SEMINAR SERIES

One of the hallmarks of cancer is the dysregulation of cancer metabolism. Although identified in the 1920’s, this feature has yet to be exploited widely for designing chemo-therapies. One reason for this is a lack of a systematic method to identify metabolic targets, predict their efficacy, and predict their toxicity to non-tumor tissues. In this talk, I will present our recent work developing a sys-tematic approach to identify metabolic chemotherapies. We have created a modeling framework based on reaction engi-neering principles to identify metabolic therapies that can complement standard treatments for cancer. Our approach uses enzyme-constrained, genome-scale metabolic models (ec-GEMs) integrated with experimental RNA-sequencing data to simulate metabolism in individual patients. I will show how we have applied this framework to simulate breast cancer metabolism in over 1,000 unique patients using publicly available RNA-seq data from The Cancer Genome Atlas, how these simulations predict metabolic flux profiles and cancer growth rates effectively, and how we use standard and novel model analyses based on engineer-ing principles to design metabolic chemotherapies.

Friday, September 20, 2019155 Goodwin Hall1:25pm - 2:15pm

Dr. Daniel J. CookChalmers Institute of Technology | Gothenburg, Sweden

Predicting Metabolic Targets for Cancer Therapy by IntegratingPatient Data with Genome-Scale Metabolic Models