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Sökning: WFRF:(Gower Alexander 1993) > (2023) > LGEM+ :

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

Gower, Alexander, 1993 (författare)
Chalmers University of Technology, Gothenburg, Sweden,Chalmers tekniska högskola
Korovin, Konstantin (författare)
The University of Manchester, Manchester, UK,University of Manchester
Brunnsåker, Daniel, 1992 (författare)
Chalmers University of Technology, Gothenburg, Sweden,Chalmers tekniska högskola
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Tiukova, Ievgeniia A. (författare)
KTH,Industriell bioteknologi,Chalmers University of Technology, Gothenburg, Sweden,Chalmers tekniska högskola,Kungliga Tekniska Högskolan (KTH),Royal Institute of Technology (KTH)
King, Ross, 1962 (författare)
Chalmers University of Technology, Gothenburg, Sweden; Cambridge University, Cambridge, UK; Alan Turing Institute, London, UK,The Alan Turing Institute,Chalmers tekniska högskola,University Of Cambridge
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 (creator_code:org_t)
Springer Nature, 2023
2023
Engelska.
Ingår i: Discovery Science - 26th International Conference, DS 2023, Proceedings. - : Springer Nature. ; 14276 LNAI, s. 628-643
  • Konferensbidrag (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
NATURVETENSKAP  -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

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

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