Cytoview: development of a cell modelling framework

The biological cell, a natural self-contained unit of prime biological importance, is an enormously complex machine that can be understood at many levels. A higher-level perspective of the entire cell requires integration of various features into coherent, biologically meaningful descriptions. There are some efforts to model cells based on their genome, proteome or metabolome descriptions. However, there are no established methods as yet to describe cell morphologies, capture similarities and differences between different cells or between healthy and disease states. Here we report a framework to model various aspects of a cell and integrate knowledge encoded at different levels of abstraction, with cell morphologies at one end to atomic structures at the other. The different issues that have been addressed are ontologies, feature description and model building. The framework describes dotted representations and tree data structures to integrate diverse pieces of data and parametric models enabling size, shape and location descriptions. The framework serves as a first step in integrating different levels of data available for a biological cell and has the potential to lead to development of computational models in our pursuit to model cell structure and function, from which several applications can flow out

RoboPol: First season rotations of optical polarization plane in blazars

We present first results on polarization swings in optical emission of blazars obtained by RoboPol, a monitoring program of an unbiased sample of gamma-ray bright blazars specially designed for effective detection of such events. A possible connection of polarization swing events with periods of high activity in gamma rays is investigated using the dataset obtained during the first season of operation. It was found that the brightest gamma-ray flares tend to be located closer in time to rotation events, which may be an indication of two separate mechanisms responsible for the rotations. Blazars with detected rotations have significantly larger amplitude and faster variations of polarization angle in optical than blazars without rotations. Our simulations show that the full set of observed rotations is not a likely outcome (probability $\le 1.5 \times 10^{-2}$) of a random walk of the polarization vector simulated by a multicell model. Furthermore, it is highly unlikely ($\sim 5 \times 10^{-5}$) that none of our rotations is physically connected with an increase in gamma-ray activity.Comment: 16 pages, 9 figure

Multilevel Parallelization of AutoDock 4.2

<p>Abstract</p> <p>Background</p> <p>Virtual (computational) screening is an increasingly important tool for drug discovery. AutoDock is a popular open-source application for performing molecular docking, the prediction of ligand-receptor interactions. AutoDock is a serial application, though several previous efforts have parallelized various aspects of the program. In this paper, we report on a multi-level parallelization of AutoDock 4.2 (mpAD4).</p> <p>Results</p> <p>Using MPI and OpenMP, AutoDock 4.2 was parallelized for use on MPI-enabled systems and to multithread the execution of individual docking jobs. In addition, code was implemented to reduce input/output (I/O) traffic by reusing grid maps at each node from docking to docking. Performance of mpAD4 was examined on two multiprocessor computers.</p> <p>Conclusions</p> <p>Using MPI with OpenMP multithreading, mpAD4 scales with near linearity on the multiprocessor systems tested. In situations where I/O is limiting, reuse of grid maps reduces both system I/O and overall screening time. Multithreading of AutoDock's Lamarkian Genetic Algorithm with OpenMP increases the speed of execution of individual docking jobs, and when combined with MPI parallelization can significantly reduce the execution time of virtual screens. This work is significant in that mpAD4 speeds the execution of certain molecular docking workloads and allows the user to optimize the degree of system-level (MPI) and node-level (OpenMP) parallelization to best fit both workloads and computational resources.</p

Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images

<p>Abstract</p> <p>Background</p> <p>Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed.</p> <p>Results</p> <p>To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies.</p> <p>Conclusions</p> <p>These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.</p

Optical polarization of gamma-ray bright blazars

We report about first results of the RoboPol project. RoboPol is a large-sample, high-cadence, polarimetric monitoring program of blazars in optical wavelengths, using a camera specifically constructed for this project, mounted at the University of Crete's Skinakas Observatory 1.3 m telescope. The analysis of RoboPol data is conducted in conjunction with Fermi LAT gamma-ray data, and multifrequency radio data from the OVRO (Caltech), F-GAMMA (MPIfR), and Torun (NCU) monitoring programs. Using carefully selected samples of gamma-ray bright and weak blazars we investigate a connection between their optical polarization behaviour and variability properties in gamma. We examine a relationship of gamma flares with polarization angle rotations relying on robust statistical criteria. We analyse also the optical polarization variability itself in order to establish some restrictions on physical models of blazars jets

RoboPol: a four-channel optical imaging polarimeter

We present the design and performance of RoboPol, a four-channel optical polarimeter operating at the Skinakas Observatory in Crete, Greece. RoboPol is capable of measuring both relative linear Stokes parameters q and u (and the total intensity I) in one sky exposure. Though primarily used to measure the polarization of point sources in the R band, the instrument features additional filters (B, V, and I), enabling multiwavelength imaging polarimetry over a large field of view (13.6' x 13.6'). We demonstrate the accuracy and stability of the instrument throughout its 5 yr of operation. Best performance is achieved within the central region of the field of view and in the R band. For such measurements the systematic uncertainty is below 0.1 per cent in fractional linear polarization, p (0.05 per cent maximum likelihood). Throughout all observing seasons the instrumental polarization varies within 0.1 per cent in p and within similar to 1 degrees in polarization angle

Reactive Sulfur Species in Biology and Medicine

Hydrogen sulfide (H2S) has emerged as a third small-molecule bioactive signaling agent, along with nitric oxide (NO) and carbon monoxide (CO) [...

Parallel implementation of AutoDock

Computational docking of ligands to protein structures is a key step in structure-based drug design. Currently, the time required for each docking run is high and thus limits the use of docking in a high-throughput manner, warranting parallelization of docking algorithms. AutoDock, a widely used tool, has been chosen for parallelization. Near-linear increases in speed were observed with 96 processors, reducing the time required for docking ligands to HIV-protease from 81 min, as an example, on a single IBM Power-5 processor ( 1.65 GHz), to about 1 min on an IBM cluster, with 96 such processors. This implementation would make it feasible to perform virtual ligand screening using AutoDock