Modelling neuronal trafficking and survival to predict the progression of neurodegenerative diseases

DTP概述：这个mrc资助的博士培训合作伙伴关系（DTP）将尖端的分子和分析科学与数据分析中的创新计算方法结合在一起，使学生能够解决假设主导的生物医学研究问题。这是一个为期4年的课程，第一年包括一系列教学模块和两个基于实验室的研究项目，最终获得跨学科生物医学研究硕士学位。前两个学期包括一系列教学模块，让学生在多学科科学方面打下坚实的基础。学生还将参加一系列由分子、细胞和组织动力学、微生物学和感染、应用生物医学技术、人工智能和数据科学等领域的学术和行业专家主持的大师班。在第三学期和夏季学期，学生在他们选择的实验室进行两个为期11周的研究项目。项目概述：由阿尔茨海默病引起的痴呆症是一个潜伏的过程，对大脑的损害通常不会被注意到，直到认知障碍以明显和可诊断的方式表现出来（例如，记忆丧失）。虽然每个人都会变老，虽然衰老是阿尔茨海默氏症的一个风险因素，但痴呆并不是变老的必然结果。阿尔茨海默病的进展还没有很好地描述，虽然目前还没有治愈阿尔茨海默病的方法，但有一些缓解治疗方法，如果通过早期发现及早实施，效果会更好。防止脑细胞死亡的关键分子是脑源性神经营养因子（BDNF）。在阿尔茨海默氏症中，这种分子的功能慢慢退化，因为它不能有效地穿过我们的脑细胞——导致我们的脑细胞慢慢萎缩和死亡。如果我们能够模拟阿尔茨海默氏症如何引发BDNF功能退化的过程，这可能有助于弄清楚我们的脑细胞复杂网络是如何在痴呆症中开始崩溃的。一种新颖而富有洞察力的模拟这种崩溃的方法是将其视为一个变得不那么可靠的通信网络，也就是说，将大脑建模为一个退化的计算机网络。本项目旨在利用分子通讯模型跟踪阿尔茨海默病中BDNF功能的退化。这可能随后被用来预测我们脑细胞的健康和连通性，这可能有助于更好地描述我们大脑网络崩溃的进程。为了评估模型的准确性，将使用在不同相关条件下施加的细胞培养进行一致的实验验证。学生将需要生成，测试，分析和验证BDNF模型，结合计算建模和实验细胞培养工作。

DTP overview:This MRC-funded doctoral training partnership (DTP) brings together cutting-edge molecular and analytical sciences with innovative computational approaches in data analysis to enable students to address hypothesis-led biomedical research questions. This is a 4-year programme whose first year involves a series of taught modules and two laboratory-based research projects that lead to an MSc in Interdisciplinary Biomedical Research. The first two terms consist of a selection of taught modules that allow students to gain a solid grounding in multidisciplinary science. Students also attend a series of masterclasses led by academic and industry experts in areas of molecular, cellular and tissue dynamics, microbiology and infection, applied biomedical technologies and artificial intelligence and data science. During the third and summer terms students conduct two eleven-week research projects in labs of their choice. Project overview:Dementia caused by Alzheimer's disease is an insidious process, and damage to the brain can often go unnoticed until cognitive impairment manifests itself in an obvious and diagnosable manner (e.g., memory loss). Whilst everyone ages and whilst ageing is a risk factor for Alzheimer's, dementia is not a necessary outcome of growing old. The progression of Alzheimer's disease is not well characterised, and whilst there currently isn't a cure for Alzheimer's, there are mitigating therapeutics which are more effective when implemented sooner rather than later through early detection. A key molecule in preventing our brain cells from dying is Brain-derived neurotrophic factor (BDNF). Under Alzheimer's, this molecule's function slowly degrades as it's unable to move through our brain cells effectively - leading to our brain cells slowly shrinking and dying. If we could model how the process of Alzheimer's initiates the degradation of BDNF's function, this may help bring some clarity towards how the complex network of our brain cells begin to collapse in dementia. A novel and insightful way to model this collapse is as a communication network that becomes less reliable, i.e., modelling the brain as if it were a degrading computer network.This project aims to use molecular communications modelling to follow the deterioration of BDNF's function through Alzheimer's. This may subsequently be used to make predictions on the health and connectivity of our brain cells, which may help better characterise the progression of our brain network collapse. To evaluate the model's accuracy, there will be coinciding experimental validation using cell cultures exerted under varying relevant conditions. The student will be required to generate, test, analyse, and validate the BDNF model, combining both computational modelling and experimental cell culture work.