Mathematical Modeling of HPA axis Using Particle Filter Algorithm

This article studies incremental prediction in the context of of a previously contributed model of the Hypothalamus-Pituitary-Adrenal gland (HPA) axis based on the particle filter algorithm. The model considers individual-level circadian rhythm in the context of three coupled nonlinear differential equations of the HPA axis, including Corticotropin-Releasing hormone (CRH), Adrenocorticotropic hormone (ACTH), and Cortisol as state variables. A particle filter approach is proposed to estimate and sample from model state in the context of incoming data in the context of non-linearity, non-Gaussian behavior, as well as stochastic parameters and process noise. The simulation results suggests a high potential for use of particle filtering in prediction of HPA axis state in the context of periodically incoming data.