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

> **NIH NIH OT2** · THOMAS JEFFERSON UNIVERSITY · 2021 · $941,588

## Abstract

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.

## Key facts

- **NIH application ID:** 10467590
- **Project number:** 3OT2OD030534-01S1
- **Recipient organization:** THOMAS JEFFERSON UNIVERSITY
- **Principal Investigator:** Rajanikanth Vadigepalli
- **Activity code:** OT2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $941,588
- **Award type:** 3
- **Project period:** 2020-09-15 → 2024-09-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10467590

## Citation

> US National Institutes of Health, RePORTER application 10467590, Modeling network dynamics of cardiac right atrial ganglionic plexus to enable in silico testing of vagal neurostimulation strategies (3OT2OD030534-01S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10467590. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*