Automated Biodesign Engineering by Abductive Meta-Interpretive Learning

TL;DR

This paper introduces a novel AI framework combining symbolic and sub-symbolic learning to improve automated biodesign, enabling more efficient, interpretable, and resource-effective genetic engineering processes.

Contribution

The work presents $Meta_{Abd}$, a new machine learning approach that integrates domain knowledge with model optimization to enhance synthetic biology design cycles.

Findings

Successfully modeled protein production in synthetic biology.

Reduced experimental costs and data annotation efforts.

Demonstrated improved model interpretability and accuracy.

Abstract

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.