As Robert Robbins* put it, the DNA sequence of an organism is “the result of literally millions of maintenance revisions performed by the worst possible set of kludge–using, spaghetti–coding, opportunistic hackers (i.e. evolution) who delight in clever tricks like writingself–modifying code and relying upon undocumented system quirks”. As a result, deciphering molecular biology is the ultimate dream of a computer scientist.
My lab focuses on understanding the operation and evolution of one of molecular biology’s most frequently exploited computational tricks: transcriptional regulation. In particular, we are interest on how the interactions of regulatory demands, genomes, transcription factors and their binding sites interact to modulate the evolution of transcriptional networks. We combine comparative genomics, evolutionary simulations and machine learning approaches with high-throughput and curated data on transcription factor-DNA interactions to probe at the genomic organization of transcriptional systems, with the goal of creating enhanced genome analysis tools and models of gene regulation.