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 .
The cellular phenotype is described by a complex network of molecular interactions. Elucidating network properties that distinguish disease from the healthy cellular state is therefore of critical importance for gaining systems-level insights into disease mechanisms and ultimately for developing improved therapies. By integrating gene expression data with a protein interaction network to induce a stochastic dynamics on the network, we here demonstrate that cancer cells are characterised by an increase in the dynamic network entropy, compared to cells of normal physiology. Using a fundamental relation between the macroscopic resilience of a dynamical system and the uncertainty (entropy) in the underlying microscopic processes, we argue that cancer cells will be more robust to random gene perturbations. In addition, we formally demonstrate that gene expression differences between normal and cancer tissue are anticorrelated with local dynamic entropy changes, thus providing a systemic link between gene expression changes at the nodes and their local network dynamics. In particular, we also find that genes which drive cell-proliferation in cancer cells and which often encode oncogenes are associated with reductions in the dynamic network entropy. In summary, our results support the view that the observed increased robustness of cancer cells to perturbation and therapy may be due to an increase in the dynamic network entropy that allows cells to adapt to the new cellular stresses. Conversely, genes that exhibit local flux entropy decreases in cancer may render cancer cells more susceptible to targeted intervention and may therefore represent promising drug targets .
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cellular
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great
critical
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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
By integrating gene expression data with a protein interaction network to induce a stochastic dynamics on the network, we here demonstrate that cancer cells are characterised by an increase in the dynamic network entropy, compared to cells of normal physiology. Using a fundamental relation between the macroscopic resilience of a dynamical system and the uncertainty (entropy) in the underlying microscopic processes, we argue that cancer cells will be more robust to random gene perturbations. In addition, we formally demonstrate that
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flux
dynamic
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at the nodes
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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
network dynamics. In particular, we also find that genes which drive cell-proliferation in cancer cells and which often encode oncogenes are associated with reductions in the dynamic network entropy. In summary, our results
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dynamical network entropy allowing
dynamic network entropy that allows
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extracellular stresses
the new cellular stresses. Conversely, genes that exhibit local flux entropy decreases in cancer may render cancer cells more susceptible to targeted intervention and may therefore represent promising drug targets