Foundations of Computational Biology and Bioinformatics II (Course number – BME 580.688/580.488 and CS 600.688/600.488) will be available this spring with Assistant Professor Rachel Karchin of the Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University.

Computational biology / bioinformatics is a fast-moving field at the interface between molecular biology, computer science, applied mathematics, biophysics, computational chemistry, and genetics. While early work in this area dates back to the 1970’s, the field really took off in the 1990’s, as genome sequencing projects and other high-throughput measurement technologies for molecular biology, such as microarrays and mass spectrometry, began to generate a tsunami of data.

These measurement technologies continue to evolve rapidly, yielding new kinds of data that require large-scale computational analysis. Developments in recent years include the increased importance of alternative splicing in eukaryotic genomes, microRNA regulation, the molecular heterogeneity of tumors and SNP haplotypes. New kinds of computational and mathematical modeling are increasingly required to integrate and make sense of discoveries in areas such as these.

Students considering a career in this field will likely see even more dramatic changes in the next 20-30 years. Success will require figuring out how to handle quantities of data much larger than have been seen previously, to analyze it statistically, and develop new algorithms to implement analysis efficiently. We are also seeing a shift away from individual labs working on specialized problems to large, interdisciplinary teams. Being able to communicate and work productively with scientists from diverse backgrounds is more important than ever before.

The goal of FCBBII is to give students a solid foundation in the basics of statistical and algorithmic approaches developed in computational biology over the past 20 years, while emphasizing the need to extend these approaches to emerging problems in the field. Students will also have an opportunity to improve their scientific communication skills and ability to work effectively in a team setting.