotentialofanarbitrarynutrient
_version_	1718424734277828608

Phenomenological model for predicting the catabolic potential of an arbitrary nutrient.

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