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

我们将建立一个实验-理论框架，以理解和设计细胞中的基因调控网络和信息处理如何在能动的细胞群落中产生适应性集体行为和宏观模式。尽管在理解某些生物系统中的细胞通信方面取得了巨大进展，但缺乏紧急模式和细胞过程之间的一般联系。结合实验和理论，我们将建立和验证提供这种联系的计算模型。我们将使用机器学习通过调整细胞过程来优化模式，利用合成生物学工具。因此，我们将设计具有自组织空间结构的细胞群，用于工业生物技术和临床应用。

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.