Computational Cancer Biology
Computational Cancer Biology
The challenge of understanding biological systems at the molecular and systems level as well as the integration of 
computational and experimental approaches for bridging basic and clinical cancer research is what motivates our vibrant research group. Our scientific interests and research efforts are in the areas of Computational Cancer Biology and Pharmacogenetics, Computational Genomics and Pharmacology, Translational Bioinformatics and Computational Medicine with the focus on the development and integration of computational and experimental approaches for (a) system-based analysis of evolutionary, genetic, molecular and clinical signatures associated with human disease; (b) modeling of complex phenotypes and prediction of cancer biomarkers; (c) design and discovery of targeted and personalized cancer therapeutics and development of expert systems for personalized medicine; (d) integration of computational biology and translational informatics with chemical biology and chemical genomics in translational cancer research; (e) enabling information-driven biomedical research on the “bench to bedside” path.
Main scientific themes of the research program:
Computational Cancer Biology and Pharmacogenetics
- Integrative analysis of genetic and molecular signatures of human disease at sequence, structure, functional and clinical levels for understanding the molecular basis of cancer and developing new tools for translational research.
- Computational chemical genomics and pharmacogenetics : development computational approaches and tools for the identification, prediction and functional analysis of cancer variants to enable design of personalized cancer medicine targeting specific genomic profiles.
- Pathway-based and network-based approaches for analysis of human disease to identify functionally related gene modules targeted by somatic mutations in cancer.
Translational Bioinformatics and Computational Medicine
- Translational bioinformatics approaches in the genome-wide functional analysis of cancer variants and prediction of cancer biomarkers.
- Computational genomics, proteomics and systems biology approaches for molecular profiling and drug discovery of protein kinases and molecular chaperone inhibitors.
- Targeted polypharmacology of signal transduction networks and pathway-targeted discovery of anti-cancer therapeutics.
- Integration of computational biology and translational informatics within the discovery of personalized anti-cancer cancer agents targeting specific genomic profiles
- Development of knowledge-based personalized medicine decision systems for clinical and translational research.