WNP: A Novel Algorithm for Gene Products Annotation from Weighted Functional Networks

Predicting the biological function of all the genes of an organism is one of the fundamental goals of computational system biology. In the last decade, high-throughput experimental methods for studying the functional interactions between gene products (GPs) have been combined with computational approaches based on Bayesian networks for data integration. The result of these computational approaches is an interaction network with weighted links representing connectivity likelihood between two functionally related GPs. The weighted network generated by these computational approaches can be used to predict annotations for functionally uncharacterized GPs. Here we introduce Weighted Network Predictor (WNP), a novel algorithm for function prediction of biologically uncharacterized GPs. Tests conducted on simulated data show that WNP outperforms other 5 state-of-the-art methods in terms of both specificity and sensitivity and that it is able to better exploit and propagate the functional and topological information of the network. We apply our method to Saccharomyces cerevisiae yeast and Arabidopsis thaliana networks and we predict Gene Ontology function for about 500 and 10000 uncharacterized GPs respectively

Chemical Equilibrium on Low Dimensional Supports: Connecting the Microscopic Mechanism to the Macroscopic Observations

Classical chemical thermodynamics predicts that the equilibrium composition of a reactive system is entirely defined by the equilibrium constants of the different reactions involved. In this paper we show that for nonlinear reactions taking place on a low-dimensional support this is not true anymore: the equilibrium state depends on the mechanistic details of the chemical processes, so that even two reactions having the same mean field kinetics and equilibrium constants can reach a different equilibrium composition, depending on the microscopic mechanism. We illustrate this point by simulations and mathematical analyses of a simple autocatalytic scheme, and we propose a theoretical route to discriminate between the different cases.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

When Brownian diffusion is not Gaussian

It is commonly presumed that the random displacements that particles undergo as a result of the thermal jiggling of the environment follow a normal, or Gaussian, distribution. Here we reason, and support with experimental examples, that non-Gaussian diffusion in soft materials is more prevalent than expected.close442