Data Driven Discovery and Refinement of Genetic Signaling Pathways and Regulatory Networks
Personnel
Dr. M. Heather Greenlee, Assistant Professor of Biomedical Sciences, Principal Investigator
Dr. Jan Buss, Professor of Biophysics, Biochemistry, and Molecular Biology, Co-Principal Investigator
Dr. Vasant Honavar, Professor of Computer Science and of Bioinformatics and Computational Biology, Co-Principal Investigator
Summary
Retinal dystrophies (diseases that involve degeneration of photoreceptors in the retina) are a major cause of blindness. Age related macular degeneration (AMD), which results in degeneration of cone photoreceptors dense macula, affects 10 million people in the United States alone. AMD is an extremely debilitating disease, which robs affected individuals of their high acuity vision. Our research is aimed at understanding the mechanisms that control retinal development and differentiation. Such understanding can lead to the development of therapies involving rehabilitation of retinal cells that can be rescued from degeneration by the application of exogenous survival factors and transplantation of retinal progenitor/stem cells into the degenerate retina.
The widely accepted model of retinal progenitor differentiation asserts that retinal progenitors pass through successive intrinsic “competency states” in which they are capable of responding to extrinsic cues. The extrinsic signal can encompass a whole host of soluble and cell-cell mediated factors. Intrinsic competence to respond to such external factors involves expression of a number of transcription factors as well as expression and plasma membrane localization of appropriate cell surface receptors that make it possible for the progenitors to respond to these cues from the extra-cellular environment. Because the ability of a progenitor cell to respond to cues from its environment is determined to a large extent by the proteins present in its proteome, we are investigating changes in expression among plasma membrane proteins of the retinal progenitor cells from several different ages. This provides us with data on the changing levels of protein expression in the developing retina which can be used to discover coordinated patterns of gene expression changes.
Specific computational aims of this research include development and applications of novel computational approaches to: d ata-driven inference genetic regulatory networks, and signaling pathways including:
- Discovery of co-expressed or co-regulated genes from gene expression patterns
- Construction and data-driven refinement of genetic networks from gene expression data
- Modeling and analysis of changes in gene expression patterns and the underlying genetic regulatory networks and signaling pathways that control retinal development and differentiation
This work builds on our recent results on analysis of gene expression in chloroplast biogenesis in maize (Lonosky et al, 2004) and inference of temporal boolean network models of genetic networks from gene expression data (Silvescu and Honavar, 2001).
Representative Publications
- Lonosky, P., Zhang, X., Honavar, V., Dobbs, D., Fu, A., and Rodermel, S. (2004) A Proteomic Analysis of Maize Chloroplast Biogenesis. Plant Physiology Vol. 134, pp. 560-574, 2004.
- Silvescu, A. and Honavar, V. (2001). Temporal Boolean Network Models of Genetic Networks and Their Inference from Gene Expression Time Series. In: Proceedings of the Atlantic Symposium on Computational Biology, Genome Information Systems & Technology.
