http://www.cnr.it/ontology/cnr/individuo/prodotto/ID279152
Identifying essential genes in Escherichia coli from a metabolic optimization principle (Articolo in rivista)
- Type
- Label
- Identifying essential genes in Escherichia coli from a metabolic optimization principle (Articolo in rivista) (literal)
- Anno
- 2009-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1073/pnas.0813229106 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Carlotta Martelli (a); Andrea De Martino (b); Enzo Marinari (c); Matteo Marsili (d); Isaac Pérez Castillo (e) (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://www.pnas.org/content/106/8/2607 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
- Note
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- (a) Dipartimento di Fisica,
(b) Consiglio Nazionale delle Ricerche (Istituto per i Sistemi Complessi)/Istituto Nazionale per la Fisica della Materia (Centre for Statistical Mechanics and Complexity) and Dipartimento di Fisica, and
(c) Dipartimento di Fisica, Consiglio Nazionale delle Ricerche/Istituto Nazionale per la Fisica della Materia and Istituto Nazionale di Fisica Nucleare, Università di Roma \"La Sapienza\", Piazzale A. Moro 2, 00185 Rome, Italy;
(d) The Abdus Salam International Center for Theoretical Physics, Strada Costiera 11, 34014 Trieste, Italy; and
(e) Department of Mathematics, King's College London, Strand, London WC2R 2LS, United Kingdom (literal)
- Titolo
- Identifying essential genes in Escherichia coli from a metabolic optimization principle (literal)
- Abstract
- Understanding the organization of reaction fluxes in cellular metabolism from the stoichiometry and the topology of the underlying biochemical network is a central issue in systems biology. In this task, it is important to devise reasonable approximation schemes that rely on the stoichiometric data only, because full-scale kinetic approaches are computationally affordable only for small networks (e.g., red blood cells, ?50 reactions). Methods commonly used are based on finding the stationary flux configurations that satisfy mass-balance conditions for metabolites, often coupling them to local optimization rules (e.g., maximization of biomass production) to reduce the size of the solution space to a single point. Such methods have been widely applied and have proven able to reproduce experimental findings for relatively simple organisms in specific conditions. Here, we define and study a constraint-based model of cellular metabolism where neither mass balance nor flux stationarity are postulated and where the relevant flux configurations optimize the global growth of the system. In the case of Escherichia coli, steady flux states are recovered as solutions, although mass-balance conditions are violated for some metabolites, implying a nonzero net production of the latter. Such solutions furthermore turn out to provide the correct statistics of fluxes for the bacterium E. coli in different environments and compare well with the available experimental evidence on individual fluxes. Conserved metabolic pools play a key role in determining growth rate and flux variability. Finally, we are able to connect phenomenological gene essentiality with \"frozen\" fluxes (i.e., fluxes with smaller allowed variability) in E. coli metabolism. (literal)
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