Mathematic modeling of drug combination effects using protein-protein interaction sub-networks

Maomao Cai, Hong Zhao, Xiaobo Zhou, Yuan Wang, Stephen T. Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Drug combination therapy has been commonly used in the treatment of cancer, hypertension and infectious diseases. Drug combination studies are highly valuable to preclinical 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.

Original languageEnglish (US)
Title of host publicationProceedings of the IASTED International Conference on Modelling, Simulation, and Identification, MSI 2009
StatePublished - Dec 1 2009
EventIASTED International Conference on Modelling, Simulation, and Identification, MSI 2009 - Beijing, China
Duration: Oct 12 2009Oct 14 2009

Other

OtherIASTED International Conference on Modelling, Simulation, and Identification, MSI 2009
CountryChina
CityBeijing
Period10/12/0910/14/09

Keywords

  • Breast cancer
  • Drug combination
  • Fold change
  • Microarray
  • Protein-protein interaction network

ASJC Scopus subject areas

  • Applied Mathematics
  • Logic
  • Modeling and Simulation

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