  We characterize different cell states, related to cancer and ageing
phenotypes, by a measure of entropy of network ensembles, integrating gene
expression values and protein interaction networks. The entropy measure
estimates the parameter space available to the network ensemble, that can be
interpreted as the level of plasticity of the system for high entropy values
(the ability to change its internal parameters, e.g. in response to
environmental stimuli), or as a fine tuning of the parameters (that restricts
the range of possible parameter values) in the opposite case. This approach can
be applied at different scales, from whole cell to single biological functions,
by defining appropriate subnetworks based on a priori biological knowledge,
thus allowing a deeper understanding of the cell processes involved. In our
analysis we used specific network features (degree sequence, subnetwork
structure and distance between gene profiles) to obtain informations at
different biological scales, providing a novel point of view for the
integration of experimental transcriptomic data and a priori biological
knowledge, but the entropy measure can also highlight other aspects of the
biological systems studied depending on the constraints introduced in the model
(e.g. community structures).
