Almost all cancer cells exhibit a metabolic phenotype characterized by high rates of glucose uptake known as the Warburg effect. Metabolic changes that are representative of distinct stages of breast cancer may suggest dependence by cancer cells on certain metabolic processes that are not relevant to normal cells. Importantly, these differences may help identify therapeutic targets that are non-lethal to normal cells. In this thesis, I present a set of models and methodologies developed using both experimental and theoretical approaches to investigate the complex intracellular signaling and metabolic networks associated with distinct stages of breast cancer. First, a detailed literature search was used to construct a logic network model of the PI3K signaling pathway, which is known to play an important regulatory role in glucose metabolism. Comparisons of experimental and simulated results suggest that the network model is well constructed but some regulatory crosstalk with MAPK requires additional refinement. Targeted therapeutic inhibitors frequently induce off-target effects. This thesis also explored the role of retroactivity in generating off-target effects in signaling networks using a computational model. The simulation results suggest that the kinetics governing covalently modified cycles in a cascade are more important for propagating an upstream off-target effect via retroactivity than the binding affinity of a drug to a targeted protein, which is a commonly optimized property in drug development. Finally, 13C tracer experiments were used to infer relative glucose and glutamine derived flux in cell lines representing distinct stages of breast cancer. Steady state metabolic flux analysis was also used to computationally fit the absolute TCA cycle flux in these cell lines. Variations in acetyl-CoA, citrate, pyruvate, and alpha-ketoglutarate flux were identified. A particularly important finding was a large reductive carboxylation flux from alpha-ketoglutarate to citrate in SUM-149 cells. Together, the models presented in this thesis provide a framework for identifying mechanistic drivers of the Warburg effect, which may represent important therapeutic targets for modulating cancer proliferation and progression.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/98062/1/mlwynn_1.pd
