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

该项目的主要目标是开发神经元和网络的计算模型 的内在心脏神经系统（ICN），在o2 S2 PARC仿真上实现仿真 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 用于心脏病改进的神经调节药物。该项目将遵循以下顺序： 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 神经丛（RAGP）在ICN内，从神经元电生理学开始，在这些基础上， 添加基于分子表型的神经调节功能和异质性，然后 在网络中将这些联系起来，研究它们对国际竞争网络总体动态的贡献， 预测来控制心脏。这些模型将考虑种属差异（大鼠vs.猪vs. 人与人之间的差异。 现在，开发RAGP-ICN神经元模型是可行的， 连接和分子多样性的系统，以这样的方式，直接预测的方法 神经调节疗法的发展。这是可行的，因为目前 系统上出现全面和基础数据，包括RNAseq和单个 神经元转录组学数据表明神经肽能信号驱动的旁分泌网络， 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 知识库将产生建模资源，以告知和广泛探索神经调节 在未来4年内，心脏治疗的机会。 待完成的主要任务、其时间轴及其可交付成果包括： 任务1：ICN神经元电生理学的机制建模（时间轴：Q1至Q4） 成功完成三个里程碑后，我们预计将提供单个神经元 不同表型的模型，单独和组合，反映数据，特别是 描绘了不同性别之间的差异。 任务2：ICN神经元表型的可扩展神经调节模型（时间轴：Q3至Q6） 可解释的：完成三项任务将提供一个低维模型的集合， 可以连接成网络模型。 任务3：ICN动态的网络建模（时间轴：Q5至Q8） 两个里程碑将产生RAGP神经元的网络模型， 神经调节动力学 该项目的工作遵循模型驱动的设计策略，建立在解剖，分子 和生理学数据以及神经节神经回路上的其他数据源， 转录物/蛋白质组学和细胞机制。建模方法利用了可用的 神经元生物物理学的定量表示库（例如，NeuronDB和ModelDB 数据库），并遵循可信的实践指南。

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.