Parameter identification with optimal experimental design for engineering biology

工程生物学优化实验设计的参数识别

• 批准号: EP/Y00342X/1
• 负责人: Alexander Darlington
• 金额: $ 20.86万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY00342X%2F1
• 关键词: Parameter; identification; optimal; experimental; design

Engineering biology has the potential to generate new disruptive products and to revolutionise our approach to many societal problems. However, at present, the engineering of these products is complicated by interactions between the host microbe's (chassis') natural biology and the engineered genetic system or metabolic pathway. This results in poor performance which can range from differences in expected behaviour, poor host growth or even complete failure of the engineered function. Recently, mathematical models and computer aided design tools have been produced which enable these interactions to be accounted for during the project design phase. These approaches are termed "host-aware" and enable synthetic biologists and biotechnologists to produce "host-friendly" designs. To date, these approaches have only been developed for the academic lab workhorse E. coli which limits their application in industry who use a variety of microbes due to other beneficial biological properties. Extending these frameworks to other industrially relevant organisms is challenging due to the lack of available data and the lack of experimental protocols which enable efficient data generation. This pilot project establishes an interdisciplinary global research team to solve this problem. First the team will carry out a rigorous analysis of the host-aware design framework which is composed of complex nonlinear ordinary differential equation models and establish new accurate and efficient tools to enable robust parametrisation of complex microbial growth models from sparse data. Working closely with industrial partners, the team will then develop an optimal experimental design approach which enables scientists to determine what experiments need to be conducted to enable these host-aware design frameworks to be developed for industrially relevant microbes. This project will enable the future generation of new industrially relevant "host-aware" computer aided design tools needed for fast and efficient engineering of microbial cells factories.

工程生物学有潜力产生新的颠覆性产品，并彻底改变我们解决许多社会问题的方法。然而，目前，由于宿主微生物（底盘）的自然生物学与工程遗传系统或代谢途径之间的相互作用，这些产品的工程变得复杂。这会导致性能不佳，包括预期行为差异、宿主生长不良甚至工程功能完全失败。最近，数学模型和计算机辅助设计工具的出现使得这些相互作用能够在项目设计阶段得到考虑。这些方法被称为“宿主感知”，使合成生物学家和生物技术人员能够产生“宿主友好”的设计。迄今为止，这些方法仅针对学术实验室主力大肠杆菌而开发，由于其他有益的生物特性，这限制了它们在使用各种微生物的工业中的应用。由于缺乏可用数据以及缺乏能够有效生成数据的实验方案，将这些框架扩展到其他工业相关生物体具有挑战性。该试点项目建立了一个跨学科的全球研究团队来解决这个问题。首先，团队将对由复杂非线性常微分方程模型组成的主机感知设计框架进行严格分析，并建立新的准确高效的工具，以实现从稀疏数据中对复杂微生物生长模型进行鲁棒参数化。然后，该团队将与工业合作伙伴密切合作，开发一种最佳的实验设计方法，使科学家能够确定需要进行哪些实验，以便为工业相关微生物开发这些宿主感知的设计框架。该项目将使下一代新型工业相关的“主机感知”计算机辅助设计工具成为快速高效的微生物细胞工厂工程所需的工具。