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