A Bio-Inspired Proteostasis Control Framework for Seeding-Driven Neurodegeneration: A Systems-Level Model of ALS

This work presents a conceptual, bio-inspired framework that models ALS as a system-level instability in proteostasis networks. It introduces a state-based representation of protein dynamics, combined with probabilistic control principles, to explore seeding, buffering, and clearance mechanisms in neurodegeneration. The framework is positioned at a hypothesis-generating level (TRL 1–2) and provides a structured basis for future modeling, in-silico simulation, and experimental research. The framework may also provide a unifying conceptual perspective across related proteinopathies.