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

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.