Genotype networks in metabolic reaction spaces
- Equal contributors
1 Max Planck Institute for Mathematics in the Sciences, Inselstr 22, 04103 Leipzig, Germany
2 INRA, UMR 0320/UMR 8120 Génétique Végétale, Univ Paris-Sud, F-91190 Gif-sur-Yvette, France
3 Laboratoire de Physique Théorique et Modèles Statistiques, CNRS, Univ Paris-Sud, UMR 8626, F-91405 Orsay Cedex, France
4 Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
5 Swiss Institute of Bioinformatics, Quartier Sorge, Batiment Genopode, 1015 Lausanne, Switzerland
6 The Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
7 University of New Mexico, Department of Biology, 167 Castetter Hall, Albuquerque, MSC03 2020, USA
BMC Systems Biology 2010, 4:30 doi:10.1186/1752-0509-4-30Published: 19 March 2010
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.
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.
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.