Three-dimensional Ising model in the fixed-magnetization ensemble: a Monte Carlo study

We study the three-dimensional Ising model at the critical point in the fixed-magnetization ensemble, by means of the recently developed geometric cluster Monte Carlo algorithm. We define a magnetic-field-like quantity in terms of microscopic spin-up and spin-down probabilities in a given configuration of neighbors. In the thermodynamic limit, the relation between this field and the magnetization reduces to the canonical relation M(h). However, for finite systems, the relation is different. We establish a close connection between this relation and the probability distribution of the magnetization of a finite-size system in the canonical ensemble.Comment: 8 pages, 2 Postscript figures, uses RevTe

Tracking repeats using significance and transitivty.

transitivity; extreme value distribution Motivation: Internal repeats in coding sequences correspond to structural and functional units of proteins. Moreover, duplication of fragments of coding sequences is known to be a mechanism to facilitate evolution. Identification of repeats is crucial to shed light on the function and structure of proteins, and explain their evolutionary past. The task is difficult because during the course of evolution many repeats diverged beyond recognition. Results: We introduce a new method TRUST, for ab-initio determination of internal repeats in proteins. It provides an improvement in prediction quality as compared to alternative state-of-the-art methods. The increased sensitivity and accuracy of the method is achieved by exploiting the concept of transitivity of alignments. Starting from significant local suboptimal alignments, the application of transitivity allows us to: 1) identify distant repeat homologues for which no alignments were found; 2) gain confidence about consistently well-aligned regions; and 3) recognize and reduce the contribution of nonhomologous repeats. This reassessment step enables us to derive a virtually noise-free profile representing a generalized repeat with high fidelity. We also obtained superior specificity by employing rigid statistical testing for self-sequence and profile-sequence alignments. Assessment was done using a database of repeat annotations based on structural superpositioning. The results show that TRUST is a useful and reliable tool for mining tandem and non-tandem repeats in protein sequence databases, able to predict multiple repeat types with varying intervening segments within a single sequence

PRALINE: a multiple sequence alignment toolbox that integrates homology-extended and secondary structure information

PRofile ALIgNEment (PRALINE) is a fully customizable multiple sequence alignment application. In addition to a number of available alignment strategies, PRALINE can integrate information from database homology searches to generate a homology-extended multiple alignment. PRALINE also provides a choice of seven different secondary structure prediction programs that can be used individually or in combination as a consensus for integrating structural information into the alignment process. The program can be used through two separate interfaces: one has been designed to cater to more advanced needs of researchers in the field, and the other for standard construction of high confidence alignments. The web-based output is designed to facilitate the comprehensive visualization of the generated alignments by means of five default colour schemes based on: residue type, position conservation, position reliability, residue hydrophobicity and secondary structure, depending on the options set. A user can also define a custom colour scheme by selecting which colour will represent one or more amino acids in the alignment. All generated alignments are also made available in the PDF format for easy figure generation for publications. The grouping of sequences, on which the alignment is based, can also be visualized as a dendrogram. PRALINE is available at

A Monte Carlo study of the triangular lattice gas with the first- and the second-neighbor exclusions

We formulate a Swendsen-Wang-like version of the geometric cluster algorithm. As an application,we study the hard-core lattice gas on the triangular lattice with the first- and the second-neighbor exclusions. The data are analyzed by finite-size scaling, but the possible existence of logarithmic corrections is not considered due to the limited data. We determine the critical chemical potential as $\mu_c=1.75682 (2)$ and the critical particle density as $\rho_c=0.180(4)$. The thermal and magnetic exponents $y_t=1.51(1) \approx 3/2$ and $y_h=1.8748 (8) \approx 15/8$, estimated from Binder ratio $Q$ and susceptibility $\chi$, strongly support the general belief that the model is in the 4-state Potts universality class. On the other hand, the analyses of energy-like quantities yield the thermal exponent $y_t$ ranging from $1.440(5)$ to $1.470(5)$. These values differ significantly from the expected value 3/2, and thus imply the existence of logarithmic corrections.Comment: 4 figures 2 table

RNA structure prediction from evolutionary patterns of nucleotide composition

Structural elements in RNA molecules have a distinct nucleotide composition, which changes gradually over evolutionary time. We discovered certain features of these compositional patterns that are shared between all RNA families. Based on this information, we developed a structure prediction method that evaluates candidate structures for a set of homologous RNAs on their ability to reproduce the patterns exhibited by biological structures. The method is named SPuNC for ‘Structure Prediction using Nucleotide Composition’. In a performance test on a diverse set of RNA families we demonstrate that the SPuNC algorithm succeeds in selecting the most realistic structures in an ensemble. The average accuracy of top-scoring structures is significantly higher than the average accuracy of all ensemble members (improvements of more than 20% observed). In addition, a consensus structure that includes the most reliable base pairs gleaned from a set of top-scoring structures is generally more accurate than a consensus derived from the full structural ensemble. Our method achieves better accuracy than existing methods on several RNA families, including novel riboswitches and ribozymes. The results clearly show that nucleotide composition can be used to reveal the quality of RNA structures and thus the presented technique should be added to the set of prediction tools

Graphical representations and cluster algorithms for critical points with fields

A two-replica graphical representation and associated cluster algorithm is described that is applicable to ferromagnetic Ising systems with arbitrary fields. Critical points are associated with the percolation threshold of the graphical representation. Results from numerical simulations of the Ising model in a staggered field are presented. The dynamic exponent for the algorithm is measured to be less than 0.5.Comment: Revtex, 12 pages with 2 figure

Generalized Geometric Cluster Algorithm for Fluid Simulation

We present a detailed description of the generalized geometric cluster algorithm for the efficient simulation of continuum fluids. The connection with well-known cluster algorithms for lattice spin models is discussed, and an explicit full cluster decomposition is derived for a particle configuration in a fluid. We investigate a number of basic properties of the geometric cluster algorithm, including the dependence of the cluster-size distribution on density and temperature. Practical aspects of its implementation and possible extensions are discussed. The capabilities and efficiency of our approach are illustrated by means of two example studies.Comment: Accepted for publication in Phys. Rev. E. Follow-up to cond-mat/041274

SEQATOMS: a web tool for identifying missing regions in PDB in sequence context.

doi:10.1093/nar/gkn23

FluxSimulator: An R package to simulate isotopomer distributions in metabolic networks

The representation of biochemical knowledge in terms of fluxes (transformation rates) in a metabolic network is often a crucial step in the development of new drugs and efficient bioreactors. Mass spectroscopy (MS) and nuclear magnetic resonance spectroscopy (NMRS) in combination with 13C labeled substrates are experimental techniques resulting in data that may be used to quantify fluxes in the metabolic network underlying a process. The massive amount of data generated by spectroscopic experiments increasingly requires software which models the dynamics of the underlying biological system. In this work we present an approach to handle isotopomer distributions in metabolic networks using an object-oriented programming approach, implemented using S4 classes in R. The developed package is called FluxSimulator and provides a user friendly interface to specify the topological information of the metabolic network as well as carbon atom transitions in plain text files. The package automatically derives the mathematical representation of the formulated network, and assembles a set of ordinary differential equations (ODEs) describing the change of each isotopomer pool over time. These ODEs are subsequently solved numerically. In a case study FluxSimulator was applied to an example network. Our results indicate that the package is able to reproduce exact changes in isotopomer compositions of the metabolite pools over time at given flux rates