Autor según el artículo: Amaral, L.A.N. Guimera, R. Sales-Pardo, M. Seaver, S.M.D.
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Departamento: Enginyeria Química
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Resumen: 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 and good predictions of the actual value of the in silico biomass production.
Entidad: Universitat Rovira i Virgili.
Áreas temáticas: Metabolic Networks
Año de publicación de la revista: 2012
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 1553-734X
Volumen de revista: 8
Repositori URV