TAILIEUCHUNG - Báo cáo Y học: Prediction of temporal gene expression Metabolic optimization by re-distribution of enzyme activities

Acomputational approach is used to analyse temporal gene expression in the context of metabolic regulation. It is based on the assumption that cells developed optimal adaptation strategies to changing environmental conditions. Time-dependent enzyme profiles are calculatedwhichoptimize the function of a metabolic pathway under the constraint of limited total linearmodel pathways it is shown thatwave-like enzyme profiles are optimal for a rapid substrate turnover. | Eur. J. Biochem. 269 5406-5413 2002 FEBS 2002 doi Prediction of temporal gene expression Metabolic optimization by re-distribution of enzyme activities Edda Klipp1 Reinhart Heinrich2 and Hermann-Georg Holzhiitter3 1 Max-Planck-Institute of Molecular Genetics Berlin Germany 2Humboldt University Berlin Institute of Biology Theoretical Biophysics Berlin Germany 3Humboldt University Berlin Medical Faculty Charite Institute of Biochemistry Berlin Germany A computational approach is used to analyse temporal gene expression in the context of metabolic regulation. It is based on the assumption that cells developed optimal adaptation strategies to changing environmental conditions. Timedependent enzyme profiles are calculated which optimize the function of a metabolic pathway under the constraint of limited total enzyme amount. For linear model pathways it is shown that wave-like enzyme profiles are optimal for a rapid substrate turnover. For the central metabolism of yeast cells enzyme profiles are calculated which ensure long-term homeostasis of key metabolites under conditions of a diauxic shift. These enzyme profiles are in close correlation with observed gene expression data. Our results demonstrate that optimality principles help to rationalize observed gene expression profiles. Keywords evolutionary optimization mathematical modelling metabolic regulation gene expression. Microarray technologies provide the means to measure simultaneously the expression patterns of thousands of genes 1 2 . These expression data and the availability of more than 80 fully sequenced genomes represent an enormous quantity of experimental data. The conversion of this genomic information into knowledge on phenotype characteristics such as metabolic pathways or signal transduction networks is a challenging task that cannot be effectively tackled without broad application of theoretical and computational methods. Time resolved tracing of expression levels for

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