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). Formal falsifiability criteria are established through three testable predictions regarding neurotransmitter recovery dynamics.Conclusions: This theoretical framework provides a quantitative, reproducible foundation for understanding rabies pathophysiology at the systems level and identifies potential mechanistic intervention points. 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.研究背景：狂犬病病毒（Rabies virus, RABV）感染出现临床症状后，致死率接近100%（>99.9%），其致病机制主要为神经递质系统与脑干自主神经中枢的功能紊乱，而非显著的神经元破坏。当前的治疗手段（包括密尔沃基疗法（Milwaukee Protocol））在严格评估中均未展现出可重复的疗效。 研究方法：本研究构建了一套严谨的多学科交叉理论框架，整合了五大研究领域：(1) 计算神经科学（Computational Neuroscience）：用于建模多受体病毒干扰下的神经递质受体动力学，涉及烟碱型乙酰胆碱受体（nicotinic acetylcholine receptor, nAChR）、p75神经营养因子受体（p75 neurotrophin receptor, p75NTR）以及细胞黏附分子（neural cell adhesion molecule, NCAM）；(2) 数学病毒学（Mathematical Virology）：用于刻画具有空间分区与免疫调控特征的宿主内病毒动态；(3) 系统生物学（Systems Biology）：用于对紊乱的神经化学与细胞凋亡通路开展网络级分析；(4) 贝叶斯推断（Bayesian Inference）：用于在认知不确定性下开展参数估计，并进行正式的结构与实际可识别性分析；(5) 神经药理学（Neuropharmacology）：用于对靶向干预策略开展计算机虚拟（in silico）评估。本研究构建了具备严格存在性与唯一性证明的耦合非线性常微分方程组，实现了带有收敛诊断的哈密顿蒙特卡洛（Hamiltonian Monte Carlo）采样，采用基于方差的索博尔指数（Sobol indices）开展全局敏感性分析，并通过可检验的预测设定了可证伪性标准。 研究结果：本模型定量刻画了乙酰胆碱（acetylcholine, ACh）与5-羟色胺（5-hydroxytryptamine, 5-HT）信号通路的紊乱以及细胞凋亡调控的异常，预测了干预疗效的关键分岔阈值。采用弱信息先验的贝叶斯参数估计结果显示，关键动力学参数存在显著的认知不确定性（后验变异系数：45%~182%）。结构可识别性分析表明，仅通过标准病毒载量测量无法确定病毒受体结合亲和力，需结合直接受体结合实验。全局敏感性分析结果显示，病毒受体结合是影响脑干功能紊乱结局的最关键参数（总效应索博尔指数：0.87±0.06）。本研究通过三项关于神经递质恢复动态的可检验预测，确立了正式的可证伪性标准。 研究结论：本理论框架为从系统层面理解狂犬病的病理生理学提供了定量且可重复的研究基础，并明确了潜在的靶向干预机制靶点。然而，鉴于有记录的症状性狂犬病近乎100%的致死率，以及既往治疗方案在对照评估中的失败，本框架仍属于纯理论假说。我们强调，目前尚无经过验证的症状性狂犬病治疗方案，通过疫苗接种与暴露后预防（post-exposure prophylaxis, PEP）进行预防仍是唯一有循证依据的手段。本研究仅旨在在恰当的伦理监管下指导未来的临床前研究，并提供了在动物模型中开展实验验证、以及与现代组学技术（omics）整合的详细路线图。