Biomathematical modeling of pulsatile hormone secretion: a historical perspective.
Shortly after the recognition of the profound physiological significance of the pulsatile nature of hormone secretion, computer-based modeling techniques were introduced for the identification and characterization of such pulses. Whereas these earlier approaches defined perturbations in hormone concentration-time series, deconvolution procedures were subsequently employed to separate such pulses into their secretion event and clearance components. Stochastic differential equation modeling was also used to define basal and pulsatile hormone secretion. To assess the regulation of individual components within a hormone network, a method that quantitated approximate entropy within hormone concentration-times series was described. To define relationships within coupled hormone systems, methods including cross-correlation and cross-approximate entropy were utilized. To address some of the inherent limitations of these methods, modeling techniques with which to appraise the strength of feedback signaling between and among hormone-secreting components of a network have been developed. Techniques such as dynamic modeling have been utilized to reconstruct dose-response interactions between hormones within coupled systems. A logical extension of these advances will require the development of mathematical methods with which to approximate endocrine networks exhibiting multiple feedback interactions and subsequently reconstruct their parameters based on experimental data for the purpose of testing regulatory hypotheses and estimating alterations in hormone release control mechanisms.
Evans
William S
WS
Endocrinology and Metabolism, Department of Medicine, University of Virginia Health System, Charlottesville, Virginia, USA.
Farhy
Leon S
LS
Johnson
Michael L
ML
D000818:Animals; D019295:Computational Biology / Q000379:methods*; D006728:Hormones / Q000378:metabolism*; D006801:Humans; D008954:Models, Biological; D008962:Models, Theoretical
D006728:Hormones