Gustafsson M, Hörnquist M, Björkegren J, Tegnér J
Department of Science and Technology, Linköping University, Norrkoping, Sweden.
IET Syst Biol. 2009 Jul;3(4):219-28. doi: 10.1049/iet-syb.2008.0112.
Recently, important insights into static network topology for biological systems have been obtained, but still global dynamical network properties determining stability and system responsiveness have not been accessible for analysis. Herein, we explore a genome-wide gene-to-gene regulatory network based on expression data from the cell cycle in Saccharomyces cerevisae (budding yeast). We recover static properties like hubs (genes having several out-going connections), network motifs and modules, which have previously been derived from multiple data sources such as whole-genome expression measurements, literature mining, protein-protein and transcription factor binding data. Further, our analysis uncovers some novel dynamical design principles; hubs are both repressed and repressors, and the intra-modular dynamics are either strongly activating or repressing whereas inter-modular couplings are weak. Finally, taking advantage of the inferred strength and direction of all interactions, we perform a global dynamical systems analysis of the network. Our inferred dynamics of hubs, motifs and modules produce a more stable network than what is expected given randomised versions. The main contribution of the repressed hubs is to increase system stability, while higher order dynamic effects (e.g. module dynamics) mainly increase system flexibility. Altogether, the presence of hubs, motifs and modules induce few flexible modes, to which the network is extra sensitive to an external signal. We believe that our approach, and the inferred biological mode of strong flexibility and stability, will also apply to other cellular networks and adaptive systems.
最近，人们对生物系统的静态网络拓扑有了重要的认识，但决定稳定性和系统响应性的全局动态网络特性仍无法进行分析。在此，我们基于酿酒酵母（芽殖酵母）细胞周期的表达数据，探索了全基因组范围内的基因到基因调控网络。我们恢复了一些静态特性，如枢纽（具有多个输出连接的基因）、网络基序和模块，这些特性此前是从多个数据源推导出来的，如全基因组表达测量、文献挖掘、蛋白质 - 蛋白质和转录因子结合数据。此外，我们的分析揭示了一些新的动态设计原则；枢纽既是被抑制的，也是抑制因子，模块内的动态要么是强烈激活的，要么是强烈抑制的，而模块间的耦合则很弱。最后，利用推断出的所有相互作用的强度和方向，我们对该网络进行了全局动态系统分析。我们推断出的枢纽、基序和模块的动态产生了一个比随机版本预期更稳定的网络。被抑制的枢纽的主要作用是提高系统稳定性，而高阶动态效应（如模块动态）主要提高系统灵活性。总的来说，枢纽、基序和模块的存在诱导出很少的灵活模式，网络对外部信号对这些模式格外敏感。我们相信，我们的方法以及推断出的强灵活性和稳定性的生物学模式，也将适用于其他细胞网络和自适应系统。
