MATHEMATIC MODELING OF DRUG COMBINATION EFFECTS USING PROTEIN-PROTEIN INTERACTION SUB-NETWORKS

摘要

Drug combination therapy has been commonly used in the treatment of cancer, hypertension and infectious diseases. Drug combination studies are highly valuable to pre-clinical pharmaceutical research. In this work, we propose a new mathematic modeling approach to predict the effects of the mixture of two breast cancer drugs based on the analysis of the protein-protein interaction (PPI) subnetworks obtained from the two individual drug treated microarray data. We choose the common sub-networks to both drugs, Doxorubicin (Dox) and Fluorouracil (Flu), as our modeling objects. To solve the quantitative problem, we establish a hypothesis that the fold changes of genes are related to the cell percentage Inhibition. This hypothesis is verified from the view of the sets of single gene and then applied to quantify the sub-networks in our model. Biological experiments using cell-based analysis are designed to validate the proposed model.