Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models

Reasoning about hypotheses and updating knowledge through empirical observations are central to scientific discovery. In this work, we applied logic-based machine learning methods to drive biological discovery by guiding experimentation. Genome-scale metabolic network models (GEMs) - comprehensive r...

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Vydáno v:Machine learning Ročník 114; číslo 11; s. 254
Hlavní autoři: Ai, Lun, Muggleton, Stephen H., Liang, Shi-Shun, Baldwin, Geoff S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.11.2025
Springer Nature B.V
Témata:
Annotations
Artificial Intelligence
Boolean
Computer Science
Control
Design of experiments
Design optimization
E coli
Experimentation
Experiments
Genes
Genetic engineering
Genomes
Hypotheses
Logic programming
Logic programs
Machine Learning
Mechatronics
Metabolism
Metabolites
Microorganisms
Natural Language Processing (NLP)
Robotics
Robots
Simulation and Modeling
Synthetic biology
Matrix
Inductive logic programming
Active learning
Computational scientific discovery
ISSN:0885-6125, 1573-0565
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Shrnutí:Reasoning about hypotheses and updating knowledge through empirical observations are central to scientific discovery. In this work, we applied logic-based machine learning methods to drive biological discovery by guiding experimentation. Genome-scale metabolic network models (GEMs) - comprehensive representations of metabolic genes and reactions - are widely used to evaluate genetic engineering of biological systems. However, GEMs often fail to accurately predict the behaviour of genetically engineered cells, primarily due to incomplete annotations of gene interactions. The task of learning the intricate genetic interactions within GEMs presents computational and empirical challenges. To efficiently predict using GEM, we describe a novel approach called Boolean Matrix Logic Programming (BMLP) by leveraging Boolean matrices to evaluate large logic programs. We developed a new system, , which guides cost-effective experimentation and uses interpretable logic programs to encode a state-of-the-art GEM of a model bacterial organism. Notably, successfully learned the interaction between a gene pair with fewer training examples than random experimentation, overcoming the increase in experimental design space. enables rapid optimisation of metabolic models to reliably engineer biological systems for producing useful compounds. It offers a realistic approach to creating a self-driving lab for biological discovery, which would then facilitate microbial engineering for practical applications.
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ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-025-06868-0