COMPUTATIONAL ANALYSIS OF THE STAT TRANSCRIPTION FACTOR

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DESCRIPTION (as provided by applicant): The National Resource for Cell Analysis and Modeling is housed within, and is the principal venture of, the Center for Biomedical Imaging Technology (CBIT) at the University of Connecticut Health Center. The Resource contains state of the art facilities for studying living cells and has developed a new technology, the Virtual Cell, for analyzing and synthesizing this knowledge. The Virtual Cell is a general framework for modeling cell biological processes. It approaches the problem by associating biochemical and electrophysiological data describing individual reactions with experimental microscopic image data describing their cellular locations. Individual processes are integrated within a physical and computational infrastructure that will accommodate any molecular mechanism. That this computational technology is being developed within a research center fully equipped for microscopic studies of living cells assures that experiment and theory drive each other synergistically. The research plan for the technology development is divided into two projects centered on theory and three that are primarily experimental. Computational Infrastructure addresses the issues of layering the cell biological models on a transparent physical and mathematical foundation, implementing the numerical methods in a distributed computing environment; and refining the remotely accessible user interface and associated database of images and reaction mechanisms. Mathematical and Physical Analysis of Cell Biological Processes has 3 goals: formulation of generalized mathematical descriptions of the elementary physical processes underlying the myriad of cell biological mechanisms; development and validation of efficient numerical methods; and development of rigorous methods for building and refining models from experimental data. Calcium Dynamics aims to elucidate intracellular calcium signaling in cells via a systematic marriage of experiment and modeling approaches;