Information-transfer characteristics in network motifs
Information-transfer characteristics in network motifs
复制标题
DOI：
10.1103/physrevresearch.5.013037
复制
发表时间：
2023-01-24
影响因子：
4.2
通讯作者：
Okada，Takashi
中科院分区：
文献类型：
--
作者：
Mori，Fumito;Okada，Takashi
生物系统中的信息处理是通过在复杂网络（如基因调控网络、信号转导网络和神经网络）上适当地传输信息流来实现的。这些信息流受到网络系统的输入信号特性和结构特性的影响，如网络拓扑结构、调节规则、内在噪声和环境噪声。许多生物网络通常包含几种典型的模式，称为网络模体，它们被认为在生物功能中起着重要作用。然而，它们的信息理论性质，特别是每个网络中的信息流对输入信号的依赖性，仍然知之甚少。在我们之前的研究中[Mori和Okada，Phys. Rev. Res. 2，043432（2020）10.1103/PhysRevResearch.2.043432]，我们开发了一种图形扩展方法来描述布尔网络中多个信息路径的传递熵（TE），这是一种信息流的度量。在那里，输入信号仅限于简单的情况，并且输入信号特性对TE的影响未被阐明。在本文中，我们改进了我们的方法，使其适用于布尔网络，接收任意随机特性的输入信号。我们的公式表示TE是如何确定的输入信号的特性，布尔函数的分配，和噪声的大小。我们发现，在正反馈和负反馈回路中，TE几乎不依赖于信号的时间尺度。相比之下，相干和非相干前馈回路显示低通和高通滤波特性，分别为随时间变化的信号，这是与以前的报告一致。低通或高通滤波的出现由传输信息流的特定路径上的布尔函数的傅立叶分量确定。因此，我们的公式揭示了网络模体中的信息传递机制，并提供了在生物网络中的信息处理的起源的见解。
Information processing in biological systems is realized by the appropriate transmission of information flows over complex networks, such as gene regulatory, signal transduction, and neural networks. These information flows are affected by the input-signal characteristics and structural properties of network systems, such as the network topology, regulation rules, and intrinsic and environmental noise. Many biological networks frequently include several typical patterns called network motifs, which are considered to play important roles in biological functions. However, their information-theoretic properties, particularly the dependence of the information flows in each network on the input signal, remain poorly understood. In our previous study [Mori and Okada, Phys. Rev. Res. 2, 043432 (2020)10.1103/PhysRevResearch.2.043432], we developed a graphical expansion method to describe transfer entropy (TE), a measure of information flow, in Boolean networks in terms of multiple information pathways. There, the input signal was limited to a simple case, and the effect of the input-signal characteristics on TE was not clarified. In this paper, we improve our method to render it applicable to Boolean networks that receive input signals with arbitrary stochastic characteristics. Our formula expresses how TE is determined by the input-signal characteristics, the assignment of Boolean functions, and the noise magnitude. We find that, in both positive and negative feedback loops, TE hardly depends on the signal timescale. In contrast, coherent and incoherent feedforward loops show low- and high-pass filtering properties, respectively, for a time-varying signal, which is consistent with previous reports. The emergence of either low- or high-pass filtering is determined by the Fourier components of the Boolean functions on specific pathways transmitting information flows. Thus our formula reveals the mechanism of information transfer in network motifs and provides insights into the origin of information processing in biological networks.