Keenan Daniel M, Chattopadhyay Somesh, Veldhuis Johannes D
Department of Statistics, University of Virginia, Charlottesville, VA 22904, USA.
J Theor Biol. 2005 Oct 7;236(3):242-55. doi: 10.1016/j.jtbi.2005.03.008.
Blood-borne neurohormonal signals reflect the intermittent burst-like release of peptides and steroids from neurons, glands and target tissues. Hormones control basic physiological processes, such as growth, metabolism, reproduction and stress-related adaptations. Secreted molecules undergo combined diffusion, advection and irreversible elimination from the circulation. Quantification of these interdependent processes by a structurally relevant model embodying discrete event times, continuous rates of secretion and elimination, and stochastic variations poses a formidable challenge. In an experimental setting, one observes only the hormone concentrations, which comprise a time-varying composite of secretion and elimination. The number, shape and location of underlying bursts (pulses) and attendant secretion and kinetic parameters are unobserved. The ability to estimate the properties of these processes from the observed data is fundamental to an understanding of regulated hormonal dynamics. The present formulation allows objective simultaneous appraisal of discrete (pulse times) and continuous (secretion/elimination) properties of neuroglandular activity in the presence of random variability. A probability distribution is constructed for the structural parameters (secretion/elimination, pulsing), and an algorithm is developed by which one can, based upon observed hormone concentration data, make probabilistic statements about the underlying structure: pulse frequency per day, total basal (constitutive) and pulsatile secretion per day, and half-lives of elimination. The algorithm consists of the following steps: first, explicit construction of a family of sequentially decreasing putative pulse-time sets for a given neurohormone concentration time series; and then, recursive iteration between the following two: (a) for a given pulse-time set, generate a sample from the probability distribution of unknown underlying hormone secretion and elimination rates; and (b) determine whether or not a probability-based transition from one pulse-time set to another is merited (i.e., add/remove a pulse-time or stay the same). We apply this procedure illustratively to joint estimation of pulse times, secretion rates and elimination kinetics of selected pituitary hormones (ACTH, LH and GH).
血源性神经激素信号反映了肽类和类固醇从神经元、腺体及靶组织间歇性的突发式释放。激素控制着诸如生长、代谢、生殖以及与应激相关的适应性变化等基本生理过程。分泌的分子在循环中经历扩散、平流和不可逆清除的综合过程。通过一个体现离散事件时间、连续分泌和清除速率以及随机变化的结构相关模型对这些相互依存的过程进行量化,是一项艰巨的挑战。在实验环境中,人们只能观察到激素浓度,而激素浓度是分泌和清除随时间变化的综合结果。潜在突发(脉冲)的数量、形状和位置以及伴随的分泌和动力学参数是无法观察到的。从观测数据估计这些过程特性的能力是理解激素调节动力学的基础。当前的公式允许在存在随机变异性的情况下,对神经腺体活动的离散(脉冲时间)和连续(分泌/清除)特性进行客观的同时评估。为结构参数(分泌/清除、脉冲)构建概率分布,并开发一种算法,据此人们可以根据观测到的激素浓度数据,对潜在结构做出概率性陈述:每天的脉冲频率、每天的总基础(组成性)和脉冲分泌量以及清除半衰期。该算法包括以下步骤:首先,针对给定的神经激素浓度时间序列,明确构建一系列依次递减的假定脉冲时间集;然后,在以下两者之间进行递归迭代:(a) 对于给定的脉冲时间集,从未知的潜在激素分泌和清除速率的概率分布中生成一个样本;(b) 确定是否值得基于概率从一个脉冲时间集过渡到另一个脉冲时间集(即添加/移除一个脉冲时间或保持不变)。我们举例应用此程序来联合估计所选垂体激素(促肾上腺皮质激素、促黄体生成素和生长激素)的脉冲时间、分泌速率和清除动力学。