'13TSB_SynBio' Genome-scale metabolic modelling to optimise high value microbial manufacturing

“13TSB_SynBio”基因组规模代谢模型可优化高价值微生物制造

• 批准号: BB/L004437/1
• 负责人: Stephen Oliver
• 金额: $ 24.75万
• 依托单位: University of Cambridge
• 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=BB%2FL004437%2F1
• 关键词: 13TSB_SynBio; Genome; scale; metabolic; modelling

We are beginning to see a shift in focus from of large-scale biotechnology away from biofuels (such as bioethanol and biobutanol) towards fine and bulk chemicals (e.g. succinic acid and propanediol) production. Methacrylic acid is the most widely used starting material to produce methylmethacrylate (MMA) for the manufacture of Perspex (also known as Lucite). MMA is a primary building block for the methacrylates industry and supports an extensive supply chain globally. All of this material is currently produced from petrochemical feedstocks. As a major player in the production of MMA (24% of total global production) one of the participating companies in this project, Lucite, is committed to developing a route based on renewable feedstocks. This project aims to engineer an industrial microbe to produce commercially viable amounts of a methacrylate precursor. The diversion of a large proportion of a microbe's resources to the generation of a product of no benefit to itself generally involves extensive realignment of its metabolic network. Scientists at the University of Cambridge will construct a computer model of the microbe's metabolism and use this in simulations to guide the engineering of the microbe by the third partner in this grouping, Ingenza. Together the three partners will optimise and scale-up the process. Both the general methodologies followed and specific results obtained will be of interest to biotechnologists, since they will be applicable to develop routes to many other bulk and speciality chemicals using renewable feedstocks. Success in a bulk chemical-based project of this type will help to promote the UK's aim to be at the leading edge of a Bio-based economy. Success in this project will also give impetus, where needed, to other chemical producers to engage in industrial biotechnology and the development of Bio-based processes. Furthermore, there will be extensive benefits throughout the supply chain associated with Lucite's activities, thus providing more general benefits across the UK economy.

我们开始看到大规模生物技术的重点从生物燃料（例如生物乙醇和生物丁醇）转向精细和大宗化学品（例如琥珀酸和丙二醇）生产。甲基丙烯酸是生产甲基丙烯酸甲酯 (MMA) 的最广泛使用的原材料，用于制造有机玻璃（也称为有机玻璃）。 MMA 是甲基丙烯酸酯行业的主要组成部分，为全球广泛的供应链提供支持。目前所有这些材料都是由石化原料生产的。作为 MMA 生产的主要参与者（占全球总产量的 24%），Lucite 是该项目的参与公司之一，致力于开发基于可再生原料的路线。该项目旨在设计一种工业微生物来生产商业上可行数量的甲基丙烯酸酯前体。将微生物的大部分资源转移到生产对自身无益的产品中，通常涉及对其代谢网络的广泛重新调整。剑桥大学的科学家将构建微生物新陈代谢的计算机模型，并在模拟中使用该模型来指导该组的第三个合作伙伴 Ingenza 的微生物工程设计。三个合作伙伴将共同优化和扩大该流程。所遵循的一般方法和获得的具体结果都将引起生物技术人员的兴趣，因为它们将适用于开发使用可再生原料的许多其他散装和特种化学品的路线。此类大宗化学品项目的成功将有助于促进英国实现生物经济领先地位的目标。该项目的成功还将在需要时推动其他化学品生产商参与工业生物技术和生物基工艺的开发。此外，与 Lucite 活动相关的整个供应链都将获得广泛的利益，从而为整个英国经济带来更普遍的利益。

项目成果

Extension of the yeast metabolic model to include iron metabolism and its use to estimate global levels of iron-recruiting enzyme abundance from cofactor requirements.

酵母代谢模型的扩展包括铁代谢及其用于根据辅因子需求估计铁吸收酶丰度的全球水平。

• DOI: http://dx.10.17863/cam.34965
• 发表时间: 2019
• 作者: Dikicioglu D

The metabolome 18 years on: a concept comes of age.

18 年后的代谢组：一个概念逐渐成熟。

• DOI: http://dx.10.1007/s11306-016-1108-4
• 发表时间: 2016
• 期刊: Official journal of the Metabolomic Society
• 作者: Kell DB

Membrane transporter engineering in industrial biotechnology and whole cell biocatalysis.

工业生物技术和全细胞生物催化中的膜转运蛋白工程。

• DOI: 10.1016/j.tibtech.2015.02.001
• 发表时间: 2015-04-01
• 期刊: Trends in biotechnology
• 影响因子: 17.3
• 作者: D. Kell;Neil Swainston;Pınar Pir;S. Oliver
• 通讯作者: S. Oliver

Extension of the yeast metabolic model to include iron metabolism and its use to estimate global levels of iron-recruiting enzyme abundance from cofactor requirements.

酵母代谢模型的扩展包括铁代谢及其用于根据辅因子需求估计铁吸收酶丰度的全球水平。

• DOI: http://dx.10.1002/bit.26905
• 发表时间: 2019
• 期刊: Biotechnology and bioengineering
• 影响因子: 3.8
• 作者: Dikicioglu D

CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology.

CamOptimus：一种利用复杂的适应性进化来优化生物技术实验和过程的工具。

• DOI: http://dx.10.1099/mic.0.000477
• 发表时间: 2017
• 期刊: Microbiology (Reading, England)
• 作者: Cankorur