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

Signaling pathways represent parts of the global biological network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during normal development or in a pathological conditions such as cancer. Advanced methods for characterizing the structure of the global directed causal network can shed light on the mechanisms of global cell reprogramming changing the distribution of possible signaling flows. We suggest a methodology, called Googlomics, for the analysis of the structure of directed biological networks using spectral analysis of their Google matrix. This approach uses parallels with quantum scattering theory, developed for processes in nuclear and mesoscopic physics and quantum chaos. We introduce the notion of reduced Google matrix in the context of the regulatory biological networks and demonstrate how its computation 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 the result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach can be useful in various contexts for characterizing non-intuitive changes in the wiring of complex and large causal biological networks.

mathematical & computational biology (fundamental biology), oncology (medical research), physics, mathematical (physics), multidisciplinary sciences