Graph Curvature for Differentiating Cancer Networks.

Abstract:

:Cellular interactions can be modeled as complex dynamical systems represented by weighted graphs. The functionality of such networks, including measures of robustness, reliability, performance, and efficiency, are intrinsically tied to the topology and geometry of the underlying graph. Utilizing recently proposed geometric notions of curvature on weighted graphs, we investigate the features of gene co-expression networks derived from large-scale genomic studies of cancer. We find that the curvature of these networks reliably distinguishes between cancer and normal samples, with cancer networks exhibiting higher curvature than their normal counterparts. We establish a quantitative relationship between our findings and prior investigations of network entropy. Furthermore, we demonstrate how our approach yields additional, non-trivial pair-wise (i.e. gene-gene) interactions which may be disrupted in cancer samples. The mathematical formulation of our approach yields an exact solution to calculating pair-wise changes in curvature which was computationally infeasible using prior methods. As such, our findings lay the foundation for an analytical approach to studying complex biological networks.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Sandhu R,Georgiou T,Reznik E,Zhu L,Kolesov I,Senbabaoglu Y,Tannenbaum A

doi

10.1038/srep12323

subject

Has Abstract

pub_date

2015-07-14 00:00:00

pages

12323

issn

2045-2322

pii

srep12323

journal_volume

5

pub_type

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