Computational Modeling Techniques for Biological Network

The rapid development and applications of high throughput measurement techniques bring the biological sciences into a 'big data' era. The vast available data for enzyme and metabolite concentrations, fluxes, and kinetics under normal or perturbed conditions in biological networks provide unprecedented opportunities to understand the cell functions. On the other hand, it brings new challenges of handling, integrating, and interpreting the large amount of data to acquire novel biological knowledge. In this thesis, we address this problem with a new ordinary differential equation (ODE) model based on the mass-action rate law (MRL) of the biochemical reactions. It describes the detailed biochemical mechanisms of the enzyme reactions, and therefore reflects closely of how the enzymes work in the systems. Because the MRL models are constructed with elementary enzyme reaction steps, it is also much more flexible than the aggregated rate law (ARL) model to incorporate new enzyme interactions and regulations. Two versions of the MRL model ensembles for the central carbon metabolic network, which generates most of the precursors for the secondary metabolite, were constructed. The E. coli version contains the basic reactions in this network and was applied to optimize the aromatic amino acid production which requires fine-tuned flux partition between glycolysis pathway and the pentose phosphate pathway. The S. cerevisiae version is more sophisticated with the incorporated dynamics of the NAD/NADH and NADP/NADPH, as well as the automatic switch from aerobic to anaerobic condition. It was applied to maximize the ethanol production yield, for which the NAD/NADH ratio is a crucial regulating factor. In order to develop methodologies to understand the intrinsic network properties and optimize the network behavior, we further explored approaches for the identification of pathway bottlenecks. Four computational assays were studied, including metabolite accumulation, conditional Vmax, increased glucose input, and decreased Eo, which were applied to the ethanol model ensemble to discover their effectiveness in bottleneck identification in this network. The TDH reaction was detected as a major bottleneck restricting