Computational Biology and Translational Bioinformatics in Cancer Research: From Understanding Genetic and Molecular Signatures of Human Disease to Personalized Medicine
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 areasof 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 informaticswith 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:
· Integrative analysis of genetic and molecular signatures of human diseaseat 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 diseaseto identify functionally related gene modules targeted by somatic mutations in cancer.
· Translational bioinformatics approachesin the genome-wide functionalanalysis of cancer variants and prediction of cancer biomarkers.
· Computational genomics, proteomics and systems biology approachesfor molecular profiling and drug discovery of protein kinases and molecular chaperone inhibitors.
· Targeted polypharmacology of signal transduction networksand pathway-targeted discovery of anti-cancer therapeutics.
· Integration of computational biology and translational informaticswith chemical biology and chemical genomics in the discovery of personalized anti-cancer cancer agents targeting specific genomic profiles
· Development of knowledge-based personalized medicine decision systemsfor clinical and translational research.