Conceptual Data Integration for the VirtualPlant

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New York University is granted an award to develop new informatics, visualization, math and statistic tools to enable dynamic modeling and visualization of molecular networks in cells. These tools will be integrated in a common platform so as to understand how internal and external perturbations affect processes, pathways & networks controlling plant growth and development. Succeeding in this endeavor will allow researchers (1) to explain mechanistically how molecular network changes occur to evoke responses and (2) to predict molecular network states under untested conditions or after modifications. Predictive models are useful for developing strategies that intervene in molecular networks for practical applications. The project has four major goals: (1) Integration: to integrate known relationships among genes, proteins and molecules (extracted from public databases, the published literature and generated with predictive algorithms) with experimental measurements (e.g. mRNA measurements). (2) Visualization: to develop novel visualization techniques that will render the multivariate information in visual formats that facilitate extraction of biological concepts. (3) Synthesis: to integrate mathematical and statistical methods with the novel visualization techniques to help summarize the data and test hypothesis. (4) Predictions: to develop algorithms to make predictions of the molecular network state under untested conditions. These approaches will be combined in a system called "VirtualPlant" which may become an essential toolbox for Systems Biological analysis of genomic data and provide a framework for the analysis of future high-throughput data.