Grid computing and molecular simulations: the vision of the eMinerals project

This paper discusses a number of aspects of using grid computing methods in support of molecular simulations, with examples drawn from the eMinerals project. A number of components for a useful grid infrastructure are discussed, including the integration of compute and data grids, automatic metadata capture from simulation studies, interoperability of data between simulation codes, management of data and data accessibility, management of jobs and workflow, and tools to support collaboration. Use of a grid infrastructure also brings certain challenges, which are discussed. These include making use of boundless computing resources, the necessary changes, and the need to be able to manage experimentation.     

Squid – a simple bioinformatics grid

Background  

BLAST is a widely used genetic research tool for analysis of similarity between nucleotide and protein sequences. This paper presents a software application entitled "Squid" that makes use of grid technology. The current version, as an example, is configured for BLAST applications, but adaptation for other computing intensive repetitive tasks can be easily accomplished in the open source version. This enables the allocation of remote resources to perform distributed computing, making large BLAST queries viable without the need of high-end computers.     

Creating realistic silica nanopores for molecular dynamics simulations

We present a detailed approach to create realistic silica pores for computer simulations especially molecular dynamics (MD) simulations. These pores are essential for all different kinds of simulations with liquids in silica confinements. Despite wide use of silica pores in simulations, a detailed documentation how to create these pores for simulations still lacks. This issue is of high significance because with the help of this paper every researcher can build own silica pores with desired geometries and is not stick to already existing pores. We discuss problems that might occur during the whole process and how to solve these problems. So far more than 3 different silica pores have been created with this method and used successfully as confinement material in MD simulations.     

Validation of Monte carlo Geant4 multithreading code for a 6 MV photon beam of varian linac on the grid computing

AimTo evaluate the computation time efficiency of the multithreaded code (G4Linac-MT) in the dosimetry application, using the high performance of the HPC-Marwan grid to determine with high accuracy the initial parameters of the 6 MV photon beam of Varian CLINAC 2100C.BackgroundThe difficulty of Monte Carlo methods is the long computation time, this is one of the disadvantages of the Monte Carlo methods.Materials and methodsCalculations are performed by the multithreaded code G4Linac-MT and Geant4.10.04.p02 using the HPC-Marwan computing grid to evaluate the computing speed for each code. The multithreaded version is tested in several CPUs to evaluate the computing speed according to the number of CPUs used. The results were compared to the measurements using different types of comparisons, TPR_20.10, penumbra, mean dose error and gamma index.ResultsThe results obtained for this work indicate a much higher computing time saving for the G4Linac-MT version compared to the Geant4.10.04 version, the computing time decreases with the number of CPUs used, can reach about 12 times if 64CPUs are used. After optimization of the initial electron beam parameters, the results of the dose simulations obtained for this work are in very good agreement with the experimental measurements with a mean dose error of up to 0.41% on the PDDs and 1.79% on the lateral dose.ConclusionsThe gain in computation time leads us to perform Monte Carlo simulations with a large number of events which gives a high accuracy of the dosimetry results obtained in this work.     

Search for β2 Adrenergic Receptor Ligands by Virtual Screening via Grid Computing and Investigation of Binding Modes by Docking and Molecular Dynamics Simulations

We designed a program called MolGridCal that can be used to screen small molecule database in grid computing on basis of JPPF grid environment. Based on MolGridCal program, we proposed an integrated strategy for virtual screening and binding mode investigation by combining molecular docking, molecular dynamics (MD) simulations and free energy calculations. To test the effectiveness of MolGridCal, we screened potential ligands for β₂ adrenergic receptor (β₂AR) from a database containing 50,000 small molecules. MolGridCal can not only send tasks to the grid server automatically, but also can distribute tasks using the screensaver function. As for the results of virtual screening, the known agonist BI-167107 of β₂AR is ranked among the top 2% of the screened candidates, indicating MolGridCal program can give reasonable results. To further study the binding mode and refine the results of MolGridCal, more accurate docking and scoring methods are used to estimate the binding affinity for the top three molecules (agonist BI-167107, neutral antagonist alprenolol and inverse agonist ICI 118,551). The results indicate agonist BI-167107 has the best binding affinity. MD simulation and free energy calculation are employed to investigate the dynamic interaction mechanism between the ligands and β₂AR. The results show that the agonist BI-167107 also has the lowest binding free energy. This study can provide a new way to perform virtual screening effectively through integrating molecular docking based on grid computing, MD simulations and free energy calculations. The source codes of MolGridCal are freely available at http://molgridcal.codeplex.com.     

网格技术在生命科学中的应用研究

生命科学尤其是生物信息学中庞大的数据处理和高性能计算大大超出了某一单个机构的计算能力,而网格技术的出现使这些困难应刃而解,并逐渐成为生命科学标准的网络基础.从计算网格、数据网格和知识网格3个方面综述了最新的网格技术.最后展望了网格技术在生命科学领域中广阔的应用前景.     

Greedy replica exchange algorithm for heterogeneous computing grids

Replica exchange molecular dynamics (REMD) has become a valuable tool in studying complex biomolecular systems. However, its application on distributed computing grids is limited by the heterogeneity of this environment. In this study, we propose a REMD implementation referred to as greedy REMD (gREMD) suitable for computations on heterogeneous grids. To decentralize replica management, gREMD utilizes a precomputed schedule of exchange attempts between temperatures. Our comparison of gREMD against standard REMD suggests four main conclusions. First, gREMD accelerates grid REMD simulations by as much as 40 %. Second, gREMD increases CPU utilization rates in grid REMD by up to 60 %. Third, we argue that gREMD is expected to maintain approximately constant CPU utilization rates and simulation wall-clock times with the increase in the number of replicas. Finally, we show that gREMD correctly implements the REMD algorithm and reproduces the conformational ensemble of a short peptide sampled in our previous standard REMD simulations. We believe that gREMD can find its place in large-scale REMD simulations on heterogeneous computing grids.

Open image in new window

Graphical Abstract Standard replica exchange molecular dynamics (REMD) typically requires all replicas to complete prior to initiation of the replica exchange protocol. Greedy REMD decentralizes this process and therefore only requires a replica and its predetermined exchange partner to have finished simulations prior to initiating replica exchange. Because greedy REMD reduces the idle time associated with replica exchange tasks, it becomes particularly well suited for performing REMD on heterogeneous distributed computing environments.

     

A computational study of liposome logic: towards cellular computing from the bottom up

In this paper we propose a new bottom-up approach to cellular computing, in which computational chemical processes are encapsulated within liposomes. This “liposome logic” approach (also called vesicle computing) makes use of supra-molecular chemistry constructs, e.g. protocells, chells, etc. as minimal cellular platforms to which logical functionality can be added. Modeling and simulations feature prominently in “top-down” synthetic biology, particularly in the specification, design and implementation of logic circuits through bacterial genome reengineering. The second contribution in this paper is the demonstration of a novel set of tools for the specification, modelling and analysis of “bottom-up” liposome logic. In particular, simulation and modelling techniques are used to analyse some example liposome logic designs, ranging from relatively simple NOT gates and NAND gates to SR-Latches, D Flip-Flops all the way to 3 bit ripple counters. The approach we propose consists of specifying, by means of P systems, gene regulatory network-like systems operating inside proto-membranes. This P systems specification can be automatically translated and executed through a multiscaled pipeline composed of dissipative particle dynamics (DPD) simulator and Gillespie’s stochastic simulation algorithm (SSA). Finally, model selection and analysis can be performed through a model checking phase. This is the first paper we are aware of that brings to bear formal specifications, DPD, SSA and model checking to the problem of modeling target computational functionality in protocells. Potential chemical routes for the laboratory implementation of these simulations are also discussed thus for the first time suggesting a potentially realistic physiochemical implementation for membrane computing from the bottom-up.     

Design and analysis of a distributed grid resource discovery protocol

Computational grids have been emerging as a new paradigm for solving large complex problems over the recent years. The problem space and data set are divided into smaller pieces that are processed in parallel over the grid network and reassembled upon completion. Typically, resources are logged into a resource broker that is somewhat aware of all of the participants available on the grid. The resource broker scheme can be a bottleneck because of the amount of computational power and network bandwidth needed to maintain a fresh view of the grid. In this paper, we propose to place the load of managing the network resource discovery on to the network itself: inside of the routers. In the proposed protocol, the routers contain tables for resources similar to routing tables. These resource tables map IP addresses to the available computing resource values, which are provided through a scoring mechanism. Each resource provider is scored based on the attributes they provide such as the number of processors, processor frequency, amount of memory, hard drive space, and the network bandwidth. The resources are discovered on the grid by the protocol’s discovery packets, which are encapsulated within the TCP/IP packets. The discovery packet visits the routers and look up in the resource tables until a satisfactory resource is found. The protocol is validated by simulations with five different deployment environments.     

Security of grid structures under disguised traffic attacks

Models of rectangular grid structures were constructed in the form of a colored Petri net. The basic model consists of a matrix of switching nodes that deliver packets to computing nodes which are attached to the matrix borders and produce and consume packets. Since grid structures are often employed to solve boundary value problems, square and torus surfaces were studied and generalized to hypercube and hypertorus in multidimensional space using a grid node that aggregates switching and computing nodes. Traffic guns were added to the models to represent traffic attacks. Simulation in CPN Tools revealed simple and dangerous traffic gun configurations, such as a traffic duel, focus, crossfire, and side shot, which bring the grid to complete deadlock at less than 5 % of the grid peak load. Comparably low gun intensity targeted to induce deadlock areas within a grid (network) is a key characteristic of disguised traffic attacks. The aim of future work will be to develop counter-measures for these attacks.     

Simulating convergent extension by way of anisotropic differential adhesion

Simulations using the Extended Potts Model suggest that anisotropic differential adhesion can account for convergent extension, as observed during embryonic development of the frog Xenopus laevis for example. During gastrulation in these frogs, convergent extension produces longitudinal tissue growth from latitudinal elongation and migration of aligned constituent cells. The Extended Potts Model employs clustered points on a grid to represent subdivided cells with probabilistic displacement of cell boundaries such that small changes in energy drive gradual tissue development. For modeling convergent extension, simulations include anisotropic differential adhesion: the degree of attachment between adjacent elongated cells depends on their relative orientation. Without considering additional mechanisms, simulations based on anisotropic differential adhesion reproduce the hallmark stages of convergent extension in the correct sequence, with random fluctuations as sufficient impetus for cell reorganization.     

Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories

The long-term goal of connecting scales in biological simulation can be facilitated by scale-agnostic methods. We demonstrate that the weighted ensemble (WE) strategy, initially developed for molecular simulations, applies effectively to spatially resolved cell-scale simulations. The WE approach runs an ensemble of parallel trajectories with assigned weights and uses a statistical resampling strategy of replicating and pruning trajectories to focus computational effort on difficult-to-sample regions. The method can also generate unbiased estimates of non-equilibrium and equilibrium observables, sometimes with significantly less aggregate computing time than would be possible using standard parallelization. Here, we use WE to orchestrate particle-based kinetic Monte Carlo simulations, which include spatial geometry (e.g., of organelles, plasma membrane) and biochemical interactions among mobile molecular species. We study a series of models exhibiting spatial, temporal and biochemical complexity and show that although WE has important limitations, it can achieve performance significantly exceeding standard parallel simulation—by orders of magnitude for some observables.     

Molecular dynamics simulation of water confined in a nanopore of amorphous silica

Molecular dynamics simulations are performed to study the transport and structural properties of water confined in a cylindrical silica nanopore. The pore wall is amorphous and mimics a typical mesoporous silica material. The diameters of silica pores studied are 4.75, 9.51, 20 and 25 Å. The self-diffusion of water calculated decreases with pore size and indicates much slower transport compared to the bulk phase. Strong adsorption of water to the silica wall is observed in the density profiles, indicating the hydrophilic nature of the wall. The hydrogen-bonding network is strongly affected by water–silica wall interaction. The average number of hydrogen bonds per water decreased with decreasing pore diameter.     

A communication-efficient linear system solver for large eddy simulation of jet engine noise

High-fidelity computational fluid dynamics (CFD) tools, such as the large eddy simulation technique, have become feasible in aiding the field of computational aeroacoustics (CAA) to compute noise on petascale computing platforms. CAA poses significant challenges for researchers because the computational schemes used in the CFD tools should have high accuracy, good spectral resolution, and low dispersion and diffusion errors. A high-order compact finite difference scheme, which is implicit in space, can be used for such simulations because it fulfills the requirements for CAA. Usually, this method is parallelized using a transposition scheme; however, that approach has a high communication overhead. In this paper, we discuss the use of a parallel tridiagonal linear system solver based on the truncated SPIKE algorithm for reducing the communication overhead in our large eddy simulations. We present theoretical performance analysis and report experimental results collected on two parallel computing platforms.     

GLAD: a system for developing and deploying large-scale bioinformatics grid

MOTIVATION: Grid computing is used to solve large-scale bioinformatics problems with gigabytes database by distributing the computation across multiple platforms. Until now in developing bioinformatics grid applications, it is extremely tedious to design and implement the component algorithms and parallelization techniques for different classes of problems, and to access remotely located sequence database files of varying formats across the grid. In this study, we propose a grid programming toolkit, GLAD (Grid Life sciences Applications Developer), which facilitates the development and deployment of bioinformatics applications on a grid. RESULTS: GLAD has been developed using ALiCE (Adaptive scaLable Internet-based Computing Engine), a Java-based grid middleware, which exploits the task-based parallelism. Two bioinformatics benchmark applications, such as distributed sequence comparison and distributed progressive multiple sequence alignment, have been developed using GLAD.     

Empirical Study of an Adaptive Multiscale Model for Simulating Cardiac Conduction

We modify and empirically study an adaptive multiscale model for simulating cardiac action potential propagation along a strand of cardiomyocytes. The model involves microscale partial differential equations posed over cells near the action potential upstroke and macroscale partial differential equations posed over the remainder of the tissue. An important advantage of the modified model of this paper is that, unlike our original model, it does not require perfect alignment between myocytes and the macroscale computational grid. We study the effects of gap-junctional coupling, ephaptic coupling, and macroscale grid spacing on the accuracy of the multiscale model. Our simulations reveal that the multiscale method accurately reproduces both the wavespeed and the waveform, including both upstroke and recovery, of fully microscale models. They also reveal that perfect alignment between myocytes and the macroscale grid is not necessary to reproduce the dynamics of a traveling action potential. Further, our simulations suggest that the macroscale grid spacing used in an adaptive multiscale model need not be much finer than the spatial width of an action potential. These results are demonstrated to hold under high, low, and zero gap-junctional coupling regimes.     

Cactus Tools for Grid Applications

Cactus is an open source problem solving environment designed for scientists and engineers. Its modular structure facilitates parallel computation across different architectures and collaborative code development between different groups. The Cactus Code originated in the academic research community, where it has been developed and used over many years by a large international collaboration of physicists and computational scientists. We discuss here how the intensive computing requirements of physics applications now using the Cactus Code encourage the use of distributed and metacomputing, and detail how its design makes it an ideal application test-bed for Grid computing. We describe the development of tools, and the experiments which have already been performed in a Grid environment with Cactus, including distributed simulations, remote monitoring and steering, and data handling and visualization. Finally, we discuss how Grid portals, such as those already developed for Cactus, will open the door to global computing resources for scientific users.     

Application of a changing field of growth rates to a description of root apex formation

Based on the growth tensor method an unsteady field of growth rates for developing root apex is presented. Maps of growth rates distribution as well as simulations in which the field was applied to initially uniform grid of points are presented. In the simulations, the grid undergoes deformation that resembles new root formation in its axial plane. Four variants of field operation on the grid are shown.     

Standard errors for EM estimates in generalized linear models with random effects

A procedure is derived for computing standard errors of EM estimates in generalized linear models with random effects. Quadrature formulas are used to approximate the integrals in the EM algorithm, where two different approaches are pursued, i.e., Gauss-Hermite quadrature in the case of Gaussian random effects and nonparametric maximum likelihood estimation for an unspecified random effect distribution. An approximation of the expected Fisher information matrix is derived from an expansion of the EM estimating equations. This allows for inferential arguments based on EM estimates, as demonstrated by an example and simulations.