Home Schedule Resources Students Semester series Login Ball TETRIC September 15 September 22 October 6 October 27 November 3 November 17	Friday, September 15, 1:30-3:30 p.m.: Computational Biology Computational Biology Overview Talk (1:30-2:00) Dannie Durand After a whirlwind review of high school biology, we will discuss the origins of computational biology and how innovation in biotechnology drives research in this field. Current research areas in computational biology will be surveyed with a focus on challenging computer science problems in theory, systems and AI that arise in computational biology research. Towards a Systematics for Protein Subcellular Location: Computer Vision and Automated Learning (2:00-2:30) Bob Murphy Determination of the functions of all expressed proteins represents one of the major upcoming challenges in computational molecular biology. Since subcellular location plays a crucial role in protein function, the availability of systems that can predict location from sequence or high-throughput systems that determine location experimentally will be essential to the full characterization of expressed proteins. The development of prediction systems is currently hindered by an absence of training data that adequately captures the complexity of protein localization patterns. What is needed is a systematics for the subcellular locations of proteins. Our group is working on the quantitative description of protein localization patterns using numerical features and the use of these features to develop classifiers that can recognize all major subcellular structures in fluorescence microscope images. Such classifiers provide a valuable tool for experiments aimed at determining the subcellular distributions of all expressed proteins. A key conclusion is that, at least in certain cases, these automated approaches are better able to distinguish similar protein localization patterns than human observers. We are also working on systems for inferring protein location from on-line sources (such as full-text journals) that may disagree or be subject to error. Computational Analysis of Vertebrate Genome Evolution (2:30-3:00) Dannie Durand Yeast, a simple, single celled organism, has roughly 6000 genes while humans have 50,000 - 100,000 genes. Yeast can make bread rise. Human beings have invented the atom bomb and painted the Mona Lisa. How did this order-of-magnitude increase in gene number arise and how did it lead to the functional complexity we see in modern vertebrates? Massive gene duplication, ranging from chromosomal segments to the entire genome, is believed to have played a crucial role in early vertebrate evolution. In the past, these processes have been poorly understood, but it is now possible to study these questions due to the recent availability of huge genomic data sets. In this talk, I will describe a computational approach to studying vertebrate genome evolution by exploiting the growing availability of diverse biological data via the World Wide Web, with an emphasis on the computer science research questions that arise in this work.