Collaborative Research: Spatiotemporal Dynamics of Synthetic Microbial Consortia

合作研究：合成微生物群落的时空动力学

• 批准号: 1662290
• 负责人: Matthew Bennett
• 金额: $ 93.11万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1662290&HistoricalAwards=false
• 关键词: Collaborative; Research; Spatiotemporal; Dynamics; Synthetic

Synthetic biology aims to engineer the genetic code of cells for practical applications such as the production of biofuels and genetic therapies. However, most synthetically engineered microbes act at the single-cell level. Such organisms cannot coordinate their activity, limiting their ability to make large impacts. In contrast, synthetic multicellular systems are composed of cells designed to act in concert to achieve their goals. The coordinated behaviors of such populations can be more complex, flexible and impactful than that of uncoordinated collections of cells. One method of creating such multicellular systems is through the engineering of synthetic microbial consortia - conglomerations of various strains of genetically engineered microbes that work together to achieve tasks. The coordinated activity of constituent strains within a consortium can display emergent behaviors that are difficult to engineer into a single strain. At their core, the individual strains in a synthetic consortium are similar to other synthetically engineered microbes, as their genetic sequences have been purposefully altered. Individual cells within a synthetic consortium communicate with one another through intercellular signaling pathways. Therefore, when designing synthetic microbial consortia, one must take into account the continually changing spatial arrangement of cells and strains within the greater population. However, the spatio-temporal dynamics of intercellular signaling within microbial consortia are poorly understood, limiting our ability to engineer large synthetic multicellular consortia. Here, the PIs will develop mathematical approaches for describing the dynamics of synthetic microbial consortia. To do so, they will use an interdisciplinary approach that combines experimental synthetic biology, microfluidic engineering, and mathematical biology. By examining increasingly complex consortia, the PIs will develop a hierarchy of sophisticated mathematical and computational models. The overall goal of this work is to better understand the complex, emergent dynamics of synthetic microbial consortia, and to engineer consortia that achieve specific goals by coordinating gene activity across space and time.The majority of synthetic gene circuits have been built within a single strain and operate at the single-cell level. Yet, to realize the full potential of synthetic biology we need to be able to design organisms that can interact with each other within and across different strains. Synthetic microbial consortia can coordinate gene expression across a population or specialize by assuming different responsibilities within the collective. This allows consortia to be more efficient, and have a wider range of functions than communities of non-interacting cells. However, the larger the consortium, the harder it is to coordinate behaviors of the constituent cells. This is because the limited diffusion of molecules in the extracellular medium makes it difficult to coordinate the activity of gene networks interacting through intercellular signals. To understand, rationally design, and control large populations it is necessary to develop and validate mathematical and computational models of gene network dynamics that describe large-scale population-wide gene regulation. This is challenging because the dynamics of microbial collectives is stochastic, nonlinear, spatially inhomogeneous, and multi-scale. Models must account for the nonlinear dynamics of genetic circuits within individual cells, the spatial diffusion of signaling molecules that mediate interactions between cells, and the dynamics of multiple, co-mingled bacterial populations whose spatial configurations change due to cellular growth and division. In the proposed work, the PIs will develop such mathematical and computational models of the spatio-temporal dynamics of synthetic microbial consortia. To do so, they will use an interdisciplinary approach that combines experimental synthetic biology, microfluidic engineering, and mathematical biology. By examining increasingly complex consortia, the PIs will develop a hierarchy of increasingly sophisticated mathematical and computational models. The overall goal of this work is to better understand the complex, emergent dynamics of synthetic microbial consortia, and to engineer consortia that coordinate gene activity across space and time.

合成生物学的目标是设计细胞的遗传密码，用于实际应用，如生产生物燃料和基因疗法。然而，大多数合成工程微生物在单细胞水平上起作用。这些生物不能协调它们的活动，限制了它们产生巨大影响的能力。相比之下，合成多细胞系统是由设计成协同行动以实现其目标的细胞组成的。这些群体的协调行为可能比不协调的细胞集合更复杂、更灵活、更有影响力。创造这种多细胞系统的一种方法是通过合成微生物联合体的工程设计——各种基因工程微生物菌株的集合体，共同完成任务。一个联合体中组成菌株的协调活动可以显示出难以设计成单一菌株的紧急行为。在它们的核心，单个菌株在一个合成联合体类似于其他合成工程微生物，因为它们的基因序列已经被有目的地改变。合成联合体中的单个细胞通过细胞间信号通路相互通信。因此，在设计合成微生物群落时，必须考虑到在更大的种群中不断变化的细胞和菌株的空间排列。然而，微生物群体内细胞间信号的时空动态知之甚少，限制了我们设计大型合成多细胞群体的能力。在这里，pi将开发数学方法来描述合成微生物群落的动力学。为此，他们将采用跨学科的方法，结合实验合成生物学、微流体工程和数学生物学。通过检查日益复杂的财团，pi将开发一个复杂的数学和计算模型的层次结构。这项工作的总体目标是更好地理解合成微生物群落的复杂、新兴动态，并通过协调跨空间和时间的基因活动来设计实现特定目标的群落。大多数合成基因电路都是在单个菌株内构建的，并在单细胞水平上运行。然而，为了实现合成生物学的全部潜力，我们需要能够设计出能够在不同菌株内部和跨菌株之间相互作用的生物体。合成微生物联合体可以在群体中协调基因表达，或者通过在集体中承担不同的责任来特殊化。这使得联盟更有效率，并且比非相互作用的细胞群体具有更广泛的功能。然而，联合体越大，协调组成细胞的行为就越困难。这是因为分子在细胞外介质中的有限扩散使得通过细胞间信号相互作用的基因网络的活动难以协调。为了理解、合理设计和控制大种群，有必要开发和验证描述大规模种群范围内基因调控的基因网络动力学的数学和计算模型。这是具有挑战性的，因为微生物群体的动力学是随机的、非线性的、空间上不均匀的和多尺度的。模型必须考虑到单个细胞内遗传回路的非线性动力学，介导细胞间相互作用的信号分子的空间扩散，以及由于细胞生长和分裂而导致空间结构变化的多个混合细菌群体的动力学。在提议的工作中，pi将开发这种合成微生物群落时空动态的数学和计算模型。为此，他们将采用跨学科的方法，结合实验合成生物学、微流体工程和数学生物学。通过检查日益复杂的财团，pi将开发一个日益复杂的数学和计算模型的层次结构。这项工作的总体目标是更好地理解复杂的，合成微生物群落的紧急动态，并设计协调跨空间和时间的基因活动的群落。

项目成果

Moran model of spatial alignment in microbial colonies

• DOI: 10.1016/j.physd.2019.02.001
• 发表时间: 2019-08-01
• 期刊: PHYSICA D-NONLINEAR PHENOMENA
• 影响因子: 4
• 作者: Karamched, B. R.;Ott, W.;Josic, K.
• 通讯作者: Josic, K.

Bayesian inference of distributed time delay in transcriptional and translational regulation

• DOI: 10.1093/bioinformatics/btz574
• 发表时间: 2020-01-15
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Choi, Boseung;Cheng, Yu-Yu;Kim, Jae Kyoung
• 通讯作者: Kim, Jae Kyoung

Majority sensing in synthetic microbial consortia

• DOI: 10.1038/s41467-020-17475-z
• 发表时间: 2020-07-21
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Alnahhas, Razan N.;Sadeghpour, Mehdi;Bennett, Matthew R.
• 通讯作者: Bennett, Matthew R.

A synthetic system for asymmetric cell division in Escherichia coli

大肠杆菌不对称细胞分裂的合成系统

• DOI: 10.1038/s41589-019-0339-x
• 发表时间: 2019
• 期刊: Nature Chemical Biology
• 影响因子: 14.8
• 作者: Molinari, Sara;Shis, David L.;Bhakta, Shyam P.;Chappell, James;Igoshin, Oleg A.;Bennett, Matthew R.
• 通讯作者: Bennett, Matthew R.

Long-range temporal coordination of gene expression in synthetic microbial consortia

• DOI: 10.1038/s41589-019-0372-9
• 发表时间: 2019-11-01
• 期刊: NATURE CHEMICAL BIOLOGY
• 影响因子: 14.8
• 作者: Kim, Jae Kyoung;Chen, Ye;Bennett, Matthew R.
• 通讯作者: Bennett, Matthew R.