Engineered burden-based feedback for robust and optimised synthetic biology

工程化的基于负荷的反馈，用于稳健和优化的合成生物学

• 批准号: EP/J021849/1
• 负责人: Thomas Ellis
• 金额: $ 55.68万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ021849%2F1
• 关键词: Engineered; burden; based; feedback; robust

Synthetic biology is an exciting new subject that is accelerating the research and development of new biotechnologies by rigorously applying engineering design principles to the way we work with biological systems. The most prominent application of synthetic biology is the rational modification and redesign of living organisms like microbes for new efficient use in sectors such as energy production, biomedicine, drug production and food technology. Crucial to developing and applying synthetic biology is the rigorous quantification, modelling, analysis and control of the synthetic biology designs. By using this engineering framework we aim to be able to reliably predict and robustly control how engineered biological systems will operate.Although synthetic biology has had numerous successes in research, it is still difficult to predict how engineered cells behave when new synthetic genetic information is added to these host cells. One of the major reasons for this is that new synthetic genes add an as-yet unquantified burden to cells, particularly to commonly used microbes like E. coli. This burden effect is due to the new genes requiring resources to be maintained and function. This means that the introduced genes take resources away from those needed by their host cell in order to grow and survive. Usually the result of this is the unpredictable failure of the synthetic biology design to behave as expected or the creation of designs that only function in a narrow set of ideal conditions.The work proposed in this project seeks to address and make use of the understudied effect of synthetic biology we know as burden. To achieve this goal, we will use novel genetic tools to quantify the effect of burden for several typical synthetic biology devices and do this work in the well-characterised microbe E. coli so that our results are useful to the many researchers who work with this model organism. To see how the cell naturally reacts to burden we will use the high-resolution tool of RNA sequencing to quantify the gene expression changes that a cell triggers when it is burdened. Quantified burden combined with the quantified gene expression changes in response to burden will together give us crucial data that can be used to build a mathematical model of how a cell reacts to new synthetic genes being added and used. This model will allow future applications of synthetic biology to predict how synthetic systems will interact with their host cells and therefore open the door for rigorous optimisation of the robustness/performance compromise inherent to any control engineering design. It will also allow for a new generation of synthetic biology devices that automatically account for the burden effect. To demonstrate this final point, this project will use our quantified understanding of burden to engineer novel synthetic plasmid vectors that are designed to auto-regulate their copy number via feedback mechanisms that take into account burden, thereby serving as general purpose burden-based controllers. We will show how these plasmid systems work by building and testing a self-regulating biological nightlight that emits bioluminescence in the dark without any significant loss in growth. The new plasmid systems we generate will be extremely valuable for synthetic biology as they will allow synthetic devices and systems to respond directly to cell health thereby endowing them with the robust and predictable behaviours needed for future applications in health, energy and biosensing.

合成生物学是一门令人兴奋的新学科，它通过将工程设计原则严格应用于我们处理生物系统的方式来加速新生物技术的研究和开发。合成生物学最突出的应用是对微生物等活体进行合理的修饰和重新设计，以便在能源生产、生物医药、药品生产和食品技术等领域实现新的高效利用。开发和应用合成生物学的关键是合成生物学设计的严格量化、建模、分析和控制。通过使用这个工程框架，我们的目标是能够可靠地预测和强有力地控制工程生物系统的运行方式。尽管合成生物学在研究中已经取得了许多成功，但仍然很难预测当新的合成遗传信息添加到这些宿主细胞时工程细胞的行为。其中一个主要原因是，新的合成基因给细胞增加了迄今尚未量化的负担，特别是对常用的微生物，如大肠杆菌。这种负担效应是由于新基因需要资源来维持和发挥作用。这意味着被引入的基因从宿主细胞中夺走了生长和生存所需的资源。通常，其结果是合成生物学设计的不可预测的失败，不能像预期的那样运行，或者创建只在狭窄的理想条件下发挥作用的设计。这个项目中提出的工作试图解决和利用合成生物学的未被充分研究的影响，我们知道这是负担。为了实现这一目标，我们将使用新的遗传工具来量化几个典型合成生物学设备的负担影响，并在特征良好的微生物大肠杆菌中进行这项工作，以便我们的结果对许多研究这一模式生物的研究人员有用。为了了解细胞对负担的自然反应，我们将使用高分辨率的RNA测序工具来量化细胞在负担时触发的基因表达变化。量化的负担与量化的基因表达变化相结合，将共同为我们提供关键的数据，这些数据可以用来建立细胞如何对添加和使用新的合成基因做出反应的数学模型。这一模型将允许合成生物学的未来应用来预测合成系统将如何与宿主细胞相互作用，从而为严格优化任何控制工程设计所固有的稳健性/性能折衷打开大门。它还将允许新一代合成生物学设备自动考虑负担效应。为了证明这最后一点，这个项目将使用我们对Burden的量化理解来设计新的合成质粒载体，这些载体旨在通过考虑Burden的反馈机制自动调节其拷贝数，从而充当基于Burden的通用控制器。我们将通过构建和测试一种自我调节的生物夜灯来展示这些质粒系统是如何工作的，这种夜灯在黑暗中发出生物发光，而不会对生长造成任何重大损失。我们产生的新的质粒系统将对合成生物学非常有价值，因为它们将允许合成设备和系统直接响应细胞健康，从而赋予它们未来在健康、能源和生物传感方面应用所需的强大和可预测的行为。

项目成果

Modelling the burden caused by gene expression: an in silico investigation into the interactions between synthetic gene circuits and their chassis cell

模拟基因表达引起的负担：对合成基因电路与其底盘细胞之间相互作用的计算机研究

• 发表时间: 2013
• 作者: Algar R Algar

Burden-driven feedback control of gene expression

• DOI: 10.1101/177030
• 发表时间: 2017-08
• 期刊: Nature Methods
• 影响因子: 48
• 作者: Francesca Ceroni;Alice Boo;Simone Furini;T. Gorochowski;Olivier Borkowski;Y. Ladak;A. Awan;Charlie Gilbert;G. Stan;T. Ellis