CIF:Medium:Collaborative Research: Integrating and Mining Bio-Data from Multiple Sources in Biological Networks

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Networks are a natural, powerful, and versatile tool representing the structure of complex systems and have been widely used in many disciplines, ranging from sociology to physics to biology. Massive amounts of biological data from multiple sources await interpretation. This calls for formal information integration, modeling and mining methods. The functioning of complex biological systems demands the intricate coordination of various cellular processes and their participating components. As biological networks grow in size and complexity, the model of a biological network must become more rigorous to keep track of all the components and their interactions, and in general this presents the need for new methods and technologies in information acquisition, transmission and processing. This research develops a set of novel methods and algorithms of integrating, analyzing and mining biological networks that include multiple sources of biomolecular information such as gene and protein expressions, interactions, and regulations, the formation and dissolution of protein complexes, and interactions between proteins and small molecules. Quantitative and qualitative bio-data from multiple sources are iteratively integrated, mined and organized to generate scalable hypothetical biomolecular network structures. The dynamics of these computational hypotheses are tested and refined through dynamic simulation and laboratory experiments. The investigators will develop an integrated modeling and mining method for the biomolecular networks governing cellular processes, with natural connections to both the biological knowledge base and the design and analysis of biological experiments. We will also plan to investigate novel graph-based theoretical models and algorithms for biological data represented as networks or graphs.