Engineering Fellowships for Growth: Advanced synthetic biology measurement to enable programmable functional biomaterials

增长工程奖学金：先进的合成生物学测量，以实现可编程功能生物材料

• 批准号: EP/M002306/1
• 负责人: Thomas Ellis
• 金额: $ 122.81万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM002306%2F1
• 关键词: Engineering; Fellowships; Growth; Advanced; synthetic

Synthetic biology accelerates 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, biomaterials, biomedicine, drug production and food technology. Crucial to developing and applying synthetic biology is the rigorous quantification, modelling and analysis of synthetic biology designs. By using this engineering framework researchers aim to predict how engineered biological systems will operate.Despite many successes, it is still difficult to predict how engineered cells behave when new synthetic genetic information is added to these host cells. Key to the high failure rates in forward engineering in synthetic biology is the lack of high-quality data available on parts and devices. Without a holistic dataset reporting on performance of a biological part in its host cell, it is difficult to predict how it will behave when included in complex designs. The work proposed in this project seeks to address this by developing a novel workflow to obtain a richer-dataset on thousands of different parts and devices as they are implemented in bacterial host cells. To achieve this goal, a screening workflow will be established, that for the first time incorporates in vitro prototyping, with in vivo assaying and mass-spectrometry profiling to simultaneously capture how synthetic biology device design affects gene expression, expression load and host cell health, energy and growth. Measuring these multiple parameters in parallel will greatly enrich predictive models and ideally will lead to robust in silico predictions on performance characteristics such as growth rate and mutation likelihood. In this project, modelling will be developed specifically for this task and mass spectrometry will also be introduced as a state-of-the-art measurement tool. Both are new frontiers for synthetic biology.While this research will have a very wide impact and accelerate the many different future applications of synthetic biology, in this project it will be specifically used to tackle a high-value biomaterials application that would be unlikely to succeed without the strong engineering foundations this work provides. For this part of the project, predictions of gene expression and growth will be used to express a library of different functional proteins in engineered microbes and microbial consortia that can then be polymerised together to generate polyprotein biomaterials with programmable catalytic and material properties. For example, by combining silk proteins with lipase enzymes in biological polymers, advanced materials such as self-cleaning fabrics can be realised. While this materials work is intended as a showcase for the foundational methods developed in this project, it will no doubt lead to many future exciting applications and new industries in a rich variety of commercial, engineering and research sectors, from fashion and manufacturing to medicine.

合成生物学通过严格地将工程设计原理应用于我们与生物系统的方式中，加速了新生物技术的研究和开发。合成生物学的最突出的应用是，在能源生产，生物材料，生物医学，药物生产和食品技术等领域的新有效使用中，像微生物（如微生物）这样的生物的合理修饰和重新设计。对于合成生物学设计的严格定量，建模和分析，对于发展和应用合成生物学至关重要。通过使用该工程框架研究人员旨在预测工程生物系统将如何运作。尽管取得了许多成功，但仍很难预测工程细胞在将新的合成遗传信息添加到这些宿主细胞中时的行为。合成生物学中远期工程的高失败率的关键是缺乏零件和设备上可用的高质量数据。如果没有整体数据集报告其宿主细胞中生物部分性能的性能，则很难预测其在复杂设计中的表现。该项目提出的工作旨在通过开发一种新颖的工作流程来解决这一问题，以在细菌宿主细胞中实施，以在数千个不同的部分和设备上获得更丰富的数据。为了实现这一目标，将建立一个筛选工作流程，该工作流程首次结合体外原型，并具有体内测定和质谱分析，以同时捕获合成生物学设备设计如何影响基因表达，表达负载和宿主细胞健康，能量和能量，能量和生长。并行测量这些多个参数将极大地丰富预测模型，理想情况下，对硅酸盐的性能特征（例如生长速率和突变可能性）的预测将有力。在该项目中，将专门为此任务开发建模，并且还将作为最新的测量工具引入质谱法。两者都是合成生物学的新领域。虽然这项研究将产生很大的影响并加速合成生物学的未来许多不同的应用，但在该项目中，它将被专门用于应对高价值的生物材料应用程序，而没有强大的工程基础，这项工作就不太可能成功。对于该项目的这一部分，基因表达和生长的预测将用于表达工程微生物和微生物伴侣中不同功能蛋白的库，然后可以将其一起聚合在一起，以产生具有可编程催化和材料特性的多蛋白生物材料。例如，通过将丝绸蛋白与脂肪酶混合在生物聚合物中，可以实现高级材料，例如自我清洁织物。尽管该材料工作旨在作为该项目开发的基础方法的展示，但毫无疑问，它将在各种商业，工程和研究领域，从时尚和制造业到医学，在各种各样的商业，工程和研究领域中提供许多令人兴奋的应用程序和新行业。

项目成果

Horizontal gene flow into Geobacillus is constrained by the chromosomal organization of growth and sporulation

• DOI: 10.1101/381442
• 发表时间: 2018-08
• 期刊: bioRxiv
• 作者: Alexander Esin;T. Ellis;Tobias Warnecke

What is synthetic genomics anyway?

• DOI: 10.1042/bio04103006
• 发表时间: 2019-01-01
• 期刊: The Biochemist
• 作者: Ellis, T.

Overloaded and stressed: whole-cell considerations for bacterial synthetic biology

• DOI: 10.1016/j.mib.2016.07.009
• 发表时间: 2016-10-01
• 期刊: CURRENT OPINION IN MICROBIOLOGY
• 影响因子: 5.4
• 作者: Borkowski, Olivier;Ceroni, Francesca;Ellis, Tom
• 通讯作者: Ellis, Tom

SYNTHETIC BIOLOGY. On the record with E. coli DNA.

合成生物学。

• DOI: 10.1126/science.aah4438
• 发表时间: 2016
• 期刊: Science (New York, N.Y.)
• 作者: Borkowski O

Host-aware synthetic biology

• DOI: 10.1016/j.coisb.2019.03.001
• 发表时间: 2019-04-01
• 期刊: Current Opinion in Systems Biology
• 影响因子: 3.7
• 作者: Boo, Alice;Ellis, Tom;Stan, Guy-Bart
• 通讯作者: Stan, Guy-Bart