Time series of gene expression of gene, protein and metabolite concentrations are becoming available as the result of the rapid development of novel, high-throughput experimental techniques in genomics sciences. Such time series implicitly contain valuable information about the connectivity and regulatory structure of the underlying genetic or biochemical network mechanism. The extraction of this information is a challenging task because it requires the development of new mathematical and computational methods of nonlinear estimation that involve iterative search algorithms. Priming these algorithms with high-quality initial guesses can great accelerate the search process.
Even when full genomic sequences for an organism are available, the functions and interactions of only a small number of gene components are clear. Presently, the functions of uncharacterized proteins have usually been inferred on the basis of sequence similarities, common structural motifs, gene order, gene fusion events, or similarities in gene expression. The proposal goal is to develop a new method for functional predictions based on the role of the gene in networks. This method allow us to perform functional predictions for proteins independent of homologies in structure or sequence, and provide a way to characterize proteins that have not yet been studied using published biological data from high-throughput technologies.
The methodology will be applicable to any organism, including humans, where only three to five percent of gene function is known. As we better understand the functions of genes and proteins in a network context, we can better predict and control their responses to internal and external perturbations. For the foreseeable future, the type of modeling predictions will likely be one of the many inputs into the decision making process in the pharmaceutical industry, and biomedical sciences. The research will provide interdisciplinary (biological, mathematical and computational; experimental and theoretical) training to undergraduate and graduate students and postdoctoral researchers, and help produce a generation of scientists comfortable both with biology, mathematics and computation.
