  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.
