http://precedings.nature.com/tags/flux%20balance%20analysis Recently posted documents tagged with 'flux balance analysis' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://precedings.nature.com/documents/2788/version/1 Background: Flux balance and network-based pathway analyses are theoretical tools aimed to find optimal steady state flux distributions in a metabolic network subject to additional constraints on the rates of the reaction steps. Although these methods are mathematically accurate, there are several physicochemical and computational aspects that are questionable and misleading. In particular, it is well known that the flux balance analysis may result in multiple flux distributions for the same objective function. Results: The flux balance and network-based pathway analyses are reformulated in terms of reaction routes (RRs), a theoretical framework that has been developed by Horiuti over 50 years ago. Not only does the theory of RRs provide the most general and rigorous definition of a pathway, but it also relates the steady state rates of the reaction steps with the rates along RRs or pathways. In this work, we employ the simple relation between the steady state rates of the reaction steps and the rates along RRs (fluxes) established by Horiuti to eliminate the steady state constraints. Conclusion: The newly proposed RR approach represents a powerful tool for a deeper understanding of optimal flux distributions in metabolic reaction systems. Application of the RR approach to several typical systems from the literature surprisingly reveals that an infinite number of flux distributions for the same optimal objective function may be a rule rather than the exception http://precedings.nature.com/documents/2788/version/1 Thu, 15 Jan 2009 21:48:27 UTC A Reaction Route Approach to Flux Balance Analysis hdl:10101/npre.2009.2788.1 2009-01-15 Ilie Fishtik Nature Precedings 2009-01-15T21:48:27Z Manuscript Biotechnology Chemistry Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/1841/version/2 Background: Every protein has a variable atomic and energetic cost to the cell based on the synthesis of its constituent amino acids. Quantifying the cost of amino acid synthesis is challenging, however natural selection is expected to favour the use of proteins whose constituents are cheaper to produce in terms of energetic and atomic cost.Results: We develop a systems biology approach to estimate the cost of amino acid synthesis based on genome-scale metabolic models, and directly investigate the effects of the cost of amino acid synthesis on transcriptomic, proteomic and metabolomic data in Saccharomyces cerevisiae. We used our two new and six previously reported measures of amino acid cost in conjunction with codon usage bias, tRNA gene number and atomic composition to identify the factors that predict transcript, protein and free amino acid levels in the yeast cell. While most previously reported cost measures are highly correlated, we find that our systems approach to formulating the cost of amino acid synthesis produces a novel measure of cost, which explains similar levels of variation in gene expression. Regardless of the measure used, the cost of amino acid synthesis is weakly associated with transcript and protein levels, independent of codon usage bias. In contrast, energetic costs explain a large proportion of variation in levels of free amino acids.Conclusions: In the economy of the yeast cell, the cost of amino acid synthesis correlates with transcript and protein levels to a lesser degree than translational optimisation, whereas atomic and energetic cost plays a much larger role in explaining levels in free amino acids. However, as there appears to be no single currency to compute the cost of amino acid synthesis, a systems approach is necessary to uncover the full effects of amino acid biosynthetic cost in complex biological systems that vary with cellular and environmental conditions. http://precedings.nature.com/documents/1841/version/2 Tue, 22 Jul 2008 08:38:32 UTC Systems biology of energetic and atomic costs in the yeast transcriptome, proteome, and metabolome hdl:10101/npre.2008.1841.2 2008-07-22 Michael D. Barton Nature Precedings 2008-07-22T08:38:32Z Manuscript Genetics & Genomics Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.1868.1 Stoichiometric and constraint-based analyses of metabolic pathways have been gaining ground in the recent past with the increase in the quality and number of pathway databases available and the curation of genome-scale metabolic models. Genome-scale metabolic models of several organisms such as Escherichia coli, Saccharomyces cerevisiae and Staphylococcus aureus have already been constructed. Flux Balance Analysis (FBA) and Minimisation of Metabolic Adjustment (MoMA) are two of the popular techniques for the constraint-based analysis of metabolic pathways.We have developed a computational tool, PathwayAnalyser, for the analysis of metabolic pathways, particularly by FBA and MoMA. PathwayAnalyser interfaces with the open-source GNU Linear Programming Toolkit (GLPK) for linear programming/FBA and Object Oriented Quadratic Programming (OOQP) for quadratic programming/MoMA. It gives a comprehensive report on gene deletions from the Systems Biology Markup Language (SBML) Model and objective function input for FBA. PathwayAnalyser is open-source and is available at http://sourceforge.net/projects/pathwayanalyser http://dx.doi.org/10.1038/npre.2008.1868.1 Thu, 08 May 2008 11:02:56 UTC PathwayAnalyser: A Systems Biology Tool for Flux Analysis of Metabolic Pathways doi:10.1038/npre.2008.1868.1 2008-05-08 Karthik Raman Nature Precedings 2008-05-08T11:02:56Z Poster Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/1841/version/1 Proteins vary in their cost to the cell and natural selection may favour the use of proteins that are cheaper to produce. We develop a novel approach to estimate the amino acid biosynthetic cost based on genome-scale metabolic models, and directly investigate the effects of biosynthetic cost on transcriptomic, proteomic and metabolomic data in Saccharomyces cerevisiae. We find that our systems approach to formulating biosynthetic cost produces a novel measure that explains similar levels of variation in gene expression compared with previously reported cost measures. Regardless of the measure used, the cost of amino acid synthesis is weakly associated with transcript and protein levels, independent of codon usage bias. In contrast, energetic costs explain a large proportion of variation in levels of free amino acids. In the economy of the yeast cell, there appears to be no single currency to compute the cost of amino acid synthesis, and thus a systems approach is necessary to uncover the full effects of amino acid biosynthetic cost in complex biological systems that vary with cellular and environmental conditions. http://precedings.nature.com/documents/1841/version/1 Tue, 29 Apr 2008 15:33:13 UTC Systems biology of energetic and atomic costs in the yeast transcriptome, proteome, and metabolome hdl:10101/npre.2008.1841.1 2008-04-29 Michael D. Barton Nature Precedings 2008-04-29T15:33:13Z Manuscript Genetics & Genomics Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/3.0/