Computation In Biology

G22.3033-009




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During the last decade we have been witnessing an explosion of new advances in the so called Life Sciences, a large collection of interrelated disciplines including Molecular Biology, Evolutionary Biology, Genomics, Pharmacology, and others. What originally led to this explosion was the maturation of a number of experimental methods (developed in the late 70's and early 80's)  that allowed researchers to examine molecular processes and molecular actors at a level of detail and at a rate previously unattainable. Today new data are produced at ever increasing rates and their study creates new questions that in turn refuel the research process. It is exactly that massive quantity of data that Computer Science is called upon to organize, handle and analyze. This course will discuss techniques for doing so. In particular, the focus will be on the last of the three aforementioned tasks, namely the "analysis" of the data.

In order to understand the subject some elementary biology will be needed: we will provide a short biology primer explaining DNA, genes and the genome structure, the role and the importance of proteins in the cell, concepts like chemical pathways etc. We will also discuss a number of biotechnologies employed in a variety of tasks including deciphering the composition of  DNA molecules (a process called "sequencing") and measuring expression levels of proteins in the cell (using devices known as "gene chips"). The presentation will be kept at a high level. No prior knowledge of biology or chemistry will be required.

As soon as the biological foundation is laid, our attention will shift to a number of algorithmic topics. In addition, we will tour the major biological data repositories on the Web as well as sites containing publicly available tools for solving some of the algorithmic problems that will be discussed in the class. At the end of this course, we expect every student that decides to pursue research in the field of Computational Molecular Biology to:

  • have a good understanding of what are the main problems worth looking at,
  • know where to get the information required for their chosen line of research.