Biomolecular control group

what we do

Our team develops computational methods to study molecular processes in living cells. We use mathematics to understand natural networks and to design novel systems for biotechnology and biomedicine. We employ a wide range of methods, such as machine learning, control theory, stochastic analysis and network theory. Large parts of our work are in collaboration with Systems and Synthetic Biology labs in the UK and abroad.

who we are

The group lead is Diego Oyarzún and includes research students and postdocs. Together we gather expertise in many disciplines like machine learning, mathematics, biochemistry and bioinformatics.

where we work

We are very lucky to be located at the School of Informatics and the School of Biological Sciences of the University of Edinburgh, in one of the most vibrant European capitals. We are members of the Edinburgh Centre for Engineering Biology.

recent papers

• Self-supervised learning of fluxomic data
• Integration of kinetic and genome-scale metabolic models
• Impact of DNA representations on sequence-to-expression models
• Graph neural networks for predicting gene essentiality
• Improved chemical production with active learning and metabolomics
• Discovery of anti-senescence compounds with machine learning.
  Media coverage The Times, Sky News, The Conversation, Deutsche Welle