engleski

Probabilistic Metabolic Control Analysis

Modern development of biotechnological processes relies upon quantitative and robust approaches to metabolic flux analysis (FBA) and metabolic control analysis (MCA). The need is driven by market need high-value industrial products (pharmaceuticals) based on genetically engineered both prokaryiotic and eukaryiotic organisms and foreseen market demand for bio-fuels. Metabolic networks are highly intricate complex and redundant control systems which usually resist simple engineering interventions. Application of robust tools for analysis may help to elucidate controlled responses of metabolic networks, thereby helping to guide more effective genetic engineering. In view of stochastic nature of cellular processes and uncertainties of available metabolic data from different sources, proposed is on a systems level a statistical approach to metabolic flux control analysis. Considered are responses of metabolic models as general dynamical systems in high dimensional parameter space. The kinetic parameters are extracellular fluxes are viewed as random variables with assumed probability density functions distribution with estimated value from experimental data as expected value. Probabilistic properties of the calculated fluxes are determined by joint probability distributions accounting for all error sources (kinetic parameters) by which are evaluated distribution first and second moments. The probabilistic flux coefficients are evaluated as relative variances of the conditional expected values of a flux for a given error source term. Individual parameter contributions to the total variance are numerically determined by extensive Monte-Carlo simulation and compared to FAST method in which each parameter is associated with a corresponding frequency and application of spectral analysis. Presented are results for transient control of E. coli central metabolism and a whole cell model of S. cerevisiae. For E. coli the results reveal a very strong shift in PTS control for several orders of magnitude of the metabolic control of glucose flux, and complex concerted control for the whole cell model of S. cerevisiae metabolism

metabic flux analysis; probability; biotechnology

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