Therapy design for neurodegenerative diseases via nonlinear control

神经退行性疾病是一种慢性神经系统疾病，由大脑、脊髓或周围神经系统等不同神经系统区域的神经细胞进行性死亡引起。最常见的神经退行性疾病包括阿尔茨海默病(AD)、帕金森氏病(PD)和多发性硬化症(MS)，其中阿尔茨海默氏症是全球中老年人口智力功能受损(痴呆)的主要原因。目前神经退行性疾病的治疗技术对患者生活质量的积极影响有限，大多数针对的是症状，而不是疾病的实际发生和发展。最重要的是，神经退行性疾病在患者的临床表型上表现出很大的差异性，从而使目前的通用治疗方法不足，个性化治疗方法的发展非常有益。为了了解神经系统的复杂行为和有效地了解神经退行性疾病的潜在机制，人们开发了多种数学模型来描述导致细胞死亡的高度相互关联的事件序列。其中许多模型使用了60年前开发的经过测试和科学可靠的Hodgkin-Huxley(HH)模型作为起点。在这个项目中，我们的目标是将非线性控制方法应用到这样的数学模型中，通过计算识别控制输入，这些控制输入将导致疾病出现的变量驱动到健康的操作区域。从使用原始HH模型的单个神经元的放电过程出发，我们将通过识别与神经元放电失败相对应的参数来模拟细胞死亡。然后，我们将通过创建神经元网络来模拟神经元退化的进程。逐步地，我们将使用先进的非线性控制方法来控制单个患病神经元的放电，并最终使用共识控制理论来控制整个神经元网络。同时，我们将探索AD、PD和MS之间的关系，并将这三个因素整合到同一组模型中，目的是简洁地描述它们的发展过程。最后，我们将通过考虑神经退行性疾病的变异性来研究治疗个性化的方面，这些变异性直接反映到模型参数中。潜在地，该项目将有助于进一步了解神经退行性疾病的进展，以及在什么条件下可以停止甚至逆转这一机制。它将提供一个计算框架，以研究和可视化神经退行性疾病，以及确定一种可能的治疗方法，以后可以与当前的药物产品或其他技术(如大脑深部刺激)联系起来。这个项目可能会导致一个平台，它将使用患者的数据作为输入，在虚拟患者上模拟他们的病例，从而确定适当的和个性化的行动过程。与EPSRC研究领域相关：-控制工程-非线性系统

Neurodegenerative diseases are chronic nervous system medical conditions that are caused by the progressive death of nerve cells of the various nervous system regions, such as the brain, the spinal cord or the peripheral nervous system. Among the most common neurodegenerative diseases are Alzheimer's disease (AD), Parkinson's disease (PD) and Multiple Sclerosis (MS), with Alzheimer's being the primary cause of impaired mental functionality (dementia) within the middle aged and aged population worldwide. Current treatment techniques for neurodegenerative diseases have limited positive effect in the patients' quality of life with most targeting the symptoms rather than the actual initiation and progression of the disease. On top of that, neurodegenerative diseases exhibit great variability in the patients' clinical phenotypes, thus making the current generalized treatments insufficient and the development of personalized treatment methods highly beneficial. In an effort to understand the complex behavior of the nervous system and effectively the mechanisms underlying the neurodegenerative diseases, multiple mathematical models to describe the sequence of highly interconnected events leading to cell death have been developed. Many of these models use the tested and scientifically reliable Hodgkin - Huxley (HH) model developed 60 years ago as starting point. In this project, we are aiming at applying nonlinear control methods to such mathematical models to computationally identify the control inputs that drive the variables responsible for the appearance of the disease to a healthy region of operation. Starting from the process of firing of a single neuron using the original HH model, we will simulate the cell death by identifying the parameters that correspond to a failed neuronal firing. Then, we will simulate the progression of neuron degeneration by creating a network of neurons. Progressively, we will control the firing of a single diseased neuron using advanced nonlinear control methods and eventually control the whole network of neurons using consensus control theory. In parallel, we will explore the relation between AD, PD and MS and integrate all three in the same set of models with the goal of compactly describing their progression. Finally, we will investigate the aspect of treatment personalization by taking into consideration the variability of the neurodegenerative diseases, that directly reflect into the model parameters. Potentially, this project will contribute to further understanding the progression of neurodegenerative diseases and the conditions under which the mechanism can be stopped or even reversed. It will provide a computational framework to study and visualize the neurodegenerative diseases as well as identify a possible treatment that can be later linked to current pharmaceutical products or other techniques such as deep brain stimulation. This project might lead to a platform, that will use patients' data as an input to simulate their case on a virtual patient, that will determine the appropriate and personalized course of action. Relevance to EPSRC research area:- Control Engineering- Nonlinear Systems