Background: The human central nervous system (CNS) operates through a complex, non-linear dialogue with the innate immune system. This review delineates the emerging field of in silico neuroimmunology, which employs mathematical abstraction to resolve the “complexity crisis” and the “stiff timescale problem” inherent in neurodegenerative pathologies.
Materials and Methods: This expert narrative review utilizes a framework-driven methodology to synthesize diverse computational formalisms. Models were selected based on their mechanistic depth and relevance to diseases such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis.
Results: By synthesizing frameworks ranging from ordinary differential equations (ODEs) to agent-based models (ABMs), we demonstrate how the transition to chronic neurotoxicity is a systems-level bifurcation. We highlight the role of hysteresis in creating self-sustaining inflammatory states and the necessity of modeling microglial plasticity as a continuous epigenetic landscape. We further address the "stiffness" of neuroimmune networks, which requires specialized solvers to bridge the 12-order-of-magnitude temporal gap between molecular events and clinical decline.
Conclusion: In silico neuroimmunology is no longer merely descriptive but offers the quantitative logic to design precision therapies. The advent of neurological digital twins and physics-informed neural networks (PINNs) marks a transition toward engineering control, allowing for the simulation of personalized therapeutic interventions and the optimization of immunomodulatory strategies.