Modeling cooperative gene regulation using Fast Orthogonal Search
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A number of computational methods have suggested means by which gene transcription – the process through which RNA is created from DNA – is activated, but there are factors at work that no model has been able to fully explain. In eukaryotes, gene regulation is quite complex, so models have primarily focused on a relatively simple species, Saccharomyces cerevisiae (budding yeast). Because of the inherent complexity in higher species, and even in yeast, a method of identifying transcription factor (TF) binding motifs (specific, short DNA sequences) must be efficient and thorough in its analysis. This thesis shows that a method using the Fast Orthogonal Search (FOS) algorithm to uncover binding motifs as well as cooperatively binding groups of motifs can explain variations in gene expression profiles, which reflect the level at which DNA is transcribed into RNA for a number of genes. The algorithm is very fast, exploring a motif list and constructing a final model within seconds to a few minutes. It produces model terms that are consistent with known motifs, while also revealing new motifs and interactions, and it causes impressive reductions in variance with relatively few model terms over the cell-cycle.