LGEM+: Automated Improvement of Metabolic Network Models and Model-Driven Experimental Design through Abduction

Preprint, 2026

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, and for experimental design consisting of: a compartmentalised first-order logic framework for describing biochemical pathways (using curated GEMs as the expert knowledge source); a two-stage hypothesis abduction procedure; integra- tion with flux-balance analysis (FBA); and metabolic pathway extraction and analysis algorithms.

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 gen- erated hypotheses using two criteria: (a) genome-wide single-gene essentiality prediction, and (b) constraint of flux-balance analysis (FBA) simulations. We present a model-driven experimental design strategy, and demonstrate this with a differential expression study, and using the ∆pfk2 mutant strain as a case study.