2009/02/06 by C. Martelli, Carlotta Martelli, A. De Martino +7
Biochemistry, Genetics and Molecular Biology · Chemistry · Mathematics · Physics and Astronomy · #Biochemical engineering #Biochemistry #Biological system #Biology #Chemistry #Computational biology #Constraint (computer-aided design) #Detailed balance #Escherichia coli #Flux (metallurgy) #Flux balance analysis #Gene #Gene Regulatory Network Analysis #Genetics #Mathematics #Metabolic flux analysis #Metabolic network #Metabolism #Microbial Metabolic Engineering and Bioproduction #Physics #Protein Structure and Dynamics #Statistical physics #Systems biology #cond-mat.dis-nn #cond-mat.stat-mech #q-bio.MN
paper · pdf · doi:10.1073/pnas.0813229106
9 pages, to appear in PNAS, see http://www.pnas.org/content/early/2009/02/05/0813229106.abstract for the early edition
arxiv created 2009/02/06 · openalex publication_date 2009/02/07 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
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, approximately 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.