Precise Functional Genomics of Digital Organisms via
Metabolic Analysis
Dr. Daniel Weise
University of
Date:
Time:
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