Molecular and Cellular Biology (MCB)

MCB's Academic Plan highlights computational technology as a technique that has led to major paradigm shifts in the area of biochemistry. As such, it is an area the Section considers essential to its program, and one that will interface with current strengths in biochemistry and with the Center for Functional Genomics and Bioinformatics. The MCB Section anticipates retirements of the majority of its faculty who currently teach in the biochemistry curriculum, and anticipates a need for, and the opportunity to conduct, at least two recruitments in the area of bioinformatics/computational structural biology. Candidates will research evolutionary (i.e., phylogenetic), functional, and structural relationships amongst members of a particular protein superfamily, utilizing the vast amount of sequence information available through the various genome projects. Technical approaches will include mathematical, statistical, and computational biological methodologies (e.g., the development of novel methodological approaches for mining of multiple databases) as well as approaches using biochemical/molecular biology for experimental assessment of functional implications of theoretical predictions. This research will ultimately encompass the newly emerging discipline of ``systems biology'' (i.e. the integration of protein sequence, three-dimensional structure and relevant biological data to understand the interplay of groups of genes in biological processes). Two recruitments are proposed over the next three years. One candidate is expected to take a leadership role (possibly a senior hire) in campus-wide efforts to strengthen the infrastructure and intellectual developments in the areas of computational biology, genomics, and bioinformatics. A second candidate in the area of computational structural biology at a more junior level is also proposed. This individual will develop tools/approaches for protein structure prediction, protein folding, protein evolution, and/or protein-protein and protein-ligand interactions of relevance to biology, medicine, and/or agriculture. These candidates are expected to utilize any number of computational approaches, including, but not limited to, molecular simulations, genetic algorithms, and/or neural networks.