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细胞生物学中的熵:二元编码的信息热力学与信号转导的齐拉德引擎链式模型

Entropy in Cell Biology: Information Thermodynamics of a Binary Code and Szilard Engine Chain Model of Signal Transduction.

作者信息

Tsuruyama Tatsuaki

机构信息

Department of Discovery Medicine, Pathology Division, Graduate School of Medicine, Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8315, Japan.

出版信息

Entropy (Basel). 2018 Aug 19;20(8):617. doi: 10.3390/e20080617.

Abstract

A model of signal transduction from the perspective of informational thermodynamics has been reported in recent studies, and several important achievements have been obtained. The first achievement is that signal transduction can be modelled as a binary code system, in which two forms of signalling molecules are utilised in individual steps. The second is that the average entropy production rate is consistent during the signal transduction cascade when the signal event number is maximised in the model. The third is that a Szilard engine can be a single-step model in the signal transduction. This article reviews these achievements and further introduces a new chain of Szilard engines as a biological reaction cascade (BRC) model. In conclusion, the presented model provides a way of computing the channel capacity of a BRC.

摘要

近期研究报道了一种从信息热力学角度出发的信号转导模型,并取得了若干重要成果。第一个成果是,信号转导可被建模为一个二进制编码系统,其中在各个步骤中使用两种形式的信号分子。第二个成果是,当模型中的信号事件数量最大化时,信号转导级联过程中的平均熵产生率是一致的。第三个成果是,齐拉德引擎可以作为信号转导中的单步模型。本文回顾了这些成果,并进一步引入了一种新的齐拉德引擎链作为生物反应级联(BRC)模型。总之,所提出的模型提供了一种计算BRC通道容量的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e77a/7513144/0f2bace28fa2/entropy-20-00617-g001.jpg

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