TL;DR
This paper demonstrates that the coherent feedforward loop with AND logic (C1-FFL) can be derived from the requirement to distinguish persistent signals from transient ones, providing an information processing perspective on its function.
Contribution
It shows that C1-FFL can be deduced from a statistical detection framework, linking its structure to the task of persistent signal discrimination.
Findings
C1-FFL approximates the log-likelihood ratio for persistent signal detection
The model provides an information processing interpretation of C1-FFL
C1-FFL effectively discriminates persistent from transient signals
Abstract
Many studies have shown that cells use 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 durations respectively. It has been shown that the coherent type-1 feedforward 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 use the model to show that persistent and transient input signals give, respectively, a non-zero and zero output. Instead of assuming the structure of C1-FFL, this paper shows that it is possible to deduce the C1-FFL model from the requirement of discriminating a persistent signal. We do this by first formulating a statistical detection problem of distinguishing persistent signals from transient ones. The solution…
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