Biology – Quantitative Biology – Molecular Networks
Scientific paper
2012-02-14
Biology
Quantitative Biology
Molecular Networks
10 pages, 3 figures, 3 tables. Submitted
Scientific paper
The cellular phenotype is described by a complex network of molecular interactions. Elucidating network properties that distinguish disease from the healthy state is therefore of great importance for gaining systems-level insights into disease mechanisms and ultimately for developing improved therapies. Recently, statistical mechanical network properties have been studied in the context of cancer networks, yet it is unclear which properties best characterise the cancer phenotype. In this work we take a step in this direction by comparing two different types of molecular entropy in their ability to discriminate cancer from the normal phenotype. One entropy measure (flux entropy) is dynamical in the sense that it is derived from a stochastic process. The second measure (covariance entropy) does not depend on the interaction network and is thus "static". Using multiple gene expression data sets of normal and cancer tissue, we demonstrate that flux entropy is a better discriminator of the cancer phenotype than covariance entropy. Specifically, we show that local flux entropy is always increased in cancer relative to normal tissue while the local covariance entropy is not. We show that gene expression differences between normal and cancer tissue are anticorrelated with local flux entropy changes, thus providing a systemic link between gene expression changes and their local information flux dynamics. We also show that genes located in the intracellular domain demonstrate preferential increases in flux entropy, while the entropy of genes encoding membrane receptors and secreted factors is preferentially reduced. Thus, these results elucidate intrinsic network properties of cancer and support the view that the observed increased robustness of cancer cells to perturbation and therapy may be due to an increase in the dynamical network entropy allowing cells to adapt to extracellular stresses.
Bianconi Ginestra
Severini Simone
Teschendorff Andrew
West James
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