A coarse-grained^M modelling approach for applications in Metabolic Engineering

用于代谢工程应用的粗粒度^M建模方法

• 批准号: 522699409
• 负责人: Professor Dr.-Ing. Andreas Kremling
• 金额: --
• 依托单位: Professur für Systembiotechnologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/522699409?language=en
• 关键词: coarse; grained; modelling; approach; applications

To improve biotechnological processes, mathematical models come into play for a better understanding of the inner workings of cells. Although various modelling approaches are available and a high number of models are published, the demand for a quantitative and a dynamic description of cellular processes is still high and not met by all approaches. Also, quantitative data is now available for different cellular levels like proteome, transcriptome, and metabolome that should be integrated into these models. The proposal's overall aim is to develop coarse-grainedM, that is, a framework for dynamic coarse-grained models with a modular structure in a very general form, such that applications to different cellular systems and process designs can be made quickly. Modularity in the context of the proposal means that, starting from a simple structure of a coarse-grained model, functional modules are defined that specify parts of the overall network depending on the problem formulation. To set up these models, a combined approach with modelling based on first principles and a data driven approach with classical elements of machine learning tools will be used. Applications in Metabolic Engineering are addressed such as by-product formation, and heterologous protein production. In addition, also defined mixed cultures are considered where two strain variants are mutual linked for an efficient production of an interesting product.

为了改善生物技术过程，数学模型开始发挥作用，以更好地了解细胞的内部工作。尽管可以使用各种建模方法并发布了大量模型，但是对定量的需求和细胞过程的动态描述仍然很高，并且没有通过所有方法来满足。同样，现在可以将定量数据用于不同的细胞水平，例如蛋白质组，转录组和代谢组，应集成到这些模型中。该提案的总体目的是开发粗粒，即具有非常通用形式的模块化结构的动态粗粒模型的框架，以便可以快速地应用于不同的蜂窝系统和过程设计。在提案的上下文中，模块化意味着，从粗粒模型的简单结构开始，定义了功能模块，该模块根据问题的表述指定了整体网络的一部分。为了设置这些模型，将使用基于第一原理建模的组合方法，并将使用与机器学习工具的经典元素进行数据驱动的方法。涉及代谢工程中的应用，例如副产品形成和异源蛋白质产生。此外，还考虑了定义的混合培养物，其中两个菌株变体相互联系以有效地生产有趣的产品。