(65h) Using Biological Heterogeneity to Understand Disease: From Single Cells to Personalized Medicine

Heterogeneity is emerging as a defining feature of biology. This biological heterogeneity can be seen at multiple characteristic scales – from individual patients to single cells – and at multiple regulatory levels – genomic, transcriptomic, proteomic, and metabolomic. Originally perceived as “biological noise”, this heterogeneity is now being leveraged to understand fundamental principles of biology and design more complete and effective therapies to treat diseases.

My work uses computational methods to use biological heterogeneity at multiple scales to understand disease progression, understand dynamic regulation of disease processes, and predict more effective disease therapies and personalized therapies. Specifically, I use a combination of “big data” analyses from individual patients, single-cell RNA-seq and proteomic analyses, and personalized genome-scale metabolic modeling to understand dynamic metabolic regulation in and predict therapies for multiple diseases including non-alcoholic fatty liver disease and breast cancer in individual patients and in patient populations.