LGEM+: A First-Order Logic Framework for Automated Improvement of Metabolic Network Models Through Abduction
Paper i proceeding, 2023

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.

automated theorem proving

first-order logic

metabolic modelling

artificial intelligence

Scientific discovery

systems biology

Författare

Alexander Gower

Chalmers, Data- och informationsteknik, Data Science och AI

Konstantin Korovin

University of Manchester

Daniel Brunnsåker

Chalmers, Data- och informationsteknik, Data Science och AI

Ievgeniia Tiukova

Chalmers, Life sciences, Infrastrukturer

Kungliga Tekniska Högskolan (KTH)

Ross King

Alan Turing Institute

Chalmers, Data- och informationsteknik, Data Science och AI

University of Cambridge

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 14276 LNAI 628-643
9783031452741 (ISBN)

26th International Conference on Discovery Science, DS 2023
Porto, Portugal,

Ämneskategorier

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

Datavetenskap (datalogi)

DOI

10.1007/978-3-031-45275-8_42

Mer information

Senast uppdaterat

2023-10-30