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Genotype networks in metabolic reaction spaces

Samal, A; Matias Rodrigues, J F; Jost, J; Martin, O C; Wagner, A (2010). Genotype networks in metabolic reaction spaces. BMC Systems Biology, 4:30.

Abstract

BACKGROUND: A metabolic genotype comprises all chemical reactions an organism can catalyze via enzymes encoded in its genome. A genotype is viable in a given environment if it is capable of producing all biomass components the organism needs to survive and reproduce. Previous work has focused on the properties of individual genotypes while little is known about how genome-scale metabolic networks with a given function can vary in their reaction content. RESULTS: We here characterize spaces of such genotypes. Specifically, we study metabolic genotypes whose phenotype is viability in minimal chemical environments that differ in their sole carbon sources. We show that regardless of the number of reactions in a metabolic genotype, the genotypes of a given phenotype typically form vast, connected, and unstructured sets -- genotype networks -- that nearly span the whole of genotype space. The robustness of metabolic phenotypes to random reaction removal in such spaces has a narrow distribution with a high mean. Different carbon sources differ in the number of metabolic genotypes in their genotype network; this number decreases as a genotype is required to be viable on increasing numbers of carbon sources, but much less than if metabolic reactions were used independently across different chemical environments. CONCLUSIONS: Our work shows that phenotype-preserving genotype networks have generic organizational properties and that these properties are insensitive to the number of reactions in metabolic genotypes.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Department of Biochemistry
07 Faculty of Science > Department of Biochemistry
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Life Sciences > Structural Biology
Physical Sciences > Modeling and Simulation
Life Sciences > Molecular Biology
Physical Sciences > Computer Science Applications
Physical Sciences > Applied Mathematics
Language:English
Date:2010
Deposited On:13 Aug 2010 07:41
Last Modified:04 Oct 2024 01:39
Publisher:BioMed Central
ISSN:1752-0509
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1186/1752-0509-4-30
PubMed ID:20302636
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  • Licence: Creative Commons: Attribution 2.0 Generic (CC BY 2.0)

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