On the absence of mutations in nucleotide excision repair genes in sporadic solid tumors.

In general, stochastic tumors show genomic instability associated with the proliferation of DNA point mutations, that is, a mutator phenotype. This feature cannot be explained by a dysfunctional mismatch repair alone, and indicates that nucleotide excision repair (NER) and/or base excision repair should be suppressed. However, mutations in NER genes are not causally implicated in the oncogenesis of sporadic solid tumors, according to the Cancer Gene Census at http://www.sanger.ac.uk/genetics/CGP/Census/. This brings up an apparent paradox: how to explain the recurrent non-existence in NER genes of somatic mutations causally related to cancer? In a recent study, we have shown that the origin of point mutations in cancer cell genomes can be explained by a structurally conserved NER with a functional disorder generated from its entanglement with a disabled apoptosis gene network. In the present study, we further characterize NER gene network properties and show that it has a highly connected architecture. This feature suggests that the absence of mutations in NER genes in sporadic solid tumors is a result of their participation in many essential cellular functions.

Concatenated retrieval of correlated stored information in neural networks.

We consider a coupled map lattice defined on a hypercube in M dimensions, taken here as the information space, to model memory retrieval and information association by a neural network. We assume that both neuronal activity and spike timing may carry information. In this model the state of the network at a given time t is completely determined by the intensity y(sigma,t) with which the information pattern represented by the integer sigma is being expressed by the network. Logistic maps, coupled in the information space, are used to describe the evolution of the intensity function y(sigma(upper arrow),t) with the intent to model memory retrieval in neural systems. We calculate the phase diagram of the system regarding the model ability to work as an associative memory. We show that this model is capable of retrieving simultaneously a correlated set of memories, after a relatively long transient that may be associated to the retrieving of concatenated memorized patterns that lead to a final attractor.

Coarsening of three-dimensional grains in crystals, or bubbles in dry foams, tends towards a universal, statistically scale-invariant regime.

We perform extensive Potts model simulations of three-dimensional dry foam coarsening. Starting with 2.25 million bubbles, we have enough statistics to fulfill the three constraints required for the study of statistical scale invariance: first, enough time for the transient to end and reach the scaling state; then, enough time in the scaling state itself to characterize its properties; and finally, enough bubbles at the end to avoid spurious finite size effects. In the scaling state, we find that the average surface area of the bubbles increases linearly with time. The geometry (bubble shape and size) and topology (number of faces and edges), as well as their correlations, become constant in time. Their distributions agree with the data of the literature. We present an analytical model (universal, up to parameters extracted from the simulations) for a disordered foam minimizing its free energy, which agrees with the simulations. We discuss the limitations of the simulations and of the model.

Information space dynamics for neural networks.

We propose a coupled map lattice defined on a hypercube in M dimensions, the information space, to model memory retrieval by a neural network. We consider that both neuronal activity and the spiking phase may carry information. In this model the state of the network at a given time t is completely determined by a function y(sigma-->,t) of the bit strings sigma-->=(sigma1,sigma2,...,sigmaM), where sigma(i)=+/-1 with i=1,2, ...,M, that gives the intensity with which the information sigma--> is being expressed by the network. As an example, we consider logistic maps, coupled in the information space, to describe the evolution of the intensity function y(sigma-->,t). We propose an interpretation of the maps in terms of the physiological state of the neurons and the coupling between them, obtain Hebb-like learning rules, show that the model works as an associative memory, numerically investigate the capacity of the network and the size of the basins of attraction, and estimate finite size effects. We finally show that the model, when exposed to sequences of uncorrelated stimuli, shows recency and latency effects that depend on the noise level, delay time of measurement, and stimulus intensity.

On the absence of mutations in nucleotide excision repair genes in sporadic solid tumors

In general, stochastic tumors show genomic instability associated with the proliferation of DNA point mutations, that is, a mutator phenotype. This feature cannot be explained by a dysfunctional mismatch repair alone, and indicates that nucleotide excision repair (NER) and/or base excision repair should be suppressed. However, mutations in NER genes are not causally implicated in the oncogenesis of sporadic solid tumors, according to the Cancer Gene Census at http://www.sanger.ac.uk/genetics/CGP/Census/. This brings up an apparent paradox: how to explain the recurrent non-existence in NER genes of somatic mutations causally related to cancer? In a recent study, we have shown that the origin of point mutations in cancer cell genomes can be explained by a structurally conserved NER with a functional disorder generated from its entanglement with a disabled apoptosis gene network. In the present study, we further characterize NER gene network properties and show that it has a highly connected architecture. This feature suggests that the absence of mutations in NER genes in sporadic solid tumors is a result of their participation in many essential cellular functions.