Abstract
Endocrine systems are characterised by complex dynamic behaviours—oscillations, transient responses, feedback regulation, and noise filtering—that are essential to physiological stability. Despite extensive molecular and clinical research, the quantitative principles governing these dynamics remain incompletely understood. In particular, the mechanistic relationship between the metabolic clearance rates of individual hormones and emergent system-level properties such as oscillatory frequency, damping, and stability has not been systematically integrated into endocrine theory.
This paper introduces a control-theoretic framework for systems endocrinology, modelling endocrine networks as cascades of second-order negative feedback systems whose parameters are derived directly from measurable clearance-rate values. Two transfer function architectures are defined—G-type (feed-forward) and H-type (feedback)—which encode a spectrum from rapid, responsive regulation to stable, noise-rejecting maintenance. Frequency-domain analysis via Bode plots extracts biologically interpretable metrics including cutoff frequency, bandwidth, phase margin, and stability margins. The framework is demonstrated using the cortisol-HPA axis as a worked example, where model-predicted timescales are shown to be consistent with experimentally observed ultradian pulsatility (~90-minute period), ACTH-stimulation response kinetics, and dexamethasone suppression dynamics. A reference table of clearance-rate-derived parameters for 36 hormones and substrates is provided, enabling immediate application across major endocrine axes. All computational tools are implemented in R and made freely available. By translating standard pharmacokinetic data into the language of control engineering, the framework provides a candidate, testable account of questions that static endocrine models leave qualitative: why cortisol pulsatility occurs on an approximately ultradian timescale, how stable the HPA axis is, and why the timing—not just the dose—of cortisol replacement determines clinical outcome.