Kim Hyunju, Davies Paul, Walker Sara Imari
BEYOND: Center for Fundamental Concepts in Science, Arizona State University, Tempe, AZ, USA.
BEYOND: Center for Fundamental Concepts in Science, Arizona State University, Tempe, AZ, USA School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA ASU-SFI Center for Biosocial Complex Systems, Arizona State University, Tempe, AZ, USA Blue Marble Space Institute of Science, Seattle, WA, USA
J R Soc Interface. 2015 Dec 6;12(113):20150944. doi: 10.1098/rsif.2015.0944.
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.
裂殖酵母（Schizosaccharomyces pombe）细胞周期调控网络的布尔网络模型（Davidich等人，2008年，《公共科学图书馆·综合》3，e1672（doi:10.1371/journal.pone.0001672））以及出芽酵母（Saccharomyces cerevisiae）的布尔网络模型（Li等人，2004年，《美国国家科学院院刊》101，4781 - 4786（doi:10.1073/pnas.0305937101））。我们将这些生物网络的结果与对两种不同类型随机网络集合进行的相同分析结果进行了比较：厄多斯 - 雷尼（Erdös-Rényi）网络和无标度网络。我们发现这两个生物网络具有一些共同特征，而这两种随机网络集合都不具备这些特征。特别是，我们研究中的生物网络平均比随机网络处理更多信息。这两个生物网络在节点间传递的信息中还呈现出一种标度关系，这使它们有别于随机网络，即便与那些在重要拓扑性质（如度分布）上与生物网络相同的随机网络相比，生物网络也显得独特。我们表明，这种标度关系中最具生物学差异的状态与调控每个生物网络动态和功能的一组控制节点相关。因此，生物网络中的信息处理被解释为拓扑结构（因果结构）和动态过程（功能）的一种涌现属性。我们的结果定量地证明了生物进化网络的信息架构如何将它们与其他不具有相同信息属性的网络架构区分开来。
