Funding is sought for a five year mentored training period at Brown University for Dr. Nicola Neretti to transition from physics to independent investigator in computational biology. The candidate has a Physics PhD from Brown University, and has been working in the field of signal processing and machine learning. During the past year he has been part of a collaborative project with Dr. John Sedivy (Department of Molecular and Cell Biology and Biochemistry) which resulted in a published paper about the analysis of gene expression array data to target c-Myc-activated genes with a correlation-based method. He has established other productive collaborations with members of the Center for Computational Molecular Biology (CCMB) at Brown University. The principal mentor will be Dr. Marc Tatar (Department of Ecology and Evolutionary Biology). The secondary mentors will be Dr. Charles Lawrence (Dep. Applied Mathematics) and Dr. John Sedivy. The work plan for the five years is to split the training/research effort evenly between computation and biology. For the training requirements the candidate plans to attend courses and workshops in genetics, biochemistry, molecular biology, bioinformatics, and related fields. Dr. Neretti also plans to complete lab rotations in the laboratory of Dr. Marc Tatar and Dr. John Sedivy, to acquire first hand experience in generating the biological data he will later analyze. The main focus of the research effort will be to use microarray data in time course experiments with a high temporal resolution to elucidate the complex interactions among genes and develop novel analytic techniques in functional genomic. In particular, the candidate proposes to integrate the results of gene clustering/graph analysis (e.g. correlation and tagged correlation based clustering) obtained from the time course data with the information available in genetic/pathway databases relevant to the process of senescence. This will allow the evaluation of given hypotheses about functional relationships among genes and the identification of novel dependencies, which can then be directly tested via experiments in model systems of aging. By using this approach the candidate proposes to address key questions in aging research such as what transcriptional changes are under the control of a nutrient sensing system, the temporal and hierarchical relationship of these changes, the magnitude of change that is biologically relevant, and whether genes within a functional metabolic network are co-regulated at the transcriptional level.
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