Advancing genetic engineering with active learning: theory, implementations and potential opportunities.

Area of Science:

• Genetic Engineering
• Machine Learning
• Computational Biology

Background:

• Machine learning (ML) models are increasingly used to accelerate experimentation and understand biological mechanisms in genetic engineering.
• Challenges include poor data quality and limited validation resources, hindering model accuracy and design improvements.
• Active learning (AL) offers a solution by iteratively identifying the most informative experiments.

Purpose of the Study:

• To review the application of AL in genetic engineering.
• To explore practical implementations of AL within the design-build-test-learn cycle.
• To discuss the potential of AL through interdisciplinary collaboration.

Main Methods:

• Review of existing literature on active learning in biological sciences.
• Analysis of AL strategies for data acquisition in genetic engineering.
• Discussion of integrating AL with ML models for experimental design.

Main Results:

• AL significantly reduces experimental effort and cost by prioritizing informative data collection.
• Iterative AL improves the accuracy and performance of ML models in genetic engineering tasks.
• AL facilitates the discovery of novel biomolecule functionalities and underlying mechanisms.

Conclusions:

• Active learning provides an efficient framework for data-driven genetic engineering.
• AL enhances the design-build-test-learn cycle, optimizing biomolecule development.
• Integrating AL with ML accelerates biological discovery and reduces experimental burdens.