Experimental and computational design of self-organised patterning via collective information processing in cellular communities

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We will build an experimental-theoretical framework to understand and design how gene-regulatory networks and information processing in cells generate adaptive collective behaviour and macroscopic patterns in motile cellular communities. Despite huge progress in understanding cell communication in certain biological systems, a general link between emergent patterns and cellular processes is lacking. Combining experiments and theory, we will build and validate computational models that provide this link. We will use machine learning to optimise patterns by tuning cellular processes, leveraging synthetic biology tools. Thus, we will design cellular populations with self-organised spatial structure, for deployment in industrial biotechnology and clinical applications.