Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges

Area of Science:

• Synthetic biology
• Computational biology
• Biotechnology

Background:

• Biosystems design is complex due to intricate interconnections.
• Machine learning (ML) and deep learning (DL) offer powerful tools for biological design.
• Synthetic biology generates data for ML/DL, while ML/DL guides synthetic biology efforts.

Purpose of the Study:

• To review the synergy between ML/DL and synthetic biology.
• To highlight ML/DL applications in engineering cells, proteins, and metabolic pathways.
• To discuss challenges and solutions in integrating ML/DL with synthetic biology.

Main Methods:

• Review of ML/DL fundamentals and predictive capabilities.
• Description of deep learning architectures and their synthetic biology applications.
• Analysis of challenges and proposed solutions for ML/DL in synthetic biology.

Main Results:

• ML/DL enables the design of novel biological parts and improved experimental strategies.
• Applications include automated microscopy data analysis and protein structure prediction.
• Biomolecular implementations of artificial neural networks (ANNs) are emerging.

Conclusions:

• ML/DL significantly advances synthetic biology by enhancing design and discovery.
• Integration of ML/DL accelerates the engineering of complex biological systems.
• Addressing current challenges will further unlock the potential of ML/DL in biotechnology.