Cellular Sensory Mechanisms for Detecting Specific Fold-Changes in Extracellular Cues
Cellular Sensory Mechanisms for Detecting Specific Fold-Changes in Extracellular Cues
复制标题
DOI：
10.1016/j.bpj.2013.10.039
复制
发表时间：
2014-01-07
影响因子：
3.4
通讯作者：
Morishita, Yoshihiro
中科院分区：
文献类型：
--
作者：
Hironaka, Ken-ichi;Morishita, Yoshihiro
细胞感觉系统通常不响应输入的绝对水平，而是响应输入的倍数变化。这种特性称为倍数变化检测（FCD），对于在绝对水平存在波动的情况下准确感测环境信号的动态变化非常重要。先前的研究将 FCD 定义为输入尺度不变性，并提出了几种实现这种条件的生化模型。在这里，我们证明以前的 FCD 模型可以通过对数微分器来近似。尽管对数微分器满足输入尺度不变性要求，但其响应幅度和响应持续时间强烈依赖于输入时间尺度。这对检测输入倍数变化的特异性和可重复性造成了限制。然而，在果蝇翅膀发育的背景下，已经报道了具有细胞特异性和可重复性的 FCD。受这一事实的启发，并通过扩展以前的 FCD 模型，我们在这里提出了两种可能的机制来实现具有特异性和可重复性的 FCD。一种是积分触发型：系统对输入的时间变化率进行积分，当积分值达到恒定阈值时做出响应，然后重置积分值。另一种是动态阈值型：当输入电平达到阈值时，系统发生响应，每次响应后阈值的值乘以某个常数。这两种机制可以通过适当组合前馈和反馈循环来以生物化学方式实现。两个模型之间的主要区别在于它们对输入历史记录的记忆；我们讨论了通过实验区分这两个模型的可能方法。
Cellular sensory systems often respond not to the absolute levels of inputs but to the fold-changes in inputs. Such a property is called fold-change detection (FCD) and is important for accurately sensing dynamic changes in environmental signals in the presence of fluctuations in their absolute levels. Previous studies defined FCD as input-scale invariance and proposed several biochemical models that achieve such a condition. Here, we prove that the previous FCD models can be approximated by a log-differentiator. Although the log-differentiator satisfies the input-scale invariance requirement, its response amplitude and response duration strongly depend on the input timescale. This creates limitations in the specificity and repeatability of detecting fold-changes in inputs. Nevertheless, FCD with specificity and repeatability by cells has been reported in the context of Drosophila wing development. Motivated by this fact and by extending previous FCD models, we here propose two possible mechanisms to achieve FCD with specificity and repeatability. One is the integrate-and-fire type: a system integrates the rate of temporal change in input and makes a response when the integrated value reaches a constant threshold, and this is followed by the reset of the integrated value. The other is the dynamic threshold type: a system response occurs when the input level reaches a threshold, whose value is multiplied by a certain constant after each response. These two mechanisms can be implemented biochemically by appropriately combining feed-forward and feedback loops. The main difference between the two models is their memory of input history; we discuss possible ways to distinguish between the two models experimentally.