Parameter identification with optimal experimental design for engineering biology

工程生物学有可能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，这些产品的工程化由于宿主微生物（底盘）的天然生物学和工程化的遗传系统或代谢途径之间的相互作用而变得复杂。这导致性能差，其范围可以从预期行为的差异、宿主生长差或甚至工程功能的完全失效。最近，数学模型和计算机辅助设计工具已经产生，使这些相互作用，在项目设计阶段的帐户。这些方法被称为“宿主感知”，使合成生物学家和生物技术学家能够产生“宿主友好”的设计。到目前为止，这些方法只针对学术实验室的主力E。大肠杆菌，这限制了它们在工业中的应用，由于其他有益的生物学特性，工业中使用各种微生物。将这些框架扩展到其他工业相关的生物体是具有挑战性的，因为缺乏可用的数据和缺乏实验方案，使有效的数据生成。这个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，该团队将对由复杂的非线性常微分方程模型组成的宿主感知设计框架进行严格分析，并建立新的准确有效的工具，以便从稀疏数据中对复杂的微生物生长模型进行稳健的参数化。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，以使这些宿主感知设计框架能够为工业相关的微生物开发。该项目将使未来一代新的工业相关的“主机感知”的计算机辅助设计工具所需的快速和有效的工程微生物细胞工厂。

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.