Our broad research goal is to help provide the computational methods necessary to achieve a complete, quantitative understanding of how cells function at the molecular level. Such an understanding will require three things: a "parts list", or catalogue of all cellular molecules; a "wiring diagram" that specifies the interactions that occur between those molecules; and, finally, quantitative models of systems of interacting molecules. The advent of large-scale genome sequencing is bringing the possibility of completing the parts list within view, although substantial work remains to be done. Most current research in molecular biology is directed at filling the wiring diagram (which may be taken as specifying molecular "function"). The modeling of molecular systems, still in its infancy, will become increasingly important as the wiring diagram approaches completion and our ability to accurately quantitate cellular molecules improve.

Most of our research has been directed at constructing computational tools to support the acquisition of the parts list, in the form of a gene-annotated genome sequence. Common themes in this work include the development of appropriate probabilistic models for the type of data to be analyzed, the construction of efficient algorithms to carry out the probabilistic calculations, and the implementation of the algorithms in software which is then made widely available to the scientific user community. Probabilistic methods in particular have proven to be crucial in all of these areas, a reflection of the inherently probabilistic nature of such biological processes as meiotic recombination and sequence evolution, as well as of laboratory data.