Biological design with machine learning and limited data.

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• Diego Oyarzun, University of Edinburgh
• Thursday 13 March 2025, 14:00-15:00
• LR3A, Department of Engineering and online (Zoom).

If you have a question about this talk, please contact Fulvio Forni.

AI and machine learning have rapidly emerged as promising tools for cellular engineering and optimisation. Yet the complexities of biological measurements often limit the applicability of state-of-the-art algorithms that require large and well-curated data for training. This gap could potentially leave behind many academic and industry laboratories that could hugely benefit from this technology. In this talk, I will describe recent applications of machine learning for in silico discovery and optimisation, with a focus on small and heterogeneous datasets typically encountered in biological design tasks. Examples include predicting protein expression/function from sequence information, low-N drug discovery against complex diseases, and optimisation of gene circuits for metabolite production.

The seminar will be held in LR3A , Department of Engineering, and online (zoom): https://newnham.zoom.us/j/92544958528?pwd=YS9PcGRnbXBOcStBdStNb3E0SHN1UT09

This talk is part of the CUED Control Group Seminars series.

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