New scaling relation for information transfer in biological networks
New scaling relation for information transfer in biological networks
复制标题
生物网络中信息传输的新标度关系
DOI：
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发表时间：
2015
影响因子：
3.9
通讯作者：
S. Walker
中科院分区：
文献类型：
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作者：
Hyunju Kim;P. Davies;S. Walker
我们量化了两个典型生物网络的信息结构特征：裂殖酵母细胞周期调控网络的布尔网络模型(Davidich等人)。2008年PLoS one 3，e1672(DOI：10.1371/Joural.pone.0001672)和萌芽酵母酿酒酵母(Li等人)。2004年第纳特·阿卡德。SCI。USA101,4781-4786(DOI：10.1073/pnas.0305937101)。我们将我们对这些生物网络的结果与对两种不同类型的随机网络：ERDöS-Rényi和无标度网络的集成进行的相同的分析进行了比较。我们表明，这两个生物网络共享的特征不是任何一个随机网络集成所共有的。特别是，我们研究中的生物网络平均比随机网络处理更多的信息。这两个生物网络还表现出在节点之间传输的信息中的比例关系，这将它们与随机网络区分开来，其中，即使与与生物网络共享诸如度分布等重要拓扑属性的随机网络相比，生物网络也是与众不同的。我们发现，这种标度关系中最独特的生物机制与调控每个生物网络的动力学和功能的控制节点子集有关。因此，生物网络中的信息处理被解释为拓扑(因果结构)和动力学(功能)的紧急属性。我们的结果定量地展示了生物进化网络的信息体系结构如何将它们与其他类型的网络体系结构区分开来，这些网络体系结构不共享相同的信息属性。
We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371/journal.pone.0001672)) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci. USA 101, 4781–4786 (doi:10.1073/pnas.0305937101)). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdös–Rényi and scale-free. We show that both biological networks share features in common that are not shared by either random network ensemble. In particular, the biological networks in our study process more information than the random networks on average. Both biological networks also exhibit a scaling relation in information transferred between nodes that distinguishes them from random, where the biological networks stand out as distinct even when compared with random networks that share important topological properties, such as degree distribution, with the biological network. We show that the most biologically distinct regime of this scaling relation is associated with a subset of control nodes that regulate the dynamics and function of each respective biological network. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics (function). Our results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties.
影响因子：
8.6
作者：
Eguíluz, VM;Chialvo, DR;Apkarian, AV
通讯作者：
Apkarian, AV
影响因子：
2
作者：
Albert, R;Othmer, HG
通讯作者：
Othmer, HG