Architectures of emerging biofoundry platforms for synthetic biology

Abstract

Biofoundries are integrated, automated platforms that accelerate synthetic biology applications by facilitating high-throughput and labor-intensive experiments. They incorporate robotic systems, analytical instruments, and software for workflow design, execution, and data management, ensuring scalable and reproducible biological engineering. This review outlines the architectural foundations of biofoundries, emphasizing Robot-Assisted Modules (RAMs) that support modular and flexible workflow configurations from simple single-task units to complex, multi-workstation systems. Advances in software development, from compiler-level tools to high-level platforms, have further enhanced workflow design and system interoperability. We examine key synthetic biology applications, including DNA assembly, strain engineering, and pathway optimization, that benefit from automated and high-throughput operations. In parallel, performance evaluation metrics are being developed to guide implementation and optimization. Finally, the integration of artificial intelligence enables predictive modeling and iterative learning, laying the groundwork for self-driving laboratories that will support sustainable and distributed synthetic biology at scale.