Gene Network Modeling of Cancer Metabolism

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Metabolism plays a crucial role in cellular behaviors and activities. The abnormal metabolism has been proposed to be one of the hallmarks of cancer. Unlike normal cells, cancer cells largely depend on glycolysis to produce energy even in the presence of oxygen, which is referred as the Warburg effect. Recent evidences, however, suggest that oxidative phosphorylation is also required for cancer progression. Yet, the underlying regulatory mechanism of these metabolic modes in cancer cells is still poorly understood. Here we use the computational systems biology approach to establish a theoretical framework for modeling genetic regulation of cancer metabolism. According to experimental evidences, we built a network of both regulatory proteins and metabolites. The network was first coarse-grained to a three-component regulatory circuit composed of HIF-1, AMPK and ROS. Thereafter, we further explored the interplay between the circuit and the metabolic pathways, including glucose oxidation, glycolysis and fatty acid oxidation. By exploring the dynamics of the metabolic circuits, we show that, while normal cells have two stable steady states – an oxidative state (O: low HIF-1, high AMPK) and a Warburg state (W: high HIF-1, low AMPK), cancer cells open an additional hybrid state (W/O: high HIF-1, high AMPK) due to higher mitochondrial ROS production and lower HIF-1 degradation rate. The ‘W/O’ hybrid phenotype contributes to cancer metabolic heterogeneity and plasticity, thus allowing cancer cells to adapt to the changes in tumor microenvironment and to promote cell proliferation and metastasis. Based on the model, we investigated the effectiveness of possible cancer therapies targeting metabolism in reducing the metabolic plasticity and circumventing the hybrid state during the course of treatment. We also discuss the connection of the metabolic hybrid state to EMT and stemness of cancer cells.

Doctor of Philosophy
Cancer, Metabolism, Gene Network, Hybrid State

Yu, Linglin. "Gene Network Modeling of Cancer Metabolism." (2016) Diss., Rice University.

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