| Biomass-based chemicals and fuels produced from such metabolic networks are attractive alternatives to fossil fuel products. Technological advancements in molecular biology and genomics have boosted metabolic engineering practices for improving desired chemical formation through the manipulation of targeted enzyme activity.;Despite of its booming applications, metabolic engineering still faces a major challenge of target identification. Considering the size, complexity; and intricate regulatory schemes of a typical metabolic network, it is infeasible to empirically predict the impact of an enzymatic alteration on the entire metabolism. The high cost and time investment required for such operation prevents target identification from experimental scanning of all enzyme modifications at a systems level.;In this thesis, I present our work on developing computational and statistical frameworks to quantify the impacts of individual genetic modifications or environmental changes to the behavior of metabolic networks and evaluate how each component fits into the big picture. The central framework is developed on the basis of the theory of Metabolic Control Analysis and, in particular, it employes principles from the previously established (log)linear MCA formalism. The current work introduces the concept of cellular uncertainty into the study of metabolic networks. The developed framework uses a Monte Carlo sampling procedure to simulate the uncertainty ill the kinetic data and applies statistical tools for the identification of rate-limiting steps in metabolic networks.;In addition, the original framework is extended to incorporate standard bioprocess conditions. The generalized framework integrates central cellular processes with different bioreactor conditions, therefore provides the mathematical basis for the quantification of the interactions between intracellular metabolism and extracellular conditions.;Finally, in order to extend the predictive power of Metabolic Control Analysis from infinitesimal enzymatic perturbations to wide-range modifications, a computational nonlinear MCA framework is developed that allows a systems-level profiling of metabolic responses during large scale overexpression or functional modulation of target enzymes. Based on the initial results, we propose multi-stage metabolic engineering strategies with multiple enzyme modulation that allow the metabolic system to stepwise evolve toward an optimal product formation. |
