YOU. The class is open to biologists and computer scientists, applied mathematicians, undergrads and grad students alike. The assignments will be adapted to your skillsets and interests.
Tuesdays and Thursdays, 2:30pm. Spring 2015 semester.
CIT 241 The Swig Boardroom nestled into the far corner of CIT's 2nd floor. The whiteboard and presentation amenities are superb, and you can enjoy guest speakers and refreshments on most Wednesdays.
What? Computational Biology?!?
Computation and biology is a fundamental pairing and most biological research today has a computational component. To put it concisely, computation is a structure that applies mathematical method and logic to problems. Computation benefits from rigorous proof and the capacity to solve complex problems through iteration and/or recursion.
Biology is perhaps the most suitable science for computational understanding since it consists of many discrete (they either happen or they don't) events occurring in rapid time. Biology needs good statistical approaches and inference methods for discovery and verification of its findings.
Why Computational Biology?
This one requires a small history lesson and a summary of the field today. Computational Biology takes on very practical dilemmas such as interpreting sequences, modeling molecules, and making statistical prediction. It complements the vast amount of biological data being produced today with a means to make sense of it all. The sequencing of the human genome has given Computational Biology new relevance over the past decade: all of biology is presumed to be explainable from the genetic code.
If you are a computer scientist, you get to code sequence alignment and genome assembly programs just like the biology gods — and you'll be solving deep math problems that are very rewarding. If you are a biologist in the class, you will get an appreciation of algorithms (the beautiful, fundamental unit of computer science). And you will be able to bring back new tools to the laboratory. The course focuses on these topics: probability (aka "how to lie with statistics"), sequence analysis, achievements in molecular biology, Hidden Markov Models, the assembly of the human genome, and the study of evolution and mutation.