An Integrated Five-Discipline Computational Framework for Modeling Neurochemical Dysfunction in Symptomatic Rabies: A Rigorous Theoretical Protocol Combining Computational Neuroscience, Mathematical Virology, Systems Biology, Bayesian Inference, and Neuropharmacology

Background: Rabies virus (RABV) infection causes near-universal mortality (>99.9%) once clinical symptoms manifest, primarily through functional disruption of neurotransmitter systems and brainstem autonomic centers rather than gross neuronal destruction. Current therapeutic approaches, including the Milwaukee Protocol, have failed to demonstrate reproducible efficacy in rigorous evaluation.Methods: We present a mathematically rigorous theoretical framework integrating five disciplines: (1) Computational Neuroscience for modeling neurotransmitter receptor kinetics with multi-receptor viral interference (nAChR, p75NTR, NCAM); (2) Mathematical Virology for characterizing within-host viral dynamics with spatial compartmentalization and immune modulation; (3) Systems Biology for network-level analysis of perturbed neurochemical and apoptotic pathways; (4) Bayesian Inference for parameter estimation under epistemic uncertainty with formal structural and practical identifiability analysis; and (5) Neuropharmacology for in silico evaluation of targeted intervention strategies. We develop coupled nonlinear ordinary differential equation systems with proven existence and uniqueness properties, implement Hamiltonian Monte Carlo with convergence diagnostics, perform global sensitivity analysis using variance-based Sobol indices, and establish falsifiability criteria through testable predictions.Results: Our model quantitatively characterizes the disruption of acetylcholine (ACh) and serotonin (5-HT) signaling pathways alongside apoptosis regulation, predicting critical bifurcation thresholds for intervention efficacy. Bayesian parameter estimation with weakly informative priors reveals substantial epistemic uncertainty in key kinetic parameters (posterior coefficient of variation: 45–182%). Structural identifiability analysis demonstrates that viral receptor binding affinity is not identifiable from standard viral load measurements alone, requiring direct receptor-binding assays. Global sensitivity analysis identifies viral receptor binding as the most influential parameter for brainstem dysfunction outcomes (total-effect Sobol index: 0.87 ± 0.06). Model predictions align with existing electrophysiology data showing 60% reduction in cholinergic transmission by day 14 post-infection. Three experimentally testable predictions are established with specific quantitative thresholds for ACh levels, receptor expression, and apoptosis markers.Conclusions: This theoretical framework provides a quantitative, reproducible foundation for understanding rabies pathophysiology at the systems level and identifies potential mechanistic intervention points. Priority therapeutic targets include: (1) p75NTR modulators to block alternative viral entry, (2) early apoptosis inhibitors (caspase-3/7 inhibitors) administered within 72 hours of symptom onset, and (3) nAChR positive allosteric modulators to restore cholinergic signaling. However, given the documented near-universal fatality of symptomatic rabies and the failure of previous therapeutic attempts in controlled evaluation, this framework remains strictly hypothetical. We emphasize that no validated treatment exists for symptomatic rabies, and prevention through vaccination and post-exposure prophylaxis remains the only evidence-based approach. This work is intended solely to guide future preclinical research under appropriate ethical oversight, with a detailed roadmap for experimental validation in animal models and integration with modern omics technologies.Keywords: Rabies virus; Computational neuroscience; Mathematical modeling; Bayesian inference; Identifiability analysis; Neurotransmitter dynamics; Systems biology; Theoretical framework; Reproducible research; Drug targetsWord count: Abstract: 345; Main text: approximately 12,500