Frameworks for Automated Discovery in Systems Biology
Licentiatavhandling, 2024

Systems biology is an integrationist approach to biological science, meaning we treat organisms as complex systems whose behaviour is dictated by the interaction of their constituent parts. Because eukaryotic organisms are extremely complex systems, research progress in systems biology can be slow. Recent advances in robotics, and more importantly in artificial intelligence (AI), offer great opportunity for automating scientific discovery in this field.

Using the model organism Saccharomyces cerevisiae, baker’s yeast, this thesis explores: the philosophical and practical motivations for the use of automation in biological research; the structure of knowledge models, experi- mental data, and hypotheses in systems biology; and computational models of metabolism, a core component of systems biology.

The first main contribution of this thesis is a set of ontologies and accompanying database software for enabling an autonomous discovery platform. The second main contribution is a first-order logic framework for modelling cellular physiology, which we call LGEM⁺. Abduction of hypotheses for improvement of knowledge models is enabled by LGEM⁺, which couples a set of predicates and clauses expressing biochemical reaction processes with an efficient automated theorem prover (ATP), iProver.

Results from these studies show automated improvement of knowledge models in systems biology can be achieved using general purpose tools, in this case ATPs, by using a first-order logic formalism faithful to domain ontologies. More work is needed to integrate these techniques with laboratory robotics and inductive reasoning agents, building on the work presented in this thesis, to achieve the goal of autonomous discovery in systems biology.

Machine learning

first-order logic

knowledge modelling



metabolic modelling

systems biology

automated theorem provers

10:an, Kemigården 4, Chalmers
Opponent: Dr Michael Bain, School of Computer Science and Engineering, University of New South Wales (UNSW), Australia


Alexander Gower

Chalmers, Data- och informationsteknik, Data Science och AI

A. H. Gower, K. Korovin, D. Brunnsåker, F. Kronström, G. K. Reder, I. A. Tiukova, R. S. Reiserer, J. P. Wikswo, R. D. King. The Use Of AI-Robotic Systems For Scientific Discovery.

Genesis-DB: a database for autonomous laboratory systems

Bioinformatics Advances,; Vol. 3(2023)

Artikel i vetenskaplig tidskrift

RIMBO - An Ontology for Model Revision Databases

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),; Vol. 14276 LNAI(2023)p. 523-534

Paper i proceeding

A. H. Gower, K. Korovin, D. Brunnsåker, E. Y. Bjurström, P. Lasin, I. A. Tiukova, R. D. King. LGEM⁺: Automated Improvement of Metabolic Network Models and Model-Driven Experimental Design through Abduction


Informations- och kommunikationsteknik

Livsvetenskaper och teknik (2010-2018)


Biokemi och molekylärbiologi

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

Datavetenskap (datalogi)


Chalmers infrastruktur för masspektrometri

C3SE (Chalmers Centre for Computational Science and Engineering)



10:an, Kemigården 4, Chalmers


Opponent: Dr Michael Bain, School of Computer Science and Engineering, University of New South Wales (UNSW), Australia

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