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

José Lages,Dima L Shepelyansky,Andrei Zinovyev

doi:10.1371/journal.pone.0190812

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.

Highlights

The network biology point of view on signaling pathway as a part of the complex integrated molecular machinery consists in considering it as a subnetwork embedded into a global molecular network
In order to illustrate the application of Google matrix approach to studying oncogenic changes in the global and local network structures, we constructed two large directed networks describing global signaling in a leukemia cancer cell line K562 compared to a healthy cell line GM12878 derived from normal B-lymphocytes
It was shown that the wiring of the transcriptional network in cancer leads to significant changes in the number of structural patterns leading to degrading the network robustness properties

Summary

Introduction

The network biology point of view on signaling pathway as a part of the complex integrated molecular machinery consists in considering it as a subnetwork embedded into a global molecular network. All properties of the pathway functioning depend on the network context to which it remains connected. Considering only the set of direct causal relations between pathway members (as is frequently the case) neglects the indirect effect of the global biological network changes which may significantly re-wire the pathway topology by introducing implicit (hidden) causal relations. Characterizing the global network structure influence on the local network properties and dynamics remains one of the major challenges of systems and network biology [1]. A number of empirical and pragmatic approaches have been suggested recently to address this question [2,3,4,5,6].

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Conclusion