'Multilevel modelling of neuronal function: combining metabotropic pathways and membrane excitability in silico for the quick screening of drugs'

“神经元功能的多级建模：在计算机中结合代谢途径和膜兴奋性以快速筛选药物”

• 批准号: 2206089
• 金额: --
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2206089
• 关键词: Multilevel; modelling; neuronal; function; combining

Neurons work as information relays. They integrate information received from the environment at the subcellular scale, generating an appropriate electrophysiological response. The understanding of electrical processes happening at the plasma membrane level has been clarified by Hodgkin and Huxley, combining experimental recordings with mathematical modelling of equivalent electrical circuits. However, quantitative models describing the effects of cell signalling on the cellular response are lacking.Adenosine is a neurotransmitter binding both A1 (Gi) and A2 (Gs) receptors. A1 and A2 stimulation results in altered neuronal excitability via K+ permeability changes. In this project, we aim to combine a mathematical model of adenosine receptor signal transduction (Tindall) with the Hodgkin-Huxley model of a neuron (Tsaneva-Atanasova), to generate an in-silico unified model, predictive of neuronal function following exposure to chemical stimulation. We will test the predictive capability of the model with whole-neuron patch-clamp recordings (Tamagnini).The mathematical models formulated during the project will utilise the theory of differential equations (ordinary and partial) solved and analysed both numerically and analytically (e.g. dynamical systems theory, asymptotic methods). The successful candidate will be involved in the design and undertaking of wet laboratory experiments for testing and informing the mathematical models. The candidate will be trained in whole cell patch clamp techniques, with the value added of performing dynamic clamp to test the validity of the model on to a live system. The design and validation of the unified model is aimed at providing a tool (software) for the pre-clinical, ethical, high-throughput screening of newly developed molecules with biological activity and their action on neuronal function.

神经元起着信息中继的作用。它们在亚细胞尺度上整合从环境中接收到的信息，产生适当的电生理反应。霍奇金和赫胥黎结合实验记录和等效电路的数学建模，澄清了对质膜水平发生的电过程的理解。腺苷是一种结合A1（Gi）和A2（Gs）受体的神经递质。A1和A2刺激通过K+渗透性变化导致神经元兴奋性改变。在这个项目中，我们的目标是结合联合收割机的腺苷受体信号转导的数学模型（Tindall）与霍奇金-赫胥黎模型的神经元（Tsaneva-Atanasova），以产生一个计算机统一的模型，预测神经元功能暴露于化学刺激。我们将使用全神经元膜片钳记录（Tamagnini）测试模型的预测能力。项目期间制定的数学模型将利用微分方程（普通和偏微分方程）理论进行数值和解析求解和分析（例如动力系统理论，渐近方法）。成功的候选人将参与设计和进行湿实验室实验，以测试和通知数学模型。候选人将接受全细胞膜片钳技术的培训，并增加执行动态钳的价值，以测试模型在活体系统上的有效性。统一模型的设计和验证旨在为临床前，伦理，高通量筛选新开发的具有生物活性的分子及其对神经元功能的作用提供工具（软件）。