Engineering microbial communities for distributed computation using bacterial micro compartments.

使用细菌微区室工程微生物群落进行分布式计算。

• 批准号: 2546732
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2546732
• 关键词: Engineering; microbial; communities; distributed; computation

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.

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，因为它减轻了与工程单一菌株相关的限制，并允许我们将系统的应用扩展到复杂的生物传感和诊断工具，生物过程控制和工业过程监测等领域。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（逻辑门，神经网络）。利用数学模型，我们设计了微生物的相互作用，然后在生物反应器或空间“计算生物膜”中构建了群落。细菌微室（microcompartments, BMCs）是原核细胞中选择性渗透的外壳，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，这些源是特定于生态位的，因此有助于形成和扭曲细菌群落。在代谢工程和生物医学领域，bmc结构作为纳米技术平台也具有广泛的应用前景。frank小组一直致力于设计和生产具有特定特性的重组bmc。该项目将以这两个小组的知识为基础，将工程微生物群落和细菌微室的不同方面结合在一起。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)应用机器学习方法来优化纳米颗粒生产。我们将以现有的强化学习框架为基础，控制生物反应器中的微生物种群。目标3)利用bmc开发可以操纵现存微生物群的新型治疗细菌这些方法有可能彻底改变我们在生物计算和治疗中设计具有新功能的微生物群落的方式。