Machine Learning Enabled Functional Discovery in Yeast Systems Biology
Licentiate thesis, 2023

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.

Metabolic Modelling

Mass Spectrometry

Metabolism

Metabolomics

Machine Learning

Systems Biology

Inductive Logic Programming

Proteomics

10:an
Opponent: Prof. Duygu Dikicioglu, University College London, UK.

Author

Daniel Brunnsåker

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

High-throughput metabolomics for the design and validation of a diauxic shift model

NPJ systems biology and applications,;Vol. 9(2023)p. 11-

Journal article

D. Brunnsåker, F. Kronström, I.A. Tiukova, R.D. King. Interpreting protein abundance in Saccharomyces cerevisiae through relational learning.

Subject Categories

Biochemistry and Molecular Biology

Bioinformatics (Computational Biology)

Bioinformatics and Systems Biology

Computer Science

Infrastructure

Chalmers Infrastructure for Mass spectrometry

Areas of Advance

Health Engineering

Life Science Engineering (2010-2018)

Publisher

Chalmers

10:an

Online

Opponent: Prof. Duygu Dikicioglu, University College London, UK.

More information

Latest update

8/30/2023