Digital to Biological Translation: How the Algorithmic Data-Driven Design Reshapes Synthetic Biology

Synthetic biology, an emergent interdisciplinary field integrating principles from biology, engineering, and computer science, endeavors to rationally design and construct novel biological systems or reprogram extant ones to achieve predefined functionalities. The conventional approach relies on an iterative Design-Build-Test-Learn (DBTL) cycle, a process frequently hampered by the intrinsic complexity, non-linear interactions, and vast design space inherent to biological systems. The advent of Artificial Intelligence (AI), and particularly its subfields of Machine Learning (ML) and Deep Learning (DL), is fundamentally reshaping this paradigm by offering robust computational frameworks to navigate these formidable challenges. This review elucidates the strategic integration of AI/ML/DL across the synthetic biology workflow, detailing the specific algorithms and mechanisms that enable rational design, autonomous experimentation, and pathway optimization. Their advanced applications are specifically underscored across critical facets, including de novo rational design, enhanced predictive modeling, intelligent high-throughput data analysis, and AI-driven laboratory automation. Furthermore, pivotal challenges, such as data sparsity, model interpretability, the “black box” problem, computational resource demands, and ethical considerations, have been addressed, while concurrently forecasting future trajectories for this rapidly advancing and convergent domain. The synergistic convergence of these disciplines is demonstrably accelerating biological discovery, facilitating the creation of innovative and scalable biological solutions, and fostering a more predictable and efficient paradigm for biological engineering.