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Decoding the Multi-Drug Response in Populations of Bacteria and Human Cancer Cells
Drugs combinations are commonly employed in the treatment of multi-component diseases, severe bacterial infections, and many types of cancer. However, the actions of individual drugs are often coupled through their effects on complex intracellular networks. As a result, it is generally impossible to infer the net effect of a multi-drug combination directly from the effects of individual drugs.
In this talk, I will discuss our recent work that explores how drug interactions accumulate as the number of drugs, N, in a combination increases. To answer this question, we develop a statistical model that associates drug interactions with correlations between random variables, allowing us to exploit methods from statistical physics to measure the contributions of all K-body interactions (K<=N) to a given N-drug effect. Using this framework, we then experimentally show that the effects of three-drug and four-drug combinations are dominated by interactions between pairs of drugs in gram negative (/E. coli/) and gram positive (/S. aureus/) bacteria as well as in multiple types of human cancer cells. Even more surprising, we find that the quantitative relationship governing the accumulation of pair wise drug interactions appears to be independent of microscopic details such as cell type and drug biochemistry.
I will conclude by briefly introducing our ongoing efforts--both theoretical and experimental--to understand and control the emergence of drug resistance in dynamic multi-drug environments.
Interested students are encouraged to contact me, (firstname.lastname@example.org) or visit our web page, (woodlab.biop.lsa.umich.edu) for more details.
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