Adaptive Biological Systems & State-Based Health Modelling A Four-Paper Research Series

This Zenodo record contains a four-paper research series examining biological health and disease through the lens of adaptive systems, state-dependent dynamics, and governance-aware modelling. Across immune, metabolic, neural, and regulatory domains, biological systems exhibit history dependence, compensation, collapse trajectories, and irreversibility that are poorly captured by static, threshold-based assessment frameworks. The series introduces a non-prescriptive conceptual architecture for understanding health as movement within constrained state spaces shaped by attractors, coupling dynamics, variance loss, and temporal windows of recoverability. The four papers address:(1) design principles for biomarker-driven adaptive health modelling,(2) state-dependent dynamics and early instability detection,(3) cross-domain biomarker coupling and collapse trajectories, and(4) human-in-the-loop governance for AI-assisted biological modelling. This work is intended for research, systems analysis, and decision-support contexts. It does not propose diagnostic criteria, therapeutic interventions, clinical protocols, or automated decision-making systems.