Licentiatseminarium, Data- och informaitonsteknik, Daniel Brunnsåker

Machine Learning Enabled Functional Discovery in Yeast
Systems Biology

Saccharomyces cerevisiae is a well-studied organism, yet roughly 20 percent of
its proteins remain poorly characterized. Recent studies also seem to indicate
that the pace of functional discovery is slow. Previous work has implied that
the most probable path forward is via not only regular automation but fully
autonomous systems that can automatically guide and perform high-throughput
experimentation.

This thesis explores various concepts to accelerate and perform functional
discovery of gene and protein functions in Saccharomyces cerevisiae. It does so
by combining ideas from artificial intelligence, such as active learning, with highthroughput
analytical techniques like mass-spectrometry. The work performed
as the basis for this thesis also served to aid in the further characterization
of different aspects of yeast systems biology. Specifically, it delved into the
diauxic shift and its regulators through the lens of untargeted metabolomics,
as well as the regulatory patterns behind genome-wide intracellular proteomic
abundances.

We find that it is essential not only to develop tools and techniques for
facilitating high-throughput experimentation, but also to ensure their optimal
utilization of already existing knowledge. It is also of paramount importance
to ensure a holistic and encompassing view of systems biology by more fully
integrating and using different levels of cellular organization and analytical
techniques.

Opponent: Associate Professor Duygu Dikicioglu, UCL IRIS, Storbritannien