Computational biofilms: analogue spatial computing with bacterial colonies

在这个项目中，我们的目标是将任意模拟计算工程化为细菌生物膜。为了探索这一点，我们将实现一种类型的人工神经网络（ANN），使用空间排列的工程菌群，并通过细胞间信号连接。以前在生物系统中的计算工作主要是试图模拟电子电路的数字逻辑能力。这是对生物系统中发生的自然模拟过程的潜在限制性约束。相比之下，人工神经网络不受这些限制的约束，这意味着它们可能是构建人工生物计算机的有用框架。我们将工程师E。大肠杆菌菌株与细胞间信号（群体感应）系统，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。开发计算机生物膜的计算机设计过程2。创建工程细菌神经元3.展示空间生物膜的计算能力我们的方法将开辟新的方法来执行生物计算，在合成生物学，生物工程和生物传感的应用。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.