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In the postgenome era many efforts have been dedicated to systematically elucidate the complex web of interacting genes and proteins. These efforts include experimental and computational methods. Microarray technology offers an opportunity for monitoring gene expression level at the genome scale. By recourse to information theory, this study proposes a mathematical approach to reconstruct gene regulatory networks at coarse-grain level from high throughput gene expression data. The method provides the {\it a posteriori} probability that a given gene regulates positively, negatively or does not regulate each one of the network genes. This approach also allows the introduction of prior knowledge and the quantification of the information gain from experimental data used in the inference procedure. This information gain can be used to chose genes to be perturbed in subsequent experiments in order to refine the knowledge about the architecture of an underlying gene regulatory network. The performance of the proposed approach has been studied by {\it in numero} experiments. Our results suggest that the approach is suitable for focusing on size-limited problems, such as, recovering a small subnetwork of interest by performing perturbation over selected genes., 17 pages, 4 figus

The origin of stochastic fluctuations in gene expression has received considerable attention recently. Fluctuations in gene expression are particularly pronounced in cellular systems because of the small copy number of species undergoing transitions between discrete chemical states and the small size of biological compartments. In this paper, we propose a stochastic model for gene expression regulation including several binding sites, considering elementary reactions only. The model is used to investigate the role of cooperativity on the intrinsic fluctuations of gene expression, by means of master equation formalism. We found that the Hill coefficient and the level of noise increases as the interaction energy between activators increases. Additionally, we show that the model allows to distinguish between two cooperative binding mechanisms., 11 pages, 3 figures

A fundamental task in developmental biology is to identify the mechanisms which drive morphogenesis. In many cases, pattern formation is driven by the positional information determined by both the gradient of maternal factors and hard-wired mechanisms embedded in the genome. Alternative mechanisms of positional information that contribute to patterning are the influence of signals derived from surrounding tissues. In this paper, we show that the interplay of geometrical constraints imposed by tissue shapes and hard-wired mechanisms, computationally implemented by a gene network model, can able to induce stable complex patterns. The rise of these patterns depends strongly on the geometrical constraints such as the shape of the surrounding tissues., Comment: 30 pages, 11 figures

This manuscript uses a statistical mechanical approach to study the effect of the adhesion, through MOCA protein, on cell locomotion. The MOCA protein regulates cell-cell adhesion, and we explore its potential role in the cell movement. We present a series of statistical descriptions of the motion in order to characterize the cell movement, and found that MOCA affects the statistical scenario of cell locomotion. In particular, we observe that MOCA enhances the tendency of joint motion, inhibits super-diffusion, and decreases overall cell motion. These facts are compatible with the hypothesis that the cells move faster in a less cohesive environment. Furthermore, we observe that velocity distribution tails are longer than those predicted by Maxwell-Boltzmann in all cases studied here, indicating that cell movement is more complex than that of a liquid., Comment: 19 pages 5 figs

Projet PROMATH; We undertake the study of signals originated in time-dependent nonlinear systems by recourse to a wavelet based multiresolution analysis, as adapted to a non-extensive (Tsallis) scenario. Diverse applications are discussed that illustrate the fact that a Tsallis environment seems to provide one with more detailed information than the conventional Shannon one.