Bioinformatics and Biocomputing - Intersections in the Structure Prediction Domain

computational methods in order to address problems in molecular biology. Many of
these problems are combinatorial or constraint optimization problems. Biocomputing
involves the development of computational methodologies that are inspired by a
biological process such as evolution (e.g. Genetic Algorithms) or RNA computing.
First, the problems of RNA and Protein Structure Prediction are introduced. These
problems are of relevance since the structure of these molecules largely determines
their function in the cell. Traditionally, structure prediction had to be performed in a wet
lab using X-crystallography and NMR methodologies. This process is costly and time
consuming. The structure prediction problem can be decomposed so that it becomes an
energy minimization problem and it is demonstrated how a Genetic Algorithm can be
employed to solve it. Various modifications to the Genetic Algorithm, with which we had
success in the TSP domain are discussed and I will demonstrate how we are now
applying these modifications in the RNA secondary structure prediction domain. Results
from investigating the effect of representation, selection strategies and crossover
operators on the efficiency and effectiveness of the algorithm are presented. While the
driving force behind an optimization algorithm for structure prediction is energy
minimization, the real measure of success is how closely the predicted structure
resembles the real structure. Here, visualization of predicted structures of bio-molecules
can help to establish how well an algorithm can predict a known structure. Visualization
is also important for biologists to conduct their own investigations using the predicted
structures. Time permitting, there will be a brief discussion of one of our current
visualization projects in a 3D immersive environment (Cave) and a live demo of a protein
visualization tool.


Brief Biography of Prof. Dr. Kay C. Wiese

Prof. Dr. Kay C. Wiese has studied Mathematics and Computer Science at the
University of the Saarland in Saarbrücken where he received his M.Sc. in Computer
Science (Dipl.-Inform.) in 1995. In January 1995, he moved to Canada where he worked
in the Distributed and Intelligent Systems Lab at the University of Regina. He received
his PhD in 1999 in the area of machine learning and combinatorial optimization. He has
taught Computer Science at the University of British Columbia and the Technical
University of British Columbia. Currently he is an Assistant Professor in Information
Technology at Simon Fraser University. His main research interest is the application of
optimization and machine learning to problems in bioinformatics.