Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks

Signaling pathways represent parts of the global biological molecular network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during development or in pathological conditions. We suggest a novel methodology, called Googlomics, for the structural analysis of directed biological networks using spectral analysis of their Google matrices, using parallels with quantum scattering theory, developed for nuclear and mesoscopic physics and quantum chaos. We introduce analytical “reduced Google matrix” method for the analysis of biological network structure. The method allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as a result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach rigorously characterizes complex systemic changes in the wiring of large causal biological networks in a computationally efficient way.

信号通路（Signaling pathways）是全局生物分子网络的组成部分，该全局生物分子网络通过复杂的直接与间接（隐藏）串扰将各信号通路连接为一个无缝整体，而这类串扰的结构可在发育进程或病理状态下发生改变。我们提出一种名为谷歌组学（Googlomics）的全新方法，用于有向生物网络（directed biological networks）的结构分析：该方法借助谷歌矩阵（Google matrix）的谱分析（spectral analysis），并借鉴了为核物理、介观物理与量子混沌（quantum chaos）领域开发的量子散射理论（quantum scattering theory）的相关思路。我们引入了用于生物网络结构分析的解析型“简化谷歌矩阵（reduced Google matrix）”方法。该方法可用于推断信号通路成员或功能相关基因簇之间的隐藏因果关联。我们探究了在癌症发生过程中，转录网络层的变化如何能够重编程隐藏因果关联的结构。所提出的谷歌组学方法能够以计算高效的方式，精准刻画大型有向因果生物网络的连接模式中复杂的系统性变化。