  The application of Artificial Intelligence (AI) to synthetic biology will
provide the foundation for the creation of a high throughput automated platform
for genetic design, in which a learning machine is used to iteratively optimise
the system through a design-build-test-learn (DBTL) cycle. However, mainstream
machine learning techniques represented by deep learning lacks the capability
to represent relational knowledge and requires prodigious amounts of annotated
training data. These drawbacks strongly restrict AI's role in synthetic biology
in which experimentation is inherently resource and time intensive. In this
work, we propose an automated biodesign engineering framework empowered by
Abductive Meta-Interpretive Learning ($Meta_{Abd}$), a novel machine learning
approach that combines symbolic and sub-symbolic machine learning, to further
enhance the DBTL cycle by enabling the learning machine to 1) exploit domain
knowledge and learn human-interpretable models that are expressed by formal
languages such as first-order logic; 2) simultaneously optimise the structure
and parameters of the models to make accurate numerical predictions; 3) reduce
the cost of experiments and effort on data annotation by actively generating
hypotheses and examples. To verify the effectiveness of $Meta_{Abd}$, we have
modelled a synthetic dataset for the production of proteins from a three gene
operon in a microbial host, which represents a common synthetic biology
problem.
