Tools for Transformation

Funded Proposals

Computational Biology Classroom

Department of Zoology, College of Arts & Sciences

Biology is undergoing a revolution -- it is becoming a highly quantitative science dealing with exceedingly massive data sets and exquisitely complex systems, from human genomic data to the complexity of nervous systems. The development of tools capable of generating enormous amounts of data, coupled with the plummeting cost of storing, analyzing and dispensing these data, enable biologists to grapple with problems that they would not have dreamed of considering only ten years ago. Now the challenge has moved from the "easy problems" to the ones involving highly sophisticated analytic and technological tools. For example, to establish that DNA is the basis of inheritance required several elegant, yet simple, experiments; in contrast, to understand how genes interact to control our development requires a myriad of computational and analytic tools to keep track of when dozens or hundreds of genes turn each other on or off. From a different angle the genetic basis of evolutionary change is, at first glance, an easy problem. But in biology there are few certainties, and in genetics variability, in particular, is a fundamental part of the evolutionary mechanism. Here exact computation of uncertain pedigrees or constructing genetic models of populations with sparse data prohibit analytic solutions. Instead, we resort to stochastic models to answer complex questions. In both realms, we need to rely on a vast array of analytic and computational tools to solve fundamental problems.

To address this issue, we propose to build up a "Computational Biology Classroom" where we teach modern analytic and computational methods for biological problem solving for undergraduate and graduate students. There are approximately 1600 Biology/Zoology majors within the biological sciences in the college of Arts and Sciences. In our core courses, we provide instruction for these as well as a nearly equivalent number of students who are majors in related disciplines (Forestry, Fisheries, Oceanography, Microbiology, etc.). Additionally, there is a large number of students in more distant disciplines (e.g., Engineering) who require more detailed training in the biological sciences than merely a survey course. All of these students progress through a series of introductory courses, followed by a mid-level series in current planning form, culminating in a new array of senior/graduate capstone research/learning experiences.

In the biological sciences, this high-end educational experience is increasingly dominated by computational technology. It is important to realize (see below) that a graduate in this burgeoning field cannot be successful without a deep understanding of highly quantitative approaches. Conservation biology, ecology, evolutionary biology, cell physiology, neurobiology, genetics, genomics, and modern molecular biology all now require significant computational skills. Both graduate students and undergraduates in our discipline as well as other students now populating our courses have expressed a deep desire to have more powerful computing for their courses and for their research projects. This includes powerful mathematical packages as well as statistical and graphical packages running on machines that can appropriately handle high-end technology relevant to the biological sciences. We all feel that our graduating students will not be competitive unless we provide the real backbone of training in this computational aspect of biology. Indeed, our goal is to have UW graduates be recognized for their unique training in computationally oriented skills in the life sciences from topics including: ecological and evolutionary algorithms , genetic algorithms, pattern analysis, cell and developmental gene network theory, neuronal computation and engineering models of organismic design. This will give them the competitive edge in placement in professional programs, industry, and in graduate programs elsewhere in the nation.

Tools for Transformation Funded Proposals

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Modified: June 19, 2001