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.

为了改进生物技术过程，数学模型开始发挥作用，以便更好地理解细胞的内部运作。尽管有各种各样的建模方法，并且发表了大量的模型，但对细胞过程的定量和动态描述的需求仍然很高，并不是所有方法都能满足的。此外，现在可以获得不同细胞水平的定量数据，如蛋白质组、转录组和代谢组，这些数据应该集成到这些模型中。该提案的总体目标是开发粗粒度模型，也就是说，一个具有非常通用形式的模块化结构的动态粗粒度模型框架，这样就可以快速地将应用程序应用于不同的细胞系统和过程设计。提案上下文中的模块化意味着，从粗粒度模型的简单结构开始，定义功能模块，这些模块根据问题表述指定整个网络的各个部分。为了建立这些模型，将使用基于第一原理的建模方法和具有机器学习工具经典元素的数据驱动方法相结合的方法。在代谢工程中的应用，如副产物的形成，异源蛋白的生产。此外，还考虑了混合培养，其中两种菌株变体相互联系，以有效地生产有趣的产品。