Computational modelling of dopaminergic networks and exploring the effects of protein aggregates on the function of these neuronal circuits

多巴胺能网络的计算模型并探索蛋白质聚集体对这些神经元回路功能的影响

• 批准号: 1782613
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1782613
• 关键词: Computational; modelling; dopaminergic; networks; exploring

This project will bridge the fields of experimental neuroscience and theoretical computational mathematical modelling. Patch Clamping will be used to explore the way in which dopamine neurons are connected and how they respond to protein aggregation. The project will also expand on previous neuronal microcircuit network work, both in the hippocampus and also of dopaminergic neurons in the SNc. Acquisition of such network information will be greatly assisted by the production of simplified computational models, which allow the production of large networks without requiring large amount of computational power.In order to translate this initial electrophysiology data into a network model, matlab will be used to generate dynamic IV curves. These can then be compared across different cell types and conditions. We propose to modify the rEIF model, which is already published for pyramidal neurons in the hippocampus, but does not accurately describe the electrophysiological properties of dopaminergic neurons. It will need to be altered using the electrophysiology data to give accurate spike prediction and hence a reliable model. Our initial experimental plan is to inject a-syn oligomers (Kaufman et al) into dopaminergic neurons and measure early changes in electrophysiology of the neurons over time. We also plan to inject tau oligomers into pyramidal cells to see how the aggregation affects the neuronal responses. As protein aggregation is a problem that develops primarily with increasing age, different ages of mice will be used to compare the response to the addition of oligomers.

该项目将连接实验神经科学和理论计算数学建模领域。膜片钳将用于探索多巴胺神经元的连接方式以及它们如何对蛋白质聚集做出反应。该项目还将扩展之前的神经元微电路网络工作，包括海马体和SNc中的多巴胺能神经元。简化的计算模型的产生将极大地帮助这些网络信息的获取，这允许在不需要大量计算能力的情况下产生大型网络。为了将这些初始电生理数据转换为网络模型，将使用matlab生成动态IV曲线。然后可以在不同的细胞类型和条件下进行比较。我们建议修改的rEIF模型，这是已经公布的海马锥体神经元，但不能准确地描述多巴胺能神经元的电生理特性。需要使用电生理学数据对其进行更改，以提供准确的尖峰预测，从而提供可靠的模型。我们最初的实验计划是将α-syn寡聚体（考夫曼等人）注射到多巴胺能神经元中，并测量神经元电生理学随时间的早期变化。我们还计划将tau寡聚体注射到锥体细胞中，以观察聚集如何影响神经元反应。由于蛋白质聚集是主要随着年龄增长而发展的问题，因此将使用不同年龄的小鼠来比较对添加寡聚体的反应。