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.