Momin Md Sorique Aziz, Biswas Ayan, Banik Suman K
Department of Chemistry, Bose Institute, 93/1 A P C Road, Kolkata 700009, India.
Phys Rev E. 2020 Feb;101(2-1):022407. doi: 10.1103/PhysRevE.101.022407.
We present a theoretical formalism to study steady-state information transmission in a coherent type-1 feed-forward loop motif with an additive signal integration mechanism. Our construct allows a two-step cascade to be slowly transformed into a bifurcation network via a feed-forward loop, which is a prominent network motif. Using a Gaussian framework, we show that among these three network patterns, the feed-forward loop motif harnesses the maximum amount of Shannon mutual information fractions constructed between the final gene-product and each of the master and coregulators of the target gene. We also show that this feed-forward loop motif provides a substantially lower amount of noise in target gene expression, compared with the other two network structures. Our theoretical predictions, which remain invariant for a couple of parametric transformations, point out that the coherent type-1 feed-forward loop motif may qualify as a better decoder of environmental signals when compared with the other two network patterns in perspective.
我们提出一种理论形式体系，用于研究具有加性信号整合机制的相干1型前馈环基序中的稳态信息传递。我们构建的体系允许通过前馈环（一种突出的网络基序）将两步级联缓慢转变为分叉网络。使用高斯框架，我们表明在这三种网络模式中，前馈环基序利用了在最终基因产物与目标基因的每个主调节因子和共调节因子之间构建的最大量的香农互信息分数。我们还表明，与其他两种网络结构相比，这种前馈环基序在目标基因表达中提供的噪声量要低得多。我们的理论预测在几次参数变换下保持不变，指出从这个角度来看，与其他两种网络模式相比，相干1型前馈环基序可能称得上是环境信号的更好解码器。
