Mathematical modelling and experiment design for hypothesis testing and model validation for PSEUDOMONAS PUTIDA (Partner 11 of SYSMO)

白色生物技术（利用微生物的催化特性用于工业应用）越来越被认为是欧洲必然要进入的知识经济的支柱之一。该项目的目的是开发必要的知识库和材料和概念（计算）资源，以建立土壤细菌恶臭假单胞菌作为将生物活动纳入整个工业相关过程的工具。数学建模已成为大多数工程学科的常用方法。一般来说，这种方法产生的模型可以非常准确地描述系统。然而，从这些模型的预测是没有意义或有用的，除非该模型可以验证收集的实验数据。为了实现这一点，我们必须估计模型中的某些参数，以使模型预测与收集的数据/识别问题相匹配。本提案中的工作旨在将建模、实验设计和验证以及控制和优化整合到一个单一的框架中，从而提高生产率并降低实验成本。这三种研究工具的整合代表了一种独特的，新颖的，跨学科的方法来解决复杂的研究和基于模型的控制和优化的文化过程的工业问题。它建立在作者进行的现有工作的基础上，并寻求整合开发的各种模型，最终目标是基于模型的控制和优化培养过程。

White Biotechnology (the exploitation of the catalytic properties of microorganisms for industrial applications) is increasingly recognized as one of the pillars of the knowledge-base Economy that Europe is bound to drive into. The purpose of this project is to develop the necessary knowledge base and the material and conceptual (computational) resources to establish the soil bacterium Pseudomonas putida as the vehicle for implementing biological activities into a whole range of industrially-related processes. Mathematical modelling has become a common approach in most engineering disciplines. In general, this approach yields models that give a very accurate description of the system. However, predictions from such models are not meaningful or useful unless the model can be validated against collected experimental data. To achieve this, we must estimate certain parameters within the model to match the model predictions with collected data / the identification problem. The work in this proposal sets out to integrate modelling, experiment design and validation, and control and optimisation into a single framework that would lead to increased productivity and reduced experimental costs. The integration of these three research tools represents a unique, novel, and interdisciplinary approach to addressing the complicated research and industrial problem of model-based control and optimisation of the culture processes. It builds on existing work carried out by the authors and seeks to integrate various models developed with the ultimate goal of model-based control and optimisation of culture processes.