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.

合成生物学通过将工程设计原则严格应用于我们处理生物系统的方式，加速了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、生物材料、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、建模和分析。通过使用这个工程框架，研究人员的目标是预测工程生物系统将如何运作。尽管取得了许多成功，但当新的合成遗传信息添加到这些宿主细胞中时，仍然很难预测工程细胞的行为。合成生物学前向工程失败率高的关键是缺乏关于部件和设备的高质量数据。如果没有一个完整的数据集来报告宿主细胞中生物部分的性能，就很难预测它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的工作流程来解决这一问题，以便在细菌宿主细胞中实施数千个不同的部件和设备时获得更丰富的数据集。为了实现这一目标，将建立一个筛选工作流程，首次将体外原型制作与体内分析和质谱分析相结合，以同时捕获合成生物学设备设计如何影响基因表达、表达负荷和宿主细胞健康、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，理想情况下将导致对诸如增长率和突变可能性等性能特征的稳健的计算机预测。在这个项目中，将专门为这项任务建立模型，并将引入质谱学作为一种最先进的测量工具。这两个领域都是合成生物学的新领域。虽然这项研究将产生非常广泛的影响，并加速合成生物学未来的许多不同应用，但在这个项目中，它将专门用于解决高价值生物材料的应用，如果没有这项工作提供的坚实的工程基础，这一应用不太可能成功。在该项目的这一部分，对基因表达和生长的预测将用于在工程微生物和微生物联合体中表达不同功能蛋白质的文库，然后这些蛋白质可以聚合在一起，生成具有可编程催化和材料特性的多蛋白生物材料。例如，通过将蚕丝蛋白与生物聚合物中的脂肪酶结合，可以实现自清洁织物等先进材料。虽然这项材料工作旨在展示在该项目中开发的基本方法，但它无疑将在从时尚、制造到医学的各种商业、工程和研究部门带来许多未来令人兴奋的应用和新行业。

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

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