  Techniques to autonomously drive research have been prominent in
Computational Scientific Discovery, while Synthetic Biology is a field of
science that focuses on designing and constructing new biological systems for
useful purposes. Here we seek to apply logic-based machine learning techniques
to facilitate cellular engineering and drive biological discovery.
Comprehensive databases of metabolic processes called genome-scale metabolic
network models (GEMs) are often used to evaluate cellular engineering
strategies to optimise target compound production. However, predicted host
behaviours are not always correctly described by GEMs, often due to errors in
the models. The task of 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,
$BMLP_{active}$, which efficiently explores the genomic hypothesis space by
guiding informative experimentation through active learning. In contrast to
sub-symbolic methods, $BMLP_{active}$ encodes a state-of-the-art GEM of a
widely accepted bacterial host in an interpretable and logical representation
using datalog logic programs. Notably, $BMLP_{active}$ can successfully learn
the interaction between a gene pair with fewer training examples than random
experimentation, overcoming the increase in experimental design space.
$BMLP_{active}$ 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 microbial engineering.
