Articles producció científica> Enginyeria Química

Phenomenological Model for Predicting the Catabolic Potential of an Arbitrary Nutrient

  • Identification data

    Identifier: imarina:9298216
    Authors:
    Seaver, Samuel M DSales-Pardo, MartaGuimera, RogerAmaral, Luis A Nunes
    Abstract:
    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. Citation: Seaver SMD, Sales-Pardo M, Guimera R, Amaral LAN (2012) Phenomenological Model for Predicting the Catabolic Potential of an Arbitrary Nutrient. PLoS Comput Biol 8(11): e1002762. doi:10.1371/journal
  • Others:

    Author, as appears in the article.: Seaver, Samuel M D; Sales-Pardo, Marta; Guimera, Roger; Amaral, Luis A Nunes
    Department: Enginyeria Química
    URV's Author/s: Guimera Manrique, Roger / Sales Pardo, Marta
    Keywords: Scale metabolic reconstruction Phosphoenolpyruvate Membrane-transport systems Mechanism Kinetics Growth Genomics Escherichia-coli Capabilities Annotation
    Abstract: 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. Citation: Seaver SMD, Sales-Pardo M, Guimera R, Amaral LAN (2012) Phenomenological Model for Predicting the Catabolic Potential of an Arbitrary Nutrient. PLoS Comput Biol 8(11): e1002762. doi:10.1371/journal.pcbi.1002762
    Thematic Areas: Saúde coletiva Psicología Molecular biology Modeling and simulation Medicina ii Medicina i Mathematics, interdisciplinary applications Mathematical & computational biology Matemática / probabilidade e estatística Interdisciplinar Genetics Ensino Engenharias iv Engenharias iii Ecology, evolution, behavior and systematics Ecology Computational theory and mathematics Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência da computação Cellular and molecular neuroscience Biotecnología Biodiversidade Biochemical research methods Astronomia / física
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: roger.guimera@urv.cat marta.sales@urv.cat
    Author identifier: 0000-0002-3597-4310 0000-0002-8140-6525
    Record's date: 2024-10-19
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002762
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Plos Computational Biology. 8 (11): e1002762-
    APA: Seaver, Samuel M D; Sales-Pardo, Marta; Guimera, Roger; Amaral, Luis A Nunes (2012). Phenomenological Model for Predicting the Catabolic Potential of an Arbitrary Nutrient. Plos Computational Biology, 8(11), e1002762-. DOI: 10.1371/journal.pcbi.1002762
    Article's DOI: 10.1371/journal.pcbi.1002762
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2012
    Publication Type: Journal Publications
  • Keywords:

    Biochemical Research Methods,Cellular and Molecular Neuroscience,Computational Theory and Mathematics,Ecology,Ecology, Evolution, Behavior and Systematics,Genetics,Mathematical & Computational Biology,Mathematics, Interdisciplinary Applications,Modeling and Simulation,Molecular Biology
    Scale metabolic reconstruction
    Phosphoenolpyruvate
    Membrane-transport systems
    Mechanism
    Kinetics
    Growth
    Genomics
    Escherichia-coli
    Capabilities
    Annotation
    Saúde coletiva
    Psicología
    Molecular biology
    Modeling and simulation
    Medicina ii
    Medicina i
    Mathematics, interdisciplinary applications
    Mathematical & computational biology
    Matemática / probabilidade e estatística
    Interdisciplinar
    Genetics
    Ensino
    Engenharias iv
    Engenharias iii
    Ecology, evolution, behavior and systematics
    Ecology
    Computational theory and mathematics
    Ciências biológicas ii
    Ciências biológicas i
    Ciências agrárias i
    Ciência da computação
    Cellular and molecular neuroscience
    Biotecnología
    Biodiversidade
    Biochemical research methods
    Astronomia / física
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