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

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General metadata

Identifiers :
local : FR-18008901306731-2017-03-28-04 external : doi:10.25666/DATAOSU-2017-03-28-04 , DOI:10.1101/096362
Description :
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.
Disciplines :
Keywords :

Dates :
Data acquisition : from Dec 2015 to Dec 2016
Data provision : 22 Dec 2016
Metadata record : Creation : 28 Mar 2017 Update : 30 Jan 2018

Language : English (eng)
Audience : Research
RightsAttribution, Non Commercial, Share Alike
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Collection

Quotation

José Lages, Dima Shepelyansky, Andrei Zinovyev (2016): Googlomics: Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks. UTINAM. doi:10.25666/DATAOSU-2017-03-28-04

Coverages

Taxonomic coverage :

  • Human
    Species
    Homo sapiens MSW (Human)

Administrative metadata

Data creators : José Lages [1] [2], Dima Shepelyansky [3], Andrei Zinovyev [4]
[1] : Institut UTINAM (UMR 6213) (Université de Franche-Comté)
[2] : Observatoire des Sciences de l'Univers - Terre, Homme, Environnement, Temps, Astronomie (UAR 3245) (Université de Franche-Comté)
[3] : Laboratoire de Physique Théorique (UMR 5152)
[4] : Institut Curie
Publisher : Institut UTINAM (UMR 6213)
Science contact : José Lages website e-mail
Computing contact : José Lages website e-mail
Project and funder :
  • ApliGoogle
    • Mission pour l’interdisciplinarité / Défi MASTODONS (CNRS)
Access : available

Technical metadata

Formats : application/pdf, text/csv, text/plain
Data acquisition methods :
Datatype : Dataset

Publications

dat@UBFC

dat@UBFC is a metadata catalogue for research data produced at UBFC.

Université de Bourgogne, Université de Franche-Comté, UTBM, AgroSup Dijon, ENSMM, BSB, Arts des Metiers