New Findings What is the topic of this review? The vagus nerve is a crucial regulator of cardiovascular homeostasis, and its activity is linked to heart health. Vagal activity originates from two brainstem nuclei: the nucleus ambiguus (fast lane) and the dorsal motor nucleus of the vagus (slow lane), nicknamed for the time scales that they require to transmit signals. What advances does it highlight? Computational models are powerful tools for organizing multi‐scale, multimodal data on the fast and slow lanes in a physiologically meaningful way. A strategy is laid out for how these models can guide experiments aimed at harnessing the cardiovascular health benefits of differential activation of the fast and slow lanes. AbstractThe vagus nerve is a key mediator of brain–heart signaling, and its activity is necessary for cardiovascular health. Vagal outflow stems from the nucleus ambiguus, responsible primarily for fast, beat‐to‐beat regulation of heart rate and rhythm, and the dorsal motor nucleus of the vagus, responsible primarily for slow regulation of ventricular contractility. Due to the high‐dimensional and multimodal nature of the anatomical, molecular and physiological data on neural regulation of cardiac function, data‐derived mechanistic insights have proven elusive. Elucidating insights has been complicated further by the broad distribution of the data across heart, brain and peripheral nervous system circuits. Here we lay out an integrative framework based on computational modelling for combining these disparate and multi‐scale data on the two vagal control lanes of the cardiovascular system. Newly available molecular‐scale data, particularly single‐cell transcriptomic analyses, have augmented our understanding of the heterogeneous neuronal states underlying vagally mediated fast and slow regulation of cardiac physiology. Cellular‐scale computational models built from these data sets represent building blocks that can be combined using anatomical and neural circuit connectivity, neuronal electrophysiology, and organ/organismal‐scale physiology data to create multi‐system, multi‐scale models that enable in silico exploration of the fast versus slow lane vagal stimulation. The insights from the computational modelling and analyses will guide new experimental questions on the mechanisms regulating the fast and slow lanes of the cardiac vagus toward exploiting targeted vagal neuromodulatory activity to promote cardiovascular health.

The Right Atrial Ganglionic Plexus (RAGP) is a component of the intrinsic cardiac nervous system. Its neurons are critical to the cardiac pacemaker response to vagal stimulation, regulating sino‐atrial node activity. We developed single‐compartment computational models of RAGP principal neurons by incorporating ion channels identified from transcriptomics data from HT‐qPCR (High Throughput quantitative Polymerase Chain Reaction) and RNA‐sequencing of 405 principal neurons in female Yucatan minipig RAGP. Out of the ~200 genes in the transcriptomics map, we identified 13 genes that encode ion channel proteins, namely: Naf (Scn1a, Nav 1.1), KDR (Kcnc1, Kv 3.1; Kcnab1, Kv 1.1), P‐, N‐, L‐, T‐type Ca2+ (Cacna1a, Cav 2.1; Cacna1b, Cav 2.2; Cacna1c, Cav 1.2; Cacna1g, Cav 3.1; Cacna1i, Cav 3.3), KA (Kcnd2) and HCN1, HCN2, HCN3, HCN4. The corresponding Hodgkin‐Huxley‐based kinetic models of these ion channel isoforms were mined from databases: Ion Channel Genealogy, Channelpedia, and ModelDB. Since each ion channel gene had several kinetic models associated with it, we ran multiple simulations on the NetPyNE software to identify a single model per gene that would best represent the channel's dynamic behaviour in the assembled electrophysiological model. A binary transcriptomic map of the 13 genes showed 101 distinct ion channel combinations. Our 505 electrophysiological models (5 sets of the 101 cells) elicited two primary firing profiles. 65% elicited a single‐spike phasic response, ~32% elicited a tonic firing in response to a current clamp stimulus, and ~3% produced tonic firing that continued after the stimulus. The firing frequencies increased by ~13 Hz for every 0.1 nA increase in the current clamp stimulus and decreased by ~11 Hz for every 0.01 S/cm2 increase in KA channel conductance. Our electrophysiological models are available to the SPARC community in NetPyNE and NIH’s O2S2PARC platform. The models exhibit phasic and tonic excitability profiles with phasic being more prevalent. We next aim to incorporate neuromodulatory inputs and simulate a network of these neuronal models.