Special Issue on Artificial Intelligence for Synthetic Biology
Authors
Martin, H. G., Mazurenko, S., Zhao, H.
Source
ACS SYNTHETIC BIOLOGY 13: 408–410 (2024)
Abstract
Synthetic biology presents significant prospects of helping scientists tackle important societal problems. However, a significant hurdle in this endeavor is our inability to predict biological systems as accurately as we predict and simulate physical or chemical ones. This limitation has important fundamental and practical implications: from the practical point of view, we are unable to design biological systems (e.g., proteins, pathways, cells) to a specification (e.g., bind to this molecule with this binding affinity or produce this chemical at this titer, rate, and yield); from the fundamental point of view, we lack an understanding of the underlying mechanisms that produce observed phenotypes. Artificial intelligence (AI) and machine learning (ML) show promise in providing the predictive power that synthetic biology needs and can be applied in all parts of the synthetic biology process.