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PhD Proposal: Omics based metabolic modeling of cancer
Rotem Katzir
Tuesday, August 27, 2019, 2:00-4:00 pm Calendar
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Cancer is a complex disease that involves multiple types of biological interactions. This complexity presents substantial challenges for the characterization of cancer biology, and motivates the study of cancer in the context of molecular, cellular, and physiological systems. One such approach is constraints-based modeling (CBM), which models the cell as a network of metabolic reactions controlled by hundreds of genes and enables the prediction of feasible metabolic behaviors under different genetic and environmental conditions. CBM was previously shown to successfully predict various metabolic phenotypes in micro-organisms and recently also in human. With this in mind, we set out to build systematic computational pipelines that integrate cancer omics measurements with genome scale metabolic models.

First, we set out to study a genetic interaction known as synthetic dosage lethality (SDL) and its’ relation to cancer. SDL denotes a genetic interaction between two genes whereby the under-expression of gene A combined with the over-expression of gene B is lethal to the cell. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly. We introduce a constraint based modeling approach called IDLE (Identifying Dosage Lethality Effects) that quantitatively predicts human SDLs in metabolism. The emerging network of SDLs is highly predictive of tumor growth and cancer patient survival.

Second, we set out to chart the different layers of metabolic regulation in breast cancer cells, predicting which enzymes and pathways are regulated at which level. To this end, we measured transcriptomic, proteomic, phospho-proteomic and fluxomics data in a breast cancer cell-line (MCF7) across different growth conditions. Integrating these multiomics data within a genome scale human metabolic model in combination with machine learning enabled us to uncover a tiered hierarchical organization of breast cancer cell metabolism. This approach lays a conceptual and computational basis for mapping metabolic regulation in additional cancers.

Our future work involves the study of metabolic interactions between tumor cells and their microenvironment. We shall focus on the arginine pathway, which plays a pivotal role in cellular physiology and involved in numerous important cancer metabolic and signaling pathways.

Examining Committee: 
                          Chair:               Dr. Eytan Ruppin
                          Dept rep:         Dr.  James Reggia
                          Members:        Dr. Hector Corrada Bravo
                                                    Dr. Max Leiserson
This talk is organized by Tom Hurst