Developing improved methods to infer pathways and complexes is essential for understanding cellular metabolism. Methods are proposed to combine information of two types - microarray gene expression data and genomic sequence data in the format of phylogenetic profiles. Logical analysis of profiles will be extended from phylogenetic profiles to gene expression profiles, and the combined analysis will be applied to both types of data (Aim 1). The combined logical relationships, now based on these two vast sources of genome-wide information, will be more informative, particularly about pathway regulation. First I will apply this novel combined logical approach to the Escherichia coli genome, with the goal of assessing the usefulness of triplet logical relationships in discovering metabolic pathways (Aim2). Next I will use the triplet logical relations to discover new pathways and complexes in the yeast Saccharomyces cerevisiae, a eukaryotic organism (Aim3). Furthermore, I will apply the logic analysis to cancer datasets to illuminate the processes which lead cells to neoplastic transformation (Aim4). ? ?
