Olga G. Troyanskaya

The new era of high-throughput experimental methods in molecular biology has created exciting challenges for computer science to develop novel algorithms for complex, accurate, and consistent interpretation of diverse biological information. In the next decades, large-scale explorations of complex molecular, cellular, and organismic systems at complementary levels of resolution will allow us to integrate our understanding of macroscopic physiology and microscopic biology. To realize the full potential of these developments, we need to develop sophisticated bioinformatics frameworks to integrate and synthesize diverse biological data produced by these methods.

The goal of the research in my laboratory is to bring the capabilities of computer science and statistics to the study of gene function and regulation in the biological networks through integrated analysis of biological data from diverse data sources--both existing and yet to come (e.g. from diverse gene expression data sets and proteomic studies). We are designing systematic and accurate computational and statistical algorithms for biological signal detection in high-throughput data sets. More specifically, our lab is interested in developing methods for better gene expression data processing and algorithms for integrated analysis of biological data from multiple genomic data sets and different types of data sources (e.g. genomic sequences, gene expression, and proteomics data).

My laboratory combines computational methods with an experimental component in a unified effort to develop comprehensive descriptions of genetic systems of cellular controls, including those whose malfunctioning becomes the basis of genetic disorders, such as cancer, and others whose failure might produce developmental defects in model systems. The experimental component the lab focuses on is S. cerevisiae (baker's yeast).