ANALYSIS OF BIOCHEMICAL NETWORK MODELS USING ROBUST CONTROL THEORY

在系统生物学的新领域中，生物化学网络的精确数学模拟模型的发展已经变得至关重要，因为由此产生的模拟可以用来测试生物体对各种变化和扰动的反应，促进可检验假设的产生。在最近的文献中，模型鲁棒性已被确定为此类模型有效性的关键指标。特别是，人们已经广泛承认，由于生物系统的关键动力学特性往往是非常强大的（不严格地说，相对不受环境条件的广泛变化）的数学模型来表示他们也必须反映这一现实，即他们必须重现所需的动态鲁棒。在这种情况下，评估模型的鲁棒性，因此需要量化的相对（不）灵敏度的模型的动态变化，其参数，结构和/或环境。在控制工程领域中，保证反馈控制系统的鲁棒性是至关重要的，已经开发了一些强大的技术来分析复杂非线性系统的鲁棒性。从这些技术开始，拟议的项目将开发新的鲁棒性分析技术，这些技术专门针对用于模拟生物化学网络的动力系统类型。这些技术将被应用到许多不同的生化网络模型，以帮助进一步验证或无效这些模型，并为了提供如何鲁棒性分析工具可以应用到这样的系统的现实演示。还将分析模型不确定性对模型敏感性分析的影响，目的是制定稳健敏感性分析的新方法。最后，通过工程和生物科学背景的研究人员之间的密切合作，（a）鲁棒性分析可以用作生物化学网络模型的开发和改进的工具，（B）实验室实验可以用来验证或反驳来自数学分析和模拟的结果，将详细研究的方式。

In the new field of Systems Biology, the development of accurate mathematical simulation models of biochemical networks has become of critical importance, since the resulting simulations can be used to test the response of organisms to various changes and perturbations, facilitating the generation of testable hypotheses. In the recent literature, model robustness has been identified as a key indicator of validity for such models. In particular, it has been widely acknowledged that, since key dynamical properties of biological systems are often extremely robust (loosely speaking, relatively unaffected by wide variations in environmental conditions) the mathematical models developed to represent them must also reflect this reality, i.e. they must reproduce the required dynamics robustly. In this context, evaluating model robustness thus requires the quantification of the relative (in)sensitivity of the model's dynamics to changes in its parameters, structure and/or environment. In the field of control engineering, where ensuring robustness of feedback control system is of paramount importance, several powerful techniques have been developed with which to analyse the robustness of complex nonlinear systems. Starting from these techniques, the proposed project will develop new robustness analysis techniques which are specifically tailored to the types of dynamical systems used to model biochemical networks. These techniques will be applied to a number of different biochemical network models, to assist in further validating or invalidating these models, and in order to provide realistic demonstrations of how robustness analysis tools may be applied to such systems. The impact of model uncertainty on the analysis of model sensitivity will also be analysed, with the aim of developing new methods for robust sensitivity analysis. Finally, through close collaboration between researchers from engineering and biological science backgrounds, the ways in which (a) robustness analysis may be used as a tool for the development and improvement of biochemical network models, and (b) laboratory experiments can be used to verify or refute results derived from mathematical analysis and simulations, will be investigated in detail.