Keenan D M, Veldhuis J D
Department of Mathematics, University of Virginia, Charlottesville 22903, USA.
Am J Physiol. 1997 Sep;273(3 Pt 2):R1182-92. doi: 10.1152/ajpregu.1997.273.3.R1182.
Neuroendocrine ensembles communicate with their remote and proximal target cells via an intermittent pattern of chemical signaling. The lack of a biomathematical formulation of the underlying burst-generating mechanics of such pulsatile secretory systems has greatly hampered quantitative analysis of the physiological, pharmacological, and pathological regulation of the amplitude and frequency components of seemingly randomly dispersed neuroendocrine signals. Here we present a stochastic differential equation model of episodic glandular signaling in which random, but structured, variations in burst amplitudes superimposed on basal hormone release are combined with a nonstationary Poisson process responsible for the timing of scattered secretory bursts. Burst timing and/or amplitude can be modulated by underlying deterministic trends, e.g., circadian variations in mean expected neurosecretory burst frequency or mass. We illustrate the diversity of output of this model and suggest its use in extracting underlying properties of irregular biological signals in relevant hormone time series. This representation of episodic secretory behavior combines stochastic and deterministic elements inherent in the intermittent activity of a neuroendocrine apparatus.
神经内分泌集合通过间歇性化学信号模式与其远端和近端靶细胞进行通讯。缺乏对这种脉冲分泌系统潜在爆发产生机制的生物数学公式,极大地阻碍了对看似随机分散的神经内分泌信号的幅度和频率成分的生理、药理和病理调节的定量分析。在此,我们提出了一个间歇性腺体信号的随机微分方程模型,其中叠加在基础激素释放上的爆发幅度的随机但有结构的变化,与负责分散分泌爆发时间的非平稳泊松过程相结合。爆发时间和/或幅度可由潜在的确定性趋势调节,例如平均预期神经分泌爆发频率或质量的昼夜变化。我们展示了该模型输出的多样性,并建议将其用于提取相关激素时间序列中不规则生物信号的潜在特性。这种间歇性分泌行为的表示结合了神经内分泌装置间歇性活动中固有的随机和确定性元素。