Computational biofilms: analogue spatial computing with bacterial colonies

在这个项目中，我们的目标是在细菌生物膜中进行任意模拟计算。为了探索这一点，我们将实现一种人工神经网络（ANN），使用空间排列并通过细胞间信号连接的工程细菌种群。以前在生物系统中计算的工作主要是试图模拟电子电路的数字逻辑能力。这是对发生在生物系统中的自然模拟过程的潜在限制约束。相比之下，人工神经网络不受这些限制，这意味着它们可以成为构建人工生物计算机的有用框架。我们将设计具有细胞间信号（群体感应）系统的大肠杆菌菌株，其中通信菌落的空间分离形成神经网络结构。有三个目标：1。开发计算生物膜的计算机设计流程2。创造工程细菌神经元。我们的方法将在合成生物学、生物工程和生物传感领域开辟新的生物计算方法。最终，这些受神经网络启发的细菌群落将帮助我们探索自然生物系统中的信息处理。

Our aim in this project is to engineer arbitrary analogue computation into bacterial biofilms. To explore this, we will implement a type of artificial neural network (ANN) using engineered bacterial populations arranged spatially and linked by intercellular signals. Previous work on computation in biological systems has mostly tried to emulate the digital logic capabilities of electronic circuits. This is a potentially limiting constraint on the naturally analogue processes that occur in biological systems. In comparison, ANNs are not constrained by these limitations, meaning they could be a useful framework for building artificial biological computers. We will engineer E. coli strains with intercellular signalling (quorum sensing) systems, where spatial separation of communicating colonies form a neural network structure. There are three objectives:1. Develop an in silico design process for computational biofilms 2. Create engineered bacterial neurons 3. Demonstrate computational capabilities of spatial biofilms Our approach will open up new ways to perform biological computation, with applications in synthetic biology, bioengineering and biosensing. Ultimately, these neural-network-inspired bacterial communities will help us explore information processing in natural biological systems.