Genomics and computational biology are now at the center of biomedical research. These disciplines take a holistic approach to ask about the origins, functions, and interactions of whole systems, using both experimental and theoretical work. Therefore, these studies require knowledge, skills, and, most importantly, synthesis and integration of biology, computer science, mathematics, statistics, and engineering.

This synthesis and integration requires a new generation of scientists that thrives in cross-disciplinary research. This can include molecular, cellular, and organismal biology (including genetics), mathematics, statistics, chemistry, and engineering. The goal of the GCB program is to train students that are experts in one or more of these disciplines and well versed in the others. We provide a comprehensive training program in Genomics and Computational Biology that gives PhD and MD/PhD students a broad foundation in the biological and quantitative sciences along with practical experience in computational and experimental genomics. The knowledge gained in this program will serve students in their careers as technology progresses.

As examples of areas of study one might explore:

Complex disease mapping, human genetic variation, and evolution of populations
Genome-scale functional analysis of RNA, protein, and pathways
Biophysical models of molecules and their interactions
Design of novel experimental systems using microfluidics, nano-fabrication, and optics
Development of computational algorithms for genome-scale data
Construction of computational pipelines and software engineering of analysis systems
System-level modeling of cell processes and organismal development
Evolutionary dynamics of genomes, transcriptomes, pathways, and organisms
Together, genomics and computational biology represent some of the most important new developments in science, and highlight the power of cross disciplinary science.

Source: https://www.med.upenn.edu/gcb/program.shtml

University of Pennsylvania | MD | PHD: Genomics and Computational Biology

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