Probabilistic approaches to optimize synthetic organisms

优化合成生物体的概率方法

• 批准号: RGPIN-2018-05085
• 负责人: Hallett, MIchael
• 金额: $ 5.03万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=745729
• 关键词: Probabilistic; approaches; optimize; synthetic; organisms

Synthetic biology is based on the “forward engineering” of biological systems: from a toolbox of well-defined genetic components, the basic building blocks are used to create combinations not present in nature. Synthetic biology is increasingly used when the synthesis of a complex molecule is either prohibitively expensive or impossible to create in the laboratory or factory. The design of novel or re-engineered enzymatic pathways into microbes promises a cost effective alternative. However this remains challenging even within genetically tractable organisms such as baker's yeast Saccharomyces cerevisiae. The resultant systems are often functional but far from optimal.Systems Biology is based on the “reverse engineering” of biological systems. Here high-throughput -omic technologies (eg next generation sequencing) are typically used to profile a large number of samples of a target organism. Often the goal is to infer from these profiles the underlying biological machinery. The use of high-throughput profiling and bioinformatic analysis together provide a more holistic molecular perspective of the state of an organism by detailing the state of individual genes, pathways and processes.This project will build systems biology models to aid in the creation of synthetic organisms. Probabilistic models are used to build an “intermediary” between genotype (genetic perturbations to induce a trait) and phenotype (expression of the target trait). The so-called transcriptional signature network (TSN) allows us to reason about the global state of the synthetic organism as we iteratively perturb it to increase efficacy. The ability to consider the molecular state of the system at each design step will allow us to shift seamlessly between forward and reverse engineering. We show proof of concept for our approach by optimizing an existing synthetic yeast system that produces benzylisoquinoline alkaloids, a class of plant secondary metabolites that includes antitussive, antibacterial, and antineoplastic drugs. Yields of the current system are too low for practical applications, a common situation in synthetic biology. A semi-rational directed evolutionary approach is used with our TSN to systematically perturb the synthetic system and improve alkaloid yields. The outcomes of this highly interdisciplinary research will have many benefits to Canada. Our short term goals to optimize existing microbial factories for biofuels and drugs could have positive commercial impact. Moreover, these unique, complicated compounds are of interest in research (eg alternative opioids for pain). The longer term intellectual challenge is to build predictive models that will make microbial factory construction routine and automated. This could open entire new research and industrial opportunities.

合成生物学基于生物系统的“正向工程”：从定义明确的遗传成分的工具箱中，基本的构建块用于创建自然界中不存在的组合。当复杂分子的合成被禁止在实验室或工厂中创造出不可能创造的复杂分子时，越来越多地使用合成生物学。将新颖或重新设计的酶促途径设计为微生物有望有效的替代方案。但是，即使在诸如贝克酵母糖酵母酿酒酵母等遗传处理生物中，这仍然受到挑战。所得系统通常具有功能性，但远非最佳系统。系统生物学基于生物系统的“反向工程”。这里通常使用高通量 - 组技术（例如下一代测序）来介绍大量目标生物的样本。通常，目标是从这些基本生物机械中推断出来。通过详细详细介绍各个基因，途径和过程的状态，将高通量分析和生物信息学分析共同提供了对生物体状态的更全面的分子观点。该项目将构建系统生物学模型，以帮助创建合成生物体。概率模型用于在基因型（诱导性状的遗传扰动）和表型（目标性状的表达）之间建立“中间”。所谓的转录签名网络（TSN）使我们能够对合成组织的全球状态进行推论，因为我们迭代地扰动它以提高效率。在每个设计步骤中考虑系统的分子状态的能力将使我们能够在前进和逆向工程之间无缝移动。我们通过优化产生苄基喹啉生物碱的现有合成酵母菌系统（包括抗毒性，抗菌和抗肿瘤药物）的现有合成酵母菌系统来展示我们的方法证明。对于实际应用，当前系统的产量太低，这是合成生物学的常见情况。我们的TSN使用半理性的定向进化方法系统地扰动合成系统并改善生物碱产量。这项高度跨学科研究的结果将对加拿大带来许多好处。我们优化生物燃料和药物现有微生物因素的短期目标可能会产生积极的商业影响。此外，这些独特的，复杂的化合物在研究中引起了人们的关注（例如，替代阿片类药物的疼痛）。长期的智力挑战是建立预测模型，以使微生物工厂的施工和自动化。这可以打开整个新的研究和工业机会。