Computational analysis of regulatory DNA sequences controlling gene transcription

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All cells and organisms possess a DNA-based set of information that describes the proteins that can be constructed, as well as information for making RNA molecules that perform important roles in the biochemical processes of life. The information present in the DNA sequences is static -cells have the same DNA present without regard to the place, time or conditions in which the cell exists. To respond to the environment, cells selectively turn on or off (or up or down) genes, allowing production of RNA and proteins suitable for the conditions encountered. For instance, yeast producing alcohol from sugar in wine fermentation turn on genes that facilitate this process. The research proposal addresses the interdisciplinary application of computers to the analysis of the regulation of genes, with a specific emphasis on the transition of information from DNA into RNA (the process called transcription). The research program includes three majn areas of activity. First, software is developed to allow for the identification of specific DNA sequences that appear near genes that are turned on in a specific condition. Finding these "on/off switches" is a first step in deciphering how cells respond to changing conditions. This approach builds on artificial intelligence methods for pattern recognition. In the second step, we address non-random properties of DNA that complicate the analysis. In many organisms, genes that are "always on" have different mixtures of A,C,G and T, compared to genes that only turn on when needed. Searching for patterns is complicated by this mixture difference. Solving this seemingly minor problem could greatly improve the interpretation of expensive laboratory-generated data from across the life sciences. Linking of the on/off switch DNA sequences to the proteins that "flip the switch" is the focus of the third aim. We will computationally analyze text from published papers to predict which proteins control the process. The research program impacts research across all of life sciences, from agriculture to bioengineering, and from microbiology to biochemistry.