LGEM+: A First-Order Logic Framework for Automated Improvement of Metabolic Network Models Through Abduction

Alexander h. Gower, Konstantin Korovin, Daniel Brunnsåker, Ievgeniia a. Tiukova, Ross d. King

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Scientific discovery in biology is difficult due to the complexity of the systems involved and the expense of obtaining high quality experimental data. Automated techniques are a promising way to make scientific discoveries at the scale and pace required to model large biological systems. A key problem for 21st century biology is to build a computational model of the eukaryotic cell. The yeast Saccharomyces cerevisiae is the best understood eukaryote, and genome-scale metabolic models (GEMs) are rich sources of background knowledge that we can use as a basis for automated inference and investigation.

We present LGEM+, a system for automated abductive improvement of GEMs consisting of: a compartmentalised first-order logic framework for describing biochemical pathways (using curated GEMs as the expert knowledge source); and a two-stage hypothesis abduction procedure.

We demonstrate that deductive inference on logical theories created using LGEM+, using the automated theorem prover iProver, can predict growth/no-growth of S. cerevisiae strains in minimal media. LGEM+ proposed 2094 unique candidate hypotheses for model improvement. We assess the value of the generated hypotheses using two criteria: (a) genome-wide single-gene essentiality prediction, and (b) constraint of flux-balance analysis (FBA) simulations. For (b) we developed an algorithm to integrate FBA with the logic model. We rank and filter the hypotheses using these assessments. We intend to test these hypotheses using the robot scientist Genesis, which is based around chemostat cultivation and high-throughput metabolomics.
Original languageEnglish
Title of host publication26th International Conference, 2023
PublisherSpringer Cham
Chapter42
Pages628-643
Volume14276
ISBN (Electronic)978-3-031-45275-8
ISBN (Print)978-3-031-45274-1
DOIs
Publication statusE-pub ahead of print - 8 Oct 2023

Publication series

NameDiscovery Science
Volume14276
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Scientific discovery
  • artificial intelligence
  • systems biology
  • metabolic modelling
  • first-order logic
  • automated theorem proving

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