title: Structural analyses of a hypothetical minimal metabolism.
abstract: By integrating data from comparative genomics and large-scale deletion studies, we previously proposed a minimal gene set comprising 206 protein-coding genes. To evaluate the consistency of the metabolism encoded by such a minimal genome, we have carried out a series of computational analyses. Firstly, the topology of the minimal metabolism was compared with that of the reconstructed networks from natural bacterial genomes. Secondly, the robustness of the metabolic network was evaluated by simulated mutagenesis and, finally, the stoichiometric consistency was assessed by automatically deriving the steady-state solutions from the reaction set. The results indicated that the proposed minimal metabolism presents stoichiometric consistency and that it is organized as a complex power-law network with topological parameters falling within the expected range for a natural metabolism of its size. The robustness analyses revealed that most random mutations do not alter the topology of the network significantly, but do cause significant damage by preventing the synthesis of several compounds or compromising the stoichiometric consistency of the metabolism. The implications that these results have on the origins of metabolic complexity and the theoretical design of an artificial minimal cell are discussed.
authors.affiliation: Bioinformatics Department, Centro de Investigación Príncipe Felipe, Avda. Autopista del Saler, 16. 46013 València, Spain.
authors.forename: Toni
authors.initials: T
authors.lastname: Gabaldón
authors.forename: Juli
authors.initials: J
authors.lastname: Peretó
authors.forename: Francisco
authors.initials: F
authors.lastname: Montero
authors.forename: Rosario
authors.initials: R
authors.lastname: Gil
authors.forename: Amparo
authors.initials: A
authors.lastname: Latorre
authors.forename: Andrés
authors.initials: A
authors.lastname: Moya
mesh_terms: D002468:Cell Physiological Phenomena; D002477:Cells / Q000378:metabolism*; D016000:Cluster Analysis; D003198:Computer Simulation; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D015233:Models, Statistical