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

Author

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  • José Lages
  • Dima L Shepelyansky
  • Andrei Zinovyev

Abstract

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.

Suggested Citation

  • José Lages & Dima L Shepelyansky & Andrei Zinovyev, 2018. "Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-28, January.
    • Handle: RePEc:plo:pone00:0190812
    DOI: 10.1371/journal.pone.0190812
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    Citations

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    Cited by:

    1. Andrei Zinovyev & Urszula Czerwinska & Laura Cantini & Emmanuel Barillot & Klaus M Frahm & Dima L Shepelyansky, 2020. "Collective intelligence defines biological functions in Wikipedia as communities in the hidden protein connection network," PLOS Computational Biology, Public Library of Science, vol. 16(2), pages 1-19, February.
    2. Demidov, Denis & Frahm, Klaus M. & Shepelyansky, Dima L., 2020. "What is the central bank of Wikipedia?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    3. C'elestin Coquid'e & Leonardo Ermann & Jos'e Lages & D. L. Shepelyansky, 2019. "Influence of petroleum and gas trade on EU economies from the reduced Google matrix analysis of UN COMTRADE data," Papers 1903.01820, arXiv.org.
    4. Frahm, Klaus M. & Shepelyansky, Dima L., 2020. "Google matrix analysis of bi-functional SIGNOR network of protein–protein interactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    5. Denis Demidov & Klaus M. Frahm & Dima L. Shepelyansky, 2019. "What is the central bank of Wikipedia?," Papers 1902.07920, arXiv.org.
    6. Célestin Coquidé & José Lages & Dima Shepelyansky, 2020. "Interdependence of sectors of economic activities for world countries from the reduced Google matrix analysis of WTO data," Post-Print hal-02132487, HAL.
    7. Célestin Coquidé & José Lages & Leonardo Ermann & Dima Shepelyansky, 2022. "COVID-19 impact on the international trade," Post-Print hal-03536528, HAL.

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