Deep Learning Concepts and Applications for Synthetic Biology

Area of Science:

• Interdisciplinary research at the intersection of synthetic biology and artificial intelligence (AI), specifically deep learning (DL).

Background:

• Synthetic biology (SynBio) and deep learning (DL) exhibit a natural synergy, with each field benefiting the other.
• SynBio can generate large datasets for training DL models (e.g., via DNA synthesis).
• DL can inform SynBio design, suggesting novel biological parts or optimal experimental strategies.

Purpose of the Study:

• To review the synergy between synthetic biology and deep learning.
• To provide an overview of synthetic biology-relevant data types and deep learning architectures.
• To highlight emerging studies and discuss future opportunities at this research interface.

Main Methods:

• Review of recent research at the interface of engineering biology and deep learning.
• Categorization of synthetic biology data relevant to deep learning applications.
• Overview of deep learning architectures applicable to synthetic biology challenges.

Main Results:

• Successful applications include designing novel biological parts, predicting protein structures, and automated microscopy data analysis.
• Deep learning aids in optimal experimental design and biomolecular implementations of neural networks.
• Emerging studies demonstrate deep learning's capability to enable novel understanding and design in synthetic biology.

Conclusions:

• The integration of synthetic biology and deep learning holds significant potential for advancing biological engineering.
• Further research is needed to address challenges and unlock future opportunities in this rapidly evolving field.
• This synergy promises to accelerate innovation in designing and understanding biological systems.