Gene Network Modeling of Cancer Metabolism

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dc.contributor.advisorMa, Jianpengen_US
dc.creatorYu, Linglinen_US
dc.date.accessioned2017-08-02T18:07:56Zen_US
dc.date.available2017-08-02T18:07:56Zen_US
dc.date.created2016-05en_US
dc.date.issued2016-04-20en_US
dc.date.submittedMay 2016en_US
dc.date.updated2017-08-02T18:07:56Zen_US
dc.description.abstractMetabolism 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.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationYu, Linglin. "Gene Network Modeling of Cancer Metabolism." (2016) Diss., Rice University. <a href="https://hdl.handle.net/1911/96235">https://hdl.handle.net/1911/96235</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/96235en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectCanceren_US
dc.subjectMetabolismen_US
dc.subjectGene Networken_US
dc.subjectHybrid Stateen_US
dc.titleGene Network Modeling of Cancer Metabolismen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentApplied Physicsen_US
thesis.degree.disciplineNatural Sciencesen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.majorApplied Physics/Bioengineeringen_US
thesis.degree.nameDoctor of Philosophyen_US
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