Convex analysis of metabolic network for optimal cell design and flux validation by GC-MS or NMR

In this thesis an integrated software tool: MetaboLogic was developed. This software not only integrates most of the techniques developed in Metabolic Flux Analysis (MFA), but also two mathematical techniques for finding multiple optimal solution and designing tracer experiments. MetaboLogic is designed to allow users to construct arbitrary network model visually through a friendly graphic user interface.; The functions of the software can be classified into two groups. The first group is to compute flux distribution by using stoichiometric information. This part includes traditional linear programming techniques and the modified simplex algorithm we developed to find the alternative optimal solutions. The second group of functions deals with the mathematical methods to simulate NMR and GC/MS spectra based on flux distributions. The multiple solutions found using MILP or modified simplex method can be used to better design the tracer experiment in terms of choice of labeled substrates and signal molecules.; We applied MetaboLogic to find a potential metabolic engineering strategy for inhibiting the acid formation. Acid formation is a major problem in production of recombinant protein in both E. coli and B. subtilis, because it limits process stability and cell concentration and thus cell based biotechnological processes. The inactivation of pyruvate kinase (PYK) was identified as one potential metabolic engineering strategy for eliminating acidic by-products. PYK mutants were constructed and characterized in terms of growth and acid formation. The experimental results confirmed that the predicted strategy is an effective way to reduce acid formation. This application is good demonstration of the MetaboLogic 's capabilities.; Finally, the MetaboLogic was used for the design of genetic-based strategies for enhancing folic acid production in E. coli and B. subtilis. The genetic strategy that emerged for reduction of acid formation (pyk mutation) was found to be a very promising start point for increased folic acid production. The experimental data in E. coli confirmed that pyk mutation increased the folic acid production by 5--6 fold compared to the wild type.