Multi-agent model of hepatitis C virus infection

Objectives: The objective of this study is to design a method for modeling hepatitis C virus (HCV) infection using multi-agent simulation and to verify it in practice. Methods and materials: In this paper, first, the modeling of HCV infection using a multi-agent system is compared with the most commonly used model type, which is based on differential equations. Then, the implementation and results of the model using a multi-agent simulation is presented. To find the values of the parameters used in the model, a method using inverted simulation flow and genetic algorithm is proposed. All of the data regarding HCV infection are taken from the paper describing the model based on the differential equation to which the proposed method is compared. Results: Important advantages of the proposed method are noted and demonstrated; these include flexibility, clarity, re-usability and the possibility to model more complex dependencies. Then, the simulation framework that uses the proposed approach is successfully implemented in C++ and is verified by comparing it to the approach based on differential equations. The verification proves that an objective function that performs the best is the function that minimizes the maximal differences in the data. Finally, an analysis of one of the already known models is performed, and it is proved that it incorrectly models a decay in the hepatocytes number by 40%. Conclusions: The proposed method has many advantages in comparison to the currently used model types and can be used successfully for analyzing HCV infection. With almost no modifications, it can also be used for other types of viral infections

RNA FRABASE 2.0: an advanced web-accessible database with the capacity to search the three-dimensional fragments within RNA structures

Background: Recent discoveries concerning novel functions of RNA, such as RNA interference, have contributed towards the growing importance of the field. In this respect, a deeper knowledge of complex three-dimensional RNA structures is essential to understand their new biological functions. A number of bioinformatic tools have been proposed to explore two major structural databases (PDB, NDB) in order to analyze various aspects of RNA tertiary structures. One of these tools is RNA FRABASE 1.0, the first web-accessible database with an engine for automatic search of 3D fragments within PDB-derived RNA structures. This search is based upon the user-defined RNA secondary structure pattern. In this paper, we present and discuss RNA FRABASE 2.0. This second version of the system represents a major extension of this tool in terms of providing new data and a wide spectrum of novel functionalities. An intuitionally operated web server platform enables very fast user-tailored search of three-dimensional RNA fragments, their multi-parameter conformational analysis and visualization. Description: RNA FRABASE 2.0 has stored information on 1565 PDB-deposited RNA structures, including all NMR models. The RNA FRABASE 2.0 search engine algorithms operate on the database of the RNA sequences and the new library of RNA secondary structures, coded in the dot-bracket format extended to hold multi-stranded structures and to cover residues whose coordinates are missing in the PDB files. The library of RNA secondary structures (and their graphics) is made available. A high level of efficiency of the 3D search has been achieved by introducing novel tools to formulate advanced searching patterns and to screen highly populated tertiary structure elements. RNA FRABASE 2.0 also stores data and conformational parameters in order to provide "on the spot" structural filters to explore the three-dimensional RNA structures. An instant visualization of the 3D RNA structures is provided. RNA FRABASE 2.0 is freely available at http://rnafrabase.cs.put.poznan.pl webcite. Conclusions: RNA FRABASE 2.0 provides a novel database and powerful search engine which is equipped with new data and functionalities that are unavailable elsewhere. Our intention is that this advanced version of the RNA FRABASE will be of interest to all researchers working in the RNA field

A Massive Data Parallel Computational Framework for Petascale/Exascale Hybrid Computer Systems

Heterogeneous systems are becoming more common on High Performance Computing (HPC) systems. Even using tools like CUDA and OpenCL it is a non-trivial task to obtain optimal performance on the GPU. Approaches to simplifying this task include Merge (a library based framework for heterogeneous multi-core systems), Zippy (a framework for parallel execution of codes on multiple GPUs), BSGP (a new programming language for general purpose computation on the GPU) and CUDA-lite (an enhancement to CUDA that transforms code based on annotations). In addition, efforts are underway to improve compiler tools for automatic parallelization and optimization of affine loop nests for GPUs and for automatic translation of OpenMP parallelized codes to CUDA. In this paper we present an alternative approach: a new computational framework for the development of massively data parallel scientific codes applications suitable for use on such petascale/exascale hybrid systems built upon the highly scalable Cactus framework. As the first non-trivial demonstration of its usefulness, we successfully developed a new 3D CFD code that achieves improved performance.Comment: Parallel Computing 2011 (ParCo2011), 30 August -- 2 September 2011, Ghent, Belgiu

From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation

Starting from a high-level problem description in terms of partial differential equations using abstract tensor notation, the Chemora framework discretizes, optimizes, and generates complete high performance codes for a wide range of compute architectures. Chemora extends the capabilities of Cactus, facilitating the usage of large-scale CPU/GPU systems in an efficient manner for complex applications, without low-level code tuning. Chemora achieves parallelism through MPI and multi-threading, combining OpenMP and CUDA. Optimizations include high-level code transformations, efficient loop traversal strategies, dynamically selected data and instruction cache usage strategies, and JIT compilation of GPU code tailored to the problem characteristics. The discretization is based on higher-order finite differences on multi-block domains. Chemora's capabilities are demonstrated by simulations of black hole collisions. This problem provides an acid test of the framework, as the Einstein equations contain hundreds of variables and thousands of terms.Comment: 18 pages, 4 figures, accepted for publication in Scientific Programmin

A Massive Data Parallel Computational Framework for Petascale/Exascale Hybrid Computer Systems

large scale scientific applicatio

Computational prediction of non-enzymatic RNA degradation patterns

Since the beginning of the 21st century, an increasing interest in the research of ribonucleic acids has been observed in response to a surprising discovery of the role that RNA molecules play in the biological systems. It was demonstrated that they do not only take part in the protein synthesis (mRNA, rRNA, tRNA) but also are involved in the regulation of gene expression. Several classes of small regulatory RNAs have been discovered (e.g. microRNA, small interfering RNA, piwiRNA). Most of them are excised from specific double-stranded RNA precursors by enzymes that belong to the RNaseIII family (Drosha, Dicer or Dicer-like proteins). More recently, it has been shown that small regulatory RNAs are also generated as stable intermediates of RNA degradation (the so called RNA fragments originating from tRNA, snRNA, snoRNA etc.). Unfortunately, the mechanisms underlying biogenesis of the RNA fragments remain unclear. It is thought that several factors may be involved in the formation of the RNA fragments. The most important are the specific RNases, RNA-protein interactions and RNA structure. In this work, we focus on the RNA primary and secondary structures as factors influencing the RNA stability and consequently the pattern of RNA fragmentation. Earlier, we identified the major structural factors affecting non-enzymatic RNA degradation. Now, based on these data, we developed a new branch-and-cut algorithm that is able to predict the products of large RNA molecules' hydrolysis in vitro. We also present the experimental data that verify the results generated using this algorithm

AmiRNA Designer - new method of artificial miRNA design

MicroRNAs (miRNAs) are small non-coding RNAs that have been found in most of the eukaryotic organisms. They are involved in the regulation of gene expression at the post-transcriptional level in a sequence specific manner. MiRNAs are produced from their precursors by Dicer-dependent small RNA biogenesis pathway. Involvement of miRNAs in a wide range of biological processes makes them excellent candidates for studying gene function or for therapeutic applications. For this purpose, different RNA-based gene silencing techniques have been developed. Artificially transformed miRNAs (amiRNAs) targeting one or several genes of interest represent one of such techniques being a potential tool in functional genomics. Here, we present a new approach to amiRNA*design, implemented as AmiRNA Designer software. Our method is based on the thermodynamic analysis of the native miRNA/miRNA* and miRNA/target duplexes. In contrast to the available automated tools, our program allows the user to perform analysis of natural miRNAs for the organism of interest and to create customized constraints for the design stage. It also provides filtering of the amiRNA candidates for the potential off-targets. AmiRNA Designer is freely available at http://www.cs.put.poznan.pl/arybarczyk/AmiRNA/

Chemora: A PDE-solving framework for modern high-performance computing architectures

Modern HPC architectures consist of heterogeneous multicore, many-node systems with deep memory hierarchies. Modern applications employ ever more advanced discretization methods to study multiphysics problems. Developing such applications that explore cutting-edge physics on cutting-edge HPC systems has become a complex task that requires significant HPC knowledge and experience. Unfortunately, this combined knowledge is currently out of reach for all but a few groups of application developers. Chemora is a framework for solving systems of partial differential equations (PDEs) that targets modern HPC architectures. Chemora is based on Cactus, which sees prominent usage in the computational relativistic astrophysics community. In Chemora, PDEs are expressed either in high-level LaTeX-like languages or in Mathematica. The authors use Chemora in the Einstein Toolkit to implement the Einstein equations on CPUs and on accelerators, and study astrophysical systems such as black hole binaries, neutron stars, and core-collapse supernovae