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David Shalloway

David Shalloway

Professor

265 Biotechnology
(607) 254-4896

With a PhD in theoretical physics and over twenty years experience in experimental biological research, it is not surprising that my current research is in computational biology, focused on the quantitative analysis of experimental data in biochemistry and cell biology. Similarly, I am particularly interested in the training of both our undergraduate and graduate biology students at the interface between biology, mathematics, physics and computation.

Research Focus

Our research focuses on the role of aberrant splicing of the Receptor Protein Tyrosine Phosphatase alpha (RPTPalpha) proto-oncoprotein in cancer. Our previous experimental studies with a limited number of patients showed that PTPalpha is aberrantly spliced in large fractions of breast and colon cancers and that it can induce cancer by "dominant-positive" activity. We are now computationally investigating much larger numbers of patients and additional tumor types by analyzing high-throughput RNA sequence data from human tumors. In experimental studies, we are investigating the mechanism of alternative splicing and whether known cancer-associated mutations are acting via the dominant positive mechanism.

Additional projects in computational biology include

1) Development of a new statistical method for improved analysis of high-throughput RNAaptamer-target binding data (i.e., HitsRap data). This new experimental technique can facilitate the rapid identification of high-affinity ligands, but requires the new type of analysis that we are developing for maximum advantage. (Collaboration with John Lis, Dept. of MBG).

3) Cell replication and motility in the epidermis and hair follicle. These systems provide models of organ development that can be studied in detail using transgenic mice in which the replication and motility of individual cells can be followed using fluorescent tracers. We use mathematical modeling to infer the underlying biological processes involved. (Collaboration with Doina Tumbar, Dept. of MBG.)

4) Improved methods of pattern recognition in multidimensional large database analysis. Even the identification of patterns in a two-dimensional field (e.g., in vision) is computationally challenging, and the many-dimensional problem posed by large-scale bio-databases is even more difficult. We are developing a novel approach using advanced methods from stochastic statistical physics.

Teaching Focus

BioMG 838: Scientific Communication in Biochemistry, Molecular and Cell Biology- students are coached in preparing a grant proposal, presenting a scientific talk, and writing scientific papers

My focus is on training students in the skill of written and oral scientific communication and on the application of mathematics, statistics and computation to experimental biological problems.

Selected Publications

Journal Publications

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