From code to cells - how AI is transforming Engineering Biology

The event, presented by GAIL Fellows Professor Filippo Menolascina and Professor Giovanni Stracquadanio, will highlight current research demonstrating how deep learning and probabilistic modelling are accelerating protein and therapeutic design, biomolecular circuit engineering, and AI-driven experimental design. Particular emphasis will be placed on closing the loop between digital models and physical experimentation through robotics and automated biofoundries, enabling faster and more data-efficient biological discovery.

Looking ahead, the session will discuss how AI, after changing how we code, is transforming how we decode and re-encode life itself. We will explore emerging paradigms where AI systems act as scientific collaborators, supporting researchers in hypothesis generation, experimental planning, and data interpretation, and ultimately enabling semi-autonomous and fully autonomous discovery pipelines.

The event will also provide an opportunity to discuss future collaboration opportunities across the Edinburgh AI and Engineering Biology communities, including emerging external partnerships and new interdisciplinary research directions. The session will also explore how lessons learned from Engineering Biology can be applied to other areas of AI for Science.