Active learning of digenic functions with boolean matrix logic programming

We apply logic-based machine learning techniques to facilitate cellular engineering and drive biological discovery, based on comprehensive databases of metabolic processes called genome-scale metabolic network models (GEMs). Predicted host behaviours are not always correctly described by GEMs. Learning the intricate genetic interactions within GEMs presents computational and empirical challenges. To address these, we describe a novel approach called Boolean Matrix Logic Programming (BMLP) by leveraging boolean matrices to evaluate large logic programs. We introduce a new system, BMLP₀₂ₓ₈ₕ₄, which efficiently explores the genomic hypothesis space by guiding informative experimentation through active learning. In contrast to sub-symbolic methods, BMLP₀₂ₓ₈ₕ₄ encodes a state-of-the-art GEM of a widely accepted bacterial host in an interpretable and logical representation using datalog logic programs. Notably, BMLP₀₂ₓ₈ₕ₄ can successfully learn the interaction between a gene pair with fewer training examples than random experimentation, overcoming the increase in experimental design space. BMLP₀₂ₓ₈ₕ₄ enables rapid optimisation of metabolic models and offers a realistic approach to a self-driving lab for microbial engineering.