Chou Chun Tung
School of Computer Science and Engineering, University of New South Wales, Sydney, Australia.
R Soc Open Sci. 2018 Nov 7;5(11):181641. doi: 10.1098/rsos.181641. eCollection 2018 Nov.
Many studies have shown that cells use the temporal dynamics of signalling molecules to encode information. One particular class of temporal dynamics is persistent and transient signals, i.e. signals of long and short duration, respectively. It has been shown that the coherent type-1 feed-forward loop with an AND logic at the output (or C1-FFL for short) can be used to discriminate a persistent input signal from a transient one. This has been done by modelling the C1-FFL, and then using the model to show that persistent and transient input signals give, respectively, a non-zero and zero output. The aim of this paper is to make a connection between the statistical detection of persistent signals and the C1-FFL. We begin by first formulating a statistical detection problem of distinguishing persistent signals from transient ones. The solution of the detection problem is to compute the log-likelihood ratio of observing a persistent signal to a transient signal. We show that, if this log-likelihood ratio is positive, which happens when the signal is likely to be persistent, then it can be approximately computed by a C1-FFL. Although the capability of C1-FFL to discriminate persistent signals is known, this paper adds an information processing interpretation on how a C1-FFL works as a detector of persistent signals.
许多研究表明，细胞利用信号分子的时间动态来编码信息。一种特殊的时间动态类型是持续信号和瞬态信号，即分别具有长持续时间和短持续时间的信号。研究表明，在输出端具有与逻辑的相干1型前馈回路（简称为C1-FFL）可用于区分持续输入信号和瞬态输入信号。这是通过对C1-FFL进行建模，然后使用该模型表明持续输入信号和瞬态输入信号分别给出非零输出和零输出实现的。本文的目的是在持续信号的统计检测与C1-FFL之间建立联系。我们首先从制定区分持续信号和瞬态信号的统计检测问题开始。检测问题的解决方案是计算观察到持续信号与瞬态信号的对数似然比。我们表明，如果这个对数似然比为正（当信号可能是持续信号时会出现这种情况），那么它可以由C1-FFL近似计算。尽管C1-FFL区分持续信号的能力是已知的，但本文增加了关于C1-FFL如何作为持续信号检测器工作的信息处理解释。
