Engineering microbial communities for distributed computation using bacterial micro compartments.

构建分布式生物系统是合成生物学的一个有吸引力的前景，因为它消除了与工程单菌株相关的限制，并使我们能够将我们的系统的应用扩展到包括复杂的生物传感和诊断工具，生物过程控制和工业过程监控在内的领域。巴恩斯小组一直致力于细菌群落的工程设计，以作为能够整合信号信息和决策的分布式生物计算系统（eglogic gates，神经网络）。我们利用数学模型设计了微生物相互作用，然后在生物反应器或空间“计算生物膜”中构建了群落。细菌微区室（BMC）是原核细胞中具有选择性渗透性的外壳，类似于真核生物中的细胞器。许多BMC使微生物能够利用特定的能量、碳和氮源，这些资源是生态位特异性的，因此有助于形成和扭曲细菌群落。BMC架构也是具有吸引力的纳米技术平台，在代谢工程和生物医学中具有应用。Frank小组一直致力于设计和生产具有特定特性的重组BMCs。该项目将建立在两个小组的知识基础上，并将工程微生物群落和细菌微区室的不同方面结合起来。主要有三个方面：目标1）使用BMC作为合成生物学工具包的附加部分，用于构建形成分布式计算系统的微生物群落。目标2）将机器学习方法应用于优化纳米颗粒生产。我们将建立在我们现有的强化学习框架，用于控制生物反应器中的微生物种群。目标3）使用BMC开发新的治疗细菌，可以操纵外部微生物组。这些方法有可能彻底改变我们如何在生物计算和治疗学中设计具有新功能的微生物群落。

Building distributed biological systems is an attractive prospect for synthetic biology as italleviates the constraints associated with engineering single strains and allows us to expandthe applications of our systems into areas including complex biosensing and diagnostic tools,bioprocess control and the monitoring of industrial processes. The Barnes group has beenworking towards the engineering of bacterial communities to serve as distributedbiocomputing systems capable of integrating signal information and decision making (eglogic gates, neural networks). Using mathematical modelling we have designed microbialinteractions and then constructed communities either within bioreactors or in spatial"computational biofilms".Bacterial microcompartments (BMCs) are selectively permeable shells in prokaryotic cellsthat are analogous to organelles in eukaryotes. Many BMCs enable microbes to utilize specificenergy, carbon, and nitrogen sources that are niche specific and therefore contribute to bothforming and distorting bacterial communities. BMCs architectures are also attractive asnanotechnology platforms with applications in metabolic engineering and biomedicine. TheFrank group have been working on the design and production of recombinant BMCs withspecified properties.This project will build on the knowledge of both groups and bring together different aspectsof engineering microbial communities and bacterial microcompartments. There are threemain strands:Aim 1) the use of BMCs as an additional part in the synthetic biology toolkit for theconstruction of microbial communities that form distributed computing systems.Aim 2) apply machine learning approaches to the optimisation of nanoparticle production. Wewill build on our existing reinforcement learning framework for control of microbialpopulations in bioreactors.Aim 3) the use of BMCs to develop novel therapeutic bacteria that can manipulate extantmicrobiomesThese approaches have the potential to revolutionise how we engineer microbialcommunities with new functionalities in biocomputing and therapeutics.