Critical Indices as Limits of Control Functions

A variant of self-similar approximation theory is suggested, permitting an easy and accurate summation of divergent series consisting of only a few terms. The method is based on a power-law algebraic transformation, whose powers play the role of control functions governing the fastest convergence of the renormalized series. A striking relation between the theory of critical phenomena and optimal control theory is discovered: The critical indices are found to be directly related to limits of control functions at critical points. The method is applied to calculating the critical indices for several difficult problems. The results are in very good agreement with accurate numerical data.Comment: 1 file, 5 pages, RevTe

Coarse-grained brownian dynamics simulation of rule-based models

International audienceStudying spatial effects in signal transduction, such as co-localization along scaffold molecules, comes at a cost of complexity. In this paper, we propose a coarse-grained, particle-based spatial simulator, suited for large signal transduction models. Our approach is to combine the particle-based reaction and diffusion method, and (non-spatial) rule-based modeling: the location of each molecular complex is abstracted by a spheric particle, while its internal structure in terms of a site-graph is maintained explicit. The particles diffuse inside the cellular compartment and the colliding complexes stochastically interact according to a rule-based scheme. Since rules operate over molecular motifs (instead of full complexes), the rule set compactly describes a combinatorial or even infinite number of reactions. The method is tested on a model of Mitogen Activated Protein Kinase (MAPK) cascade of yeast pheromone response signaling. Results demonstrate that the molecules of the MAPK cascade co-localize along scaffold molecules, while the scaffold binds to a plasma membrane bound upstream component, localizing the whole signaling complex to the plasma membrane. Especially we show, how rings stabilize the resulting molecular complexes and derive the effective dissociation rate constant for it

Three particles in a finite volume: The breakdown of spherical symmetry

Lattice simulations of light nuclei necessarily take place in finite volumes, thus affecting their infrared properties. These effects can be addressed in a model-independent manner using Effective Field Theories. We study the model case of three identical bosons (mass m) with resonant two-body interactions in a cubic box with periodic boundary conditions, which can also be generalized to the three-nucleon system in a straightforward manner. Our results allow for the removal of finite volume effects from lattice results as well as the determination of infinite volume scattering parameters from the volume dependence of the spectrum. We study the volume dependence of several states below the break-up threshold, spanning one order of magnitude in the binding energy in the infinite volume, for box side lengths L between the two-body scattering length a and L = 0.25a. For example, a state with a three-body energy of -3/(ma^2) in the infinite volume has been shifted to -10/(ma^2) at L = a. Special emphasis is put on the consequences of the breakdown of spherical symmetry and several ways to perturbatively treat the ensuing partial wave admixtures. We find their contributions to be on the sub-percent level compared to the strong volume dependence of the S-wave component. For shallow bound states, we find a transition to boson-diboson scattering behavior when decreasing the size of the finite volume.Comment: 21 pages, 4 figures, 2 table

Meredys, a multi-compartment reaction-diffusion simulator using multistate realistic molecular complexes

<p>Abstract</p> <p>Background</p> <p>Most cellular signal transduction mechanisms depend on a few molecular partners whose roles depend on their position and movement in relation to the input signal. This movement can follow various rules and take place in different compartments. Additionally, the molecules can form transient complexes. Complexation and signal transduction depend on the specific states partners and complexes adopt. Several spatial simulator have been developed to date, but none are able to model reaction-diffusion of realistic multi-state transient complexes.</p> <p>Results</p> <p><it>Meredys </it>allows for the simulation of multi-component, multi-feature state molecular species in two and three dimensions. Several compartments can be defined with different diffusion and boundary properties. The software employs a Brownian dynamics engine to simulate reaction-diffusion systems at the reactive particle level, based on compartment properties, complex structure, and hydro-dynamic radii. Zeroth-, first-, and second order reactions are supported. The molecular complexes have realistic geometries. Reactive species can contain user-defined feature states which can modify reaction rates and outcome. Models are defined in a versatile NeuroML input file. The simulation volume can be split in subvolumes to speed up run-time.</p> <p>Conclusions</p> <p><it>Meredys </it>provides a powerful and versatile way to run accurate simulations of molecular and sub-cellular systems, that complement existing multi-agent simulation systems. <it>Meredys </it>is a Free Software and the source code is available at <url>http://meredys.sourceforge.net/</url>.</p

High expression of gabarapl1 is associated with a better outcome for patients with lymph node-positive breast cancer

International audienceBACKGROUND: This study evaluates the relation of the early oestrogen-regulated gene gabarapl1 to cellular growth and its prognostic significance in breast adenocarcinoma. METHODS: First, the relation between GABARAPL1 expression and MCF-7 growth rate was analysed. Thereafter, by performing macroarray and reverse transcriptase quantitative-polymerase chain reaction (RT-qPCR) experiments, gabarapl1 expression was quantified in several histological breast tumour types and in a retrospective cohort of 265 breast cancers. RESULTS: GABARAPL1 overexpression inhibited MCF-7 growth rate and gabarapl1 expression was downregulated in breast tumours. Gabarapl1 mRNA levels were found to be significantly lower in tumours presenting a high histological grade, with a lymph node-positive (pN+) and oestrogen and/or progesterone receptor-negative status. In univariate analysis, high gabarapl1 levels were associated with a lower risk of metastasis in all patients (hazard ratio (HR) 4.96), as well as in pN+ patients (HR 14.96). In multivariate analysis, gabarapl1 expression remained significant in all patients (HR 3.63), as well as in pN+ patients (HR 5.65). In univariate or multivariate analysis, gabarapl1 expression did not disclose any difference in metastasis risk in lymph node-negative patients. CONCLUSIONS: Our data show for the first time that the level of gabarapl1 mRNA expression in breast tumours is a good indicator of the risk of recurrence, specifically in pN+ patients

RCTP: An Enhanced Routing Protocol Based on Collection Tree Protocol

Due to implementation of routing protocols in limited power supply devices in wireless sensor networks (WSNs), this paper presents and evaluates Rainbow Collection Tree Protocol (RCTP) as an enhanced version of Collection Tree Protocol (CTP). CTP is a lightweight, efficient, robust, and also reliable routing protocol for WSNs. CTP as a cross layer routing protocol is also a platform-independent protocol. It uses Trickle Algorithm to optimize the overhead cost and also makes it quickly adaptable to changes in topology. The basic foundation of CTP is on link quality identification and it uses expected transmission count (ETX). ETX is not stable during the time in real environments and ETX fluctuations cause the routing protocols to not work in optimum level. RCTP uses average expected transmission count (AETX) as link quality metric that has shown it is more stable than ETX. It also uses a new mechanism in parent selection to make it more accurate. Rainbow mechanism is used in RCTP to detect and route around connectivity nodes and avoid route through dead end paths. The Omnet++ has been used as a simulator and the results show RCTP performs more efficiently than CTP in dynamic and crowded environments

Long-Lasting Metabolic Imbalance Related to Obesity Alters Olfactory Tissue Homeostasis and Impairs Olfactory-Driven Behaviors.

Obesity is associated with chronic food intake disorders and binge eating. Food intake relies on the interaction between homeostatic regulation and hedonic signals among which, olfaction is a major sensory determinant. However, its potential modulation at the peripheral level by a chronic energy imbalance associated to obese status remains a matter of debate. We further investigated the olfactory function in a rodent model relevant to the situation encountered in obese humans, where genetic susceptibility is juxtaposed on chronic eating disorders. Using several olfactory-driven tests, we compared the behaviors of obesity-prone Sprague-Dawley rats (OP) fed with a high-fat/high-sugar diet with those of obese-resistant ones fed with normal chow. In OP rats, we reported 1) decreased odor threshold, but 2) poor olfactory performances, associated with learning/memory deficits, 3) decreased influence of fasting, and 4) impaired insulin control on food seeking behavior. Associated with these behavioral modifications, we found a modulation of metabolism-related factors implicated in 1) electrical olfactory signal regulation (insulin receptor), 2) cellular dynamics (glucorticoids receptors, pro- and antiapoptotic factors), and 3) homeostasis of the olfactory mucosa and bulb (monocarboxylate and glucose transporters). Such impairments might participate to the perturbed daily food intake pattern that we observed in obese animals

A First-Passage Kinetic Monte Carlo Algorithm for Complex Diffusion-Reaction Systems

We develop an asynchronous event-driven First-Passage Kinetic Monte Carlo (FPKMC) algorithm for continuous time and space systems involving multiple diffusing and reacting species of spherical particles in two and three dimensions. The FPKMC algorithm presented here is based on the method introduced in [Phys. Rev. Lett., 97:230602, 2006] and is implemented in a robust and flexible framework. Unlike standard KMC algorithms such as the n-fold algorithm, FPKMC is most efficient at low densities where it replaces the many small hops needed for reactants to find each other with large first-passage hops sampled from exact time-dependent Green's functions, without sacrificing accuracy. We describe in detail the key components of the algorithm, including the event-loop and the sampling of first-passage probability distributions, and demonstrate the accuracy of the new method. We apply the FPKMC algorithm to the challenging problem of simulation of long-term irradiation of metals, relevant to the performance and aging of nuclear materials in current and future nuclear power plants. The problem of radiation damage spans many decades of time-scales, from picosecond spikes caused by primary cascades, to years of slow damage annealing and microstructure evolution. Our implementation of the FPKMC algorithm has been able to simulate the irradiation of a metal sample for durations that are orders of magnitude longer than any previous simulations using the standard Object KMC or more recent asynchronous algorithms.Comment: See also arXiv:0905.357