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

View ORCID ProfileJosé Lages, View ORCID ProfileDima L. Shepelyansky, View ORCID ProfileAndrei Zinovyev
doi: https://doi.org/10.1101/096362
José Lages
Institut UTINAM, Observatoire des Sciences de l’Univers THETA, CNRS, Université de Franche-Comté, 25030 Besançon, France
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Dima L. Shepelyansky
Laboratoire de Physique Théorique du CNRS, IRSAMC, Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
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Andrei Zinovyev
Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, F-75005, Paris, France
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Abstract

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 11011-intuitive changes in the wiring of complex and large causal biological networks.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 22, 2016.
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Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks
José Lages, Dima L. Shepelyansky, Andrei Zinovyev
bioRxiv 096362; doi: https://doi.org/10.1101/096362
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Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks
José Lages, Dima L. Shepelyansky, Andrei Zinovyev
bioRxiv 096362; doi: https://doi.org/10.1101/096362

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