Modeling network dynamics of cardiac right atrial ganglionic plexus to enable in silico testing of vagal neurostimulation strategies

对心脏右心房神经节丛的网络动力学进行建模，以实现迷走神经刺激策略的计算机测试

• 批准号: 10208324
• 负责人: Rajanikanth Vadigepalli
• 金额: $ 86.63万
• 依托单位: THOMAS JEFFERSON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10208324
• 关键词: Anatomy; Biophysics; Cardiac; Computer Models; Data; Data Sources; Databases; Dimensions; Electrophysiology (science); Family suidae; Foundations; Ganglia; Heart; Heart Atrium; Heart Diseases; Heterogeneity; Human; Kinetics; Libraries; Medicine; Modeling; Molecular; Nervous system structure; Neurons; Phenotype; Physiology; Practice Guidelines; Proteomics; Rattus; Resources; Sex Differences; Signal Transduction; System; Testing; TimeLine; Transcript; base; combinatorial; computational neuroscience; design; improved; in silico; molecular phenotype; network models; neural circuit; neural network; neuroregulation; paracrine; repository; sex; simulation; species difference; therapy development; transcriptome sequencing; transcriptomics

The primary objective of the project is to develop computational models of neurons and networks of the intrinsic cardiac nervous system (ICN), implement simulations on the o2S2PARC simulation platform, in order to better understand how vagal inputs influence the local cardiac circuits, and improve neuromodulatory medicine for heart disease. The project will follow a sequence of increasing model complexity of the neurons and neural networks forming the right atrial ganglionic plexus (RAGP) within the ICN, beginning with neuronal electrophysiology, building on these to add neuromodulatory function and heterogeneity based on molecular phenotypes, and then connecting these in networks examining their contributions to overall ICN dynamics for specific predictions to control the heart. These models will account for species differences (rat vs. pig vs. human) and sex differences. It is now feasible to develop RAGP-ICN neuronal models incorporating the specific anatomical, connectional and molecular diversity of the system in such a way as to directly predict approaches to neuromodulatory therapy development. This has become feasible due to the current emergence of comprehensive and foundational data on the system, including RNAseq and single neuron transcriptomic data suggesting neuropeptidergic signaling driven paracrine networks to explore in simulation. Combining these data with state-of-the-art computational neuroscience repositories will produce modeling resources to inform and widely explore neuromodulatory therapy opportunities at the heart within the next 4 years. Major Tasks to be accomplished, their timeline, and their deliverables include: Task 1: Mechanistic modeling of ICN neuron electrophysiology (timeline: Q1 to Q4) Deliverables: Upon successful completion of three milestones, we expect to provide single neuron models of distinct phenotypes, alone and combinatorial, reflecting the data, particularly delineating the differences across sexes. Task 2: Scalable neuromodulation models of ICN neuronal phenotypes (timeline: Q3 to Q6) Deliverables: Completion of three tasks will provide an ensemble of low-dimensional models that can be connected into a network model. Task 3: Network modeling of ICN dynamics (timeline: Q5 to Q8) Deliverables: Two milestones will produce network models of RAGP neurons with adaptive neuromodulatory kinetics. The project efforts follow a model-driven design strategy to build on the anatomical, molecular and physiology data in the SPARC and other data sources on the ganglionic neural circuits, transcript/prote-omics, and cellular mechanisms. The modeling approach leverages the available library of quantitative representations of neuronal biophysics (e.g., NeuronDB and ModelDB databases) and follows the credible practice guidelines.

该项目的主要目标是开发神经元和网络的计算模型 内在心脏神经系统 (ICN)，在 o2S2PARC 模拟上实施模拟 平台，以便更好地了解迷走神经输入如何影响局部心脏回路，以及 改善心脏病的神经调节药物。该项目将遵循一系列 增加形成右心房神经节的神经元和神经网络的模型复杂性 ICN 内的神经丛（RAGP），从神经元电生理学开始，在此基础上 根据分子表型添加神经调节功能和异质性，然后 将这些连接到网络中，检查它们对特定的 ICN 整体动态的贡献 预测控制心脏。这些模型将考虑物种差异（大鼠、猪、猪）。 人类）和性别差异。 现在可以开发结合特定解剖学、 系统的连接和分子多样性，以直接预测方法 神经调节疗法的发展。由于目前的情况，这已变得可行 系统上出现了全面的基础数据，包括 RNAseq 和单一数据 神经元转录组数据表明神经肽能信号驱动旁分泌网络 在模拟中探索。将这些数据与最先进的计算神经科学相结合 存储库将产生建模资源来告知和广泛探索神经调节 未来 4 年内的心脏治疗机会。 要完成的主要任务、时间表和可交付成果包括： 任务 1：ICN 神经元电生理学的机制建模（时间线：Q1 至 Q4） 可交付成果：在成功完成三个里程碑后，我们期望提供单个神经元 不同表型的模型，单独的和组合的，反映数据，特别是 描绘出性别之间的差异。 任务 2：ICN 神经元表型的可扩展神经调节模型（时间线：Q3 至 Q6） 可交付成果：完成三项任务将提供一组低维模型， 可以连接成网络模型。 任务 3：ICN 动态的网络建模（时间线：Q5 至 Q8） 可交付成果：两个里程碑将产生具有自适应能力的 RAGP 神经元网络模型 神经调节动力学。 该项目工作遵循模型驱动的设计策略，以解剖学、分子学为基础 SPARC 中的生理学数据和神经节神经回路的其他数据源， 转录本/蛋白质组学和细胞机制。建模方法利用了可用的 神经元生物物理学定量表示库（例如 NeuronDB 和 ModelDB 数据库）并遵循可靠的实践指南。