FFATA: CAREER: Models and Algorithms for Comparative Analysis of Biological Networks
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Recent advent of high-throughput technologies for measuring molecular interactions has yielded large collections of biological networks, which enable systematic studies of complex biological organisms. Since biological pathways with critical functions are often conserved across different organisms, comparative analysis of these networks can provide an excellent way of investigating the organization of biological networks, as well as tracking down novel pathways and studying their functions. Intellectual Merit: This project aims to develop a solid mathematical framework for comparative network analysis and devise innovative techniques for comparing genome-scale biological networks. The main research objectives include: (1) develop a multi-state semi-Markov random walk (SMRW) model for probabilistic comparison of large-scale networks; (2) develop efficient algorithms for querying and aligning biological networks; (3) apply the developed algorithms to identify novel biological pathways and investigate their functions and their detailed mechanisms. Broader Impact: The research activities in this project are closely integrated with a comprehensive educational plan, whose overall goal lies in enhancing students? learning experience through the integration of research and education. This goal is translated into a three-part educational plan: (1) develop a concept inventory (CI) for genomic signal processing and computational biology (called the CIGSP); (2) shift a graduate-level course on ?Probabilistic Graphical Models? to a problem-based format; (3) design a new undergraduate course on Probabilistic Models for Network Biology based on proven and emerging pedagogical approaches. The new concept inventory CIGSP will provide a valuable diagnosis/assessment tool for enhancing education in genomic signal processing and computational biology, and it will be used in the PI?s courses, to design measurable educational objectives and evaluate the learning outcomes.