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Institute for Applied Cancer Science

Computational Biology

Computational biology is an evolving multidisciplinary field that utilizes knowledge of biology, computer science, applied mathematics, statistics, biochemistry, chemistry, biophysics, and visualization to develop and apply theoretical methods, mathematical modeling, and computational simulation algorithms to study biological systems.

The IACS computational biology team is composed of scientists with backgrounds in biology, biochemistry, statistics, and computer science. Team members have participated in successful large scale data generation, analysis and software development projects that include TCGA, GDAC, modENCODE, and Bioconductor.

The computational biology team leverages a robust data processing and analytic infrastructure to support the translation of large-scale data into knowledge that links IACS drug discovery efforts to targeted patient populations. The computational biology team helps to identify relevant biomarkers for patient stratification and response to drug.  By projecting these attributes onto the space of available experimental models, these hypotheses undergo validation and further refinement. Expert knowledge in molecular modeling, genomics, biostatistics, bioinformatics, systems biology and other related disciplines is leveraged against large-scale tumor genome characterization and high-performance computational analysis efforts that are ongoing at MD Anderson. The IACS advantage is the seamless integration of the biology and chemistry teams with the computational biology efforts to perform analyses that are impactful, complement experimental efforts and position targeted therapies best.

Jianhua (John) Zhang, Ph.D., leads the IACS computational biology team.

© 2014 The University of Texas MD Anderson Cancer Center