Criticality of Lamellar Surfaces by Conformational Degrees of freedom

A new model for lamellar surfaces formed by anisotropic molecules is proposed. The molecules have internal degrees of freedom, associated with their flexible section of length $N$ at zero temperature. We obtain a 2D non-standard six vertex model, which is exactly soluble and exhibits a finite order transition. The order and the character of the transition are determined by the dominant term in the $1 \over N$-expansion of the interaction energy. The dependence of the critical temperatures on $N$ is, instead, determined by the non-leading terms in the same expansion.Comment: 26 pages,plane tex, 5 figures not included, [email protected]

A Model for the Self-Organization of Microtubules Driven by Molecular Motors

We propose a two-dimensional model for the organization of stabilized microtubules driven by molecular motors in an unconfined geometry. In this model two kinds of dynamics are competing. The first one is purely diffusive, with an interaction between the rotational degrees of freedom, the second one is a local drive, dependent on microtubule polarity. As a result, there is a configuration dependent driving field. Applying a molecular field approximation, we are able to derive continuum equations. A study on the solutions shows nonequilibrium steady states. The presence and stability of such self-organized states are investigated in terms of entropy production. Numerical simulations confirm analytical results.Comment: 23 pages, 10 figures, LaTeX, ep

Functional models for large-scale gene regulation networks: realism and fiction

High-throughput experiments are shedding light on the topology of large regulatory networks and at the same time their functional states, namely the states of activation of the nodes (for example transcript or protein levels) in different conditions, times, environments. We now possess a certain amount of information about these two levels of description, stored in libraries, databases and ontologies. A current challenge is to bridge the gap between topology and function, i.e. developing quantitative models aimed at characterizing the expression patterns of large sets of genes. However, approaches that work well for small networks become impossible to master at large scales, mainly because parameters proliferate. In this review we discuss the state of the art of large-scale functional network models, addressing the issue of what can be considered as realistic and what the main limitations may be. We also show some directions for future work, trying to set the goals that future models should try to achieve. Finally, we will emphasize the possible benefits in the understanding of biological mechanisms underlying complex multifactorial diseases, and in the development of novel strategies for the description and the treatment of such pathologies.Comment: to appear on Mol. BioSyst. 200

"Cloud" health-care workers.

Certain bacteria dispersed by health-care workers can cause hospital infections. Asymptomatic health-care workers colonized rectally, vaginally, or on the skin with group A streptococci have caused outbreaks of surgical site infection by airborne dispersal. Outbreaks have been associated with skin colonization or viral upper respiratory tract infection in a phenomenon of airborne dispersal of Staphylococcus aureus called the "cloud" phenomenon. This review summarizes the data supporting the existence of cloud health-care workers

Random Networks Tossing Biased Coins

In statistical mechanical investigations on complex networks, it is useful to employ random graphs ensembles as null models, to compare with experimental realizations. Motivated by transcription networks, we present here a simple way to generate an ensemble of random directed graphs with, asymptotically, scale-free outdegree and compact indegree. Entries in each row of the adjacency matrix are set to be zero or one according to the toss of a biased coin, with a chosen probability distribution for the biases. This defines a quick and simple algorithm, which yields good results already for graphs of size n ~ 100. Perhaps more importantly, many of the relevant observables are accessible analytically, improving upon previous estimates for similar graphs