Precise Functional Genomics of Digital Organisms via
Dr. Daniel Weise
Place: 3105 Engineering
Abstract: Functional genomics ascribes functions to genes, usually via genomic mutation studies to determine how the presence or absence of a gene affects phenotype. The realm of evolved digital organisms, with its wealth of data and history, allows us to compute functional genomics in the other direction, from phenotype to genotype. We do this by employing metabolic analysis that assigns the phenotypic effects of each gene in a digital organism. Metabolic analysis is not only many times faster than mutation studies, but is also immune to most of the effects of epistatic interactions that plague mutation studies. In addition, metabolic analysis determines whether a gene affects a given phenotype through metabolic or through regulatory pathways. We show the utility, accuracy, and importance of metabolic analysis by directly comparing its output to prior published results that use mutational studies. We discovered two unexpected benefits of metabolic analysis: first, because it is much more detailed than phenotypic information, it provides deep insights into evolutionary trajectories; second, that it shows that far more complexity has been evolving than has been assumed.
Biography: Daniel Weise is an affiliate faculty member of the Computer
Science and Engineering Department of the