BDCGrid:基于Globus的生物信息高性能计算网格系统

随着分子生物信息数据量高速增长,生物信息学面临着大规模、高通量、密集型计算的巨大挑战。为有效利用计算机资源,缩短高通量生物信息计算程序执行时间,我们基于Globus Toolkit网格中间件,实现了一个支持高通量生物数据计算的网格系统(Biological Data Computing Grid,简称BDCGrid)。BDCGrid计算网格系统模型可以有效整合中小型生物信息学实验室计算机资源,大大缩短高通量生物信息计算程序执行时间,为相关研究人员利用现有计算机资源处理大规模、高通量生物信息计算任务提供一种新的途径。     

Bio-Imitation of Mexican Migration Routes to the USA with Slime Mould on 3D Terrains

Plasmodium of Physarum polycephalum (P. polycephalum) is a large single cell visible by an unaided eye. It shows sophisticated behavioural traits in foraging for nutrients and developing an optimal transport network of protoplasmic tubes spanning sources of nutrients. When placed in an environment with distributed sources of nutrients the cell ‘computes’ an optimal graph spanning the nutrients by growing a network of protoplasmic tubes. P. polycephalum imitates development of man-made transport networks of a country when configuration of nutrients represents major urban areas. We employed this feature of the slime mould to imitate mexican migration to USA. The Mexican migration to USA is the World's largest migration system. We bio-physically imitated the migration using slime mould P. polycephalum. In laboratory experiments with 3D Nylon terrains of USA we imitated development of migratory routes from Mexico-USA border to ten urban areas with high concentration of Mexican migrants. From results of laboratory experiments we extracted topologies of migratory routes, and highlighted a role of elevations in shaping the human movement networks.     

Converting differential-equation models of biological systems to membrane computing

This paper presents a method to convert the deterministic, continuous representation of a biological system by ordinary differential equations into a non-deterministic, discrete membrane computation. The dynamics of the membrane computation is governed by rewrite rules operating at certain rates. That has the advantage of applying accurately to small systems, and to expressing rates of change that are determined locally, by region, but not necessary globally. Such spatial information augments the standard differentiable approach to provide a more realistic model. A biological case study of the ligand–receptor network of protein TGF-β is used to validate the effectiveness of the conversion method. It demonstrates the sense in which the behaviours and properties of the system are better preserved in the membrane computing model, suggesting that the proposed conversion method may prove useful for biological systems in particular.     

Generation of initial molecular dynamics configurations in arbitrary geometries and in parallel

A computational pre-processing tool for generating initial configurations of molecules for molecular dynamics simulations in geometries described by a mesh of unstructured arbitrary polyhedra is described. The mesh is divided into separate zones and each can be filled with a single crystal lattice of atoms. Each zone is filled by creating an expanding cube of crystal unit cells, initiated from an anchor point for the lattice. Each unit cell places the appropriate atoms for the user-specified crystal structure and orientation. The cube expands until the entire zone is filled with the lattice; zones with concave and disconnected volumes may be filled. When the mesh is spatially decomposed into portions for distributed parallel processing, each portion may be filled independently, meaning that the entire molecular system never needs to fit onto a single processor, allowing very large systems to be created. The computational time required to fill a zone with molecules scales linearly with the number of cells in the zone for a fixed number of molecules, and better than linearly with the number of molecules for a fixed number of mesh cells. Our tool, molConfig, has been implemented in the open source C++ code OpenFOAM.     

Application of Artificial Bee Colony Algorithm to Maximum Likelihood DOA Estimation

Maximum Likelihood (ML) method has an excellent performance for Direction-Of-Arrival (DOA) estimation, but a multidimensional nonlinear solution search is required which complicates the computation and prevents the method from practical use. To reduce the high computational burden of ML method and make it more suitable to engineering applications, we apply the Artificial Bee Colony (ABC) algorithm to maximize the likelihood function for DOA estimation. As a recently proposed bio-inspired computing algorithm, ABC algorithm is originally used to optimize multivariable functions by imitating the behavior of bee colony finding excellent nectar sources in the nature environment. It offers an excellent alternative to the conventional methods in ML-DOA estimation. The performance of ABC-based ML and other popular meta-heuristic-based ML methods for DOA estimation are compared for various scenarios of convergence, Signal-to-Noise Ratio (SNR), and number of iterations. The computation loads of ABC-based ML and the conventional ML methods for DOA estimation are also investigated. Simulation results demonstrate that the proposed ABC based method is more efficient in computation and statistical performance than other ML-based DOA estimation methods.     

EDISON-WMW: Exact Dynamic Programing Solution of the Wilcoxon–Mann–Whitney Test

In many research disciplines, hypothesis tests are applied to evaluate whether findings are statistically significant or could be explained by chance. The Wilcoxon–Mann–Whitney(WMW) test is among the most popular hypothesis tests in medicine and life science to analyze if two groups of samples are equally distributed. This nonparametric statistical homogeneity test is commonly applied in molecular diagnosis. Generally, the solution of the WMW test takes a high combinatorial effort for large sample cohorts containing a significant number of ties. Hence, P value is frequently approximated by a normal distribution. We developed EDISON-WMW, a new approach to calculate the exact permutation of the two-tailed unpaired WMW test without any corrections required and allowing for ties. The method relies on dynamic programing to solve the combinatorial problem of the WMW test efficiently. Beyond a straightforward implementation of the algorithm, we presented different optimization strategies and developed a parallel solution. Using our program,the exact P value for large cohorts containing more than 1000 samples with ties can be calculated within minutes. We demonstrate the performance of this novel approach on randomly-generated data, benchmark it against 13 other commonly-applied approaches and moreover evaluate molecular biomarkers for lung carcinoma and chronic obstructive pulmonary disease(COPD). We foundthat approximated P values were generally higher than the exact solution provided by EDISONWMW. Importantly, the algorithm can also be applied to high-throughput omics datasets, where hundreds or thousands of features are included. To provide easy access to the multi-threaded version of EDISON-WMW, a web-based solution of our algorithm is freely available at http://www.ccb.uni-saarland.de/software/wtest/.     

Simulation of 3D tumor cell growth using nonlinear finite element method

We propose a novel parallel computing framework for a nonlinear finite element method (FEM)-based cell model and apply it to simulate avascular tumor growth. We derive computation formulas to simplify the simulation and design the basic algorithms. With the increment of the proliferation generations of tumor cells, the FEM elements may become larger and more distorted. Then, we describe a remesh and refinement processing of the distorted or over large finite elements and the parallel implementation based on Message Passing Interface to improve the accuracy and efficiency of the simulation. We demonstrate the feasibility and effectiveness of the FEM model and the parallelization methods in simulations of early tumor growth.     

适用于磁压榨吻合的平移钳的设计

目的:提出了磁压榨吻合操作中可用于磁体夹持固定的平移钳的结构设计方案。方法:分析本团队多种原创性磁压榨吻合技术操作中存在的缺陷和不足,认为目前磁压榨吻合操作中依靠术者徒手控制或利用现有血管钳对磁体进行夹持和固定,是造成操作中诸多不便的主要原因。从力学角度分析了常规血管钳夹持磁体易导致磁体滑脱的原因,指出设计加工磁吻合专用钳是解决目前磁吻合操作不便的有效措施。在此基础上提出了利用平移钳来固定磁体的结构设计方案,并从力学角度进行了受力分析,设计出了用于磁压榨吻合操作中磁体夹持固定的平移钳。结果:平移钳的结构设计能够稳定夹持磁体,避免磁体滑脱;同时,借助齿轮传动结构控制钳头的平行移动能够更好地控制磁体的精准对吸和分离,可进一步简化操作,避免副损伤,节省手术操作时间。结论:平移钳能满足多种形状及大小的磁体的夹持和固定,可有效控制磁体的吸合与分离,极大地方便操作。该平移钳加工简单,使用方便,有助于推动磁压榨吻合技术在临床广泛开展。     

Acceleration of cryo-EM Flexible Fitting for Large Biomolecular Systems by Efficient Space Partitioning

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Scalable remote homology detection and fold recognition in massive protein networks

The global connectivities in very large protein similarity networks contain traces of evolution among the proteins for detecting protein remote evolutionary relations or structural similarities. To investigate how well a protein network captures the evolutionary information, a key limitation is the intensive computation of pairwise sequence similarities needed to construct very large protein networks. In this article, we introduce label propagation on low-rank kernel approximation (LP-LOKA) for searching massively large protein networks. LP-LOKA propagates initial protein similarities in a low-rank graph by Nyström approximation without computing all pairwise similarities. With scalable parallel implementations based on distributed-memory using message-passing interface and Apache-Hadoop/Spark on cloud, LP-LOKA can search protein networks with one million proteins or more. In the experiments on Swiss-Prot/ADDA/CASP data, LP-LOKA significantly improved protein ranking over the widely used HMM-HMM or profile-sequence alignment methods utilizing large protein networks. It was observed that the larger the protein similarity network, the better the performance, especially on relatively small protein superfamilies and folds. The results suggest that computing massively large protein network is necessary to meet the growing need of annotating proteins from newly sequenced species and LP-LOKA is both scalable and accurate for searching massively large protein networks.