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B.S.E. University of Michigan, 1989
M.A. Princeton University, 1991
Ph.D. Princeton University, 1995
At the heart of the complexity of multi-cellular life is the proper context-dependent expression genes. To achieve this, cells have evolved a highly interconnected transcriptional network composed of signalling molecules, transcription factors (TFs), and their DNA targets. The mRNA expression level of a gene is typically determined by several input signals, through the cis-regulatory logic encoded in its non-coding regulatory DNA sequences. This cis-regulatory logic is fundamental to many processes, including physiological adaptation, generation of cell diversity, and morphological development.
With the arrival of whole-genome approaches for measuring the expression of genes, there is an emerging movement to learn the structural and dynamical properties of transcriptional networks on a genomic scale. My research focuses on computational and experimental approaches for understanding gene regulation from genomic DNA sequence.
On the computational side, I use microarray gene expression data and whole-genome DNA sequence to systematically infer combinatorial regulatory logic. I use pattern recognition algorithms to identify over-represented and phylogenetically conserved DNA sequence elements (or putative transcription factor binding sites). I then use a probabilistic Bayesian network to find the most likely functional constraints on the position, spacing, orientation, and combinations of these DNA sequence elements. This methodology has generated a large set of high confidence predictions for regulatory interactions, and is in principle applicable to any organism with microarray and genome sequence data. On the experimental side, I'm testing these computational predictions by rapid generation of transgenic GFP reporter strains in C. elegans via microparticle bombardment.
- Combinatorial gene regulation
- Computational molecular biology and genomics
- Bayesian networks and machine learning
- Preceptor-Predoctoral Training Program in Human Genetics
- McGaughey, D.M., Vinton, R.M., Huynh, J., Al-Saif, A., Beer, M., and McCallion, A.S. Metrics of sequence constraint overlook regulatory sequences in an exhaustive analysis at phox2b. Genome Research, 2007. In press.
- M.A. Beer and S. Tavazoie: Predicting Gene Expression from Sequence. Cell 117, p185-198 (2004).
- M. Pritsker, Y.C. Liu, M.A. Beer, and S. Tavazoie: Whole-genome discovery of transcription factor finding sites by network-level conservation. Genome Research 14, p99-108 (2004).
- D.J. Katz, M.A. Beer, J.M. Levorse and S.M. Tilghman: Functional characterization of a novel Ku70/80 pause site at the H19/lgf2 imprinting control region. Mol Cell Biol 25, p3855-3863 (2005).
Michael A. Beer, Ph.D.
Johns Hopkins University School of Medicine
McKusick-Nathans Institute of Genetic Medicine
Edward D. Miller Research Building, Room 573
733 N. Broadway
Baltimore, MD 21205