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 心脏固有神经系统(ICN)的模拟，在o2S2PARC模拟上实现模拟 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 改进心脏病的神经调节药物。该项目将遵循一系列 形成右房神经节的神经元和神经网络的模型复杂性增加 ICN内的神经丛(RAGP)，从神经元电生理学开始，建立在这些基础上 加上神经调节功能和基于分子表型的异质性，然后 在网络中将它们联系起来，检查它们对特定的整体ICN动态的贡献 控制心脏的预言。这些模型将考虑物种差异(老鼠、猪和 人类)和性别差异。 现在，建立RAGP-ICN神经元模型是可行的，它结合了特定的解剖学， 以这种方式直接预测方法的体系的连接性和分子多样性 神经调节疗法的发展。由于目前的情况，这已变得可行 出现关于该系统的全面和基础数据，包括RNAseq和Single 神经元转录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 资料库将产生建模资源，以提供信息并广泛探索神经调节 未来4年内心脏治疗的机会。 要完成的主要任务、时间表和交付成果包括： 任务1：ICN神经元电生理学的机械建模(时间表：第一季度至第四季度) 交付成果：在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(时间表：第三季度至第六季度) 交付成果：完成三项任务将提供一套低维模型， 可以连接到网络模型中。 任务3：ICN动态的网络建模(时间表：第5季度至第8季度) 交付成果：两个里程碑将产生自适应的RAGP神经元网络模型 神经调节动力学。 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 和SPARC中的生理数据以及神经节神经回路上的其他数据源， 转录/蛋白质组学和细胞机制。建模方法利用可用的 神经元生物物理学的定量表示库(例如，NeuronDB和ModelDB 数据库)，并遵循可信的做法指南。

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.