Equipment ontology for modular reconfigurable assembly systems

Evolvable and Reconfigurable Assembly Systems (RAS) enable enterprises to rapidly respond to changes in today’s increasingly volatile and dynamic global markets. One of the key success factors for the effective use of RAS is methods and tools that can rapidly configure and reconfigure assembly systems driven by changing requirements. The focus of this paper is the development of a suitable equipment model to support the effective design of reconfigurable assembly systems. The work has been motivated by the need to provide solutions for increasing product customisation and volume changes over the product life-cycle that directly impact on the final product assembly. The paper proposes a comprehensive equipment ontology to enable effective decision-making during the design and evaluation of new RAS configurations. The proposed ontology is based on the function-behaviour-structure paradigm, and is formalised to facilitate its application in distributed web-enabled decision-making environments. The equipment configuration and reconfiguration approach and prototype decision-making environment are illustrated using system design examples.     

Reconfigurable modular automation systems for automotive power-train manufacture

This paper describes research towards the realization of reconfigurable modular automated machines and the associated engineering methods and tools necessary to support their lifecycle needs. UK-based research, in collaboration with the Ford Motor Company and several machine builders, has resulted in the development of full-scale prototype reconfigurable modular automation systems for both engine assembly and machining applications. The implementation of an assembly system is featured in this paper. An engineering environment and associated reconfigurable component-based control system architecture have been created aimed at supporting the lifecycle needs of a new generation of agile automated systems, i.e., providing reconfigurable, easily scalable automated machinery. This approach has the potential to fit within a wider collaborative automation strategy where manufacturing systems are implemented as a conglomerate of distributed, autonomous, and reusable units.     

pTransgenesis: a cross-species, modular transgenesis resource

As studies aim increasingly to understand key, evolutionarily conserved properties of biological systems, the ability to move transgenesis experiments efficiently between organisms becomes essential. DNA constructions used in transgenesis usually contain four elements, including sequences that facilitate transgene genome integration, a selectable marker and promoter elements driving a coding gene. Linking these four elements in a DNA construction, however, can be a rate-limiting step in the design and creation of transgenic organisms. In order to expedite the construction process and to facilitate cross-species collaborations, we have incorporated the four common elements of transgenesis into a modular, recombination-based cloning system called pTransgenesis. Within this framework, we created a library of useful coding sequences, such as various fluorescent protein, Gal4, Cre-recombinase and dominant-negative receptor constructs, which are designed to be coupled to modular, species-compatible selectable markers, promoters and transgenesis facilitation sequences. Using pTransgenesis in Xenopus, we demonstrate Gal4-UAS binary expression, Cre-loxP-mediated fate-mapping and the establishment of novel, tissue-specific transgenic lines. Importantly, we show that the pTransgenesis resource is also compatible with transgenesis in Drosophila, zebrafish and mammalian cell models. Thus, the pTransgenesis resource fosters a cross-model standardization of commonly used transgenesis elements, streamlines DNA construct creation and facilitates collaboration between researchers working on different model organisms.     

Simulated parallel annealing within a neighborhood for optimization of biomechanical systems

Optimization problems for biomechanical systems have become extremely complex. Simulated annealing (SA) algorithms have performed well in a variety of test problems and biomechanical applications; however, despite advances in computer speed, convergence to optimal solutions for systems of even moderate complexity has remained prohibitive. The objective of this study was to develop a portable parallel version of a SA algorithm for solving optimization problems in biomechanics. The algorithm for simulated parallel annealing within a neighborhood (SPAN) was designed to minimize interprocessor communication time and closely retain the heuristics of the serial SA algorithm. The computational speed of the SPAN algorithm scaled linearly with the number of processors on different computer platforms for a simple quadratic test problem and for a more complex forward dynamic simulation of human pedaling.     

MetaSim: a sequencing simulator for genomics and metagenomics

Background

The new research field of metagenomics is providing exciting insights into various, previously unclassified ecological systems. Next-generation sequencing technologies are producing a rapid increase of environmental data in public databases. There is great need for specialized software solutions and statistical methods for dealing with complex metagenome data sets.

Methodology/Principal Findings

To facilitate the development and improvement of metagenomic tools and the planning of metagenomic projects, we introduce a sequencing simulator called MetaSim. Our software can be used to generate collections of synthetic reads that reflect the diverse taxonomical composition of typical metagenome data sets. Based on a database of given genomes, the program allows the user to design a metagenome by specifying the number of genomes present at different levels of the NCBI taxonomy, and then to collect reads from the metagenome using a simulation of a number of different sequencing technologies. A population sampler optionally produces evolved sequences based on source genomes and a given evolutionary tree.

Conclusions/Significance

MetaSim allows the user to simulate individual read datasets that can be used as standardized test scenarios for planning sequencing projects or for benchmarking metagenomic software.     

Desulfoferrodoxin: a modular protein

The gene encoding the non-heme iron-containing desulfoferrodoxin from Desulfovibrio vulgaris was cloned in two fragments in order to obtain polypeptides corresponding to the N- and C-terminal domains observed in the tertiary structure. These fragments were expressed in Escherichia coli, purified to homogeneity and biochemically and spectroscopically characterized. Both recombinant fragments behaved as independent metal-binding domains. The N-terminal fragment exhibited properties similar to desulforedoxin, as expected by the presence of a Fe(S-Cys)4 metal binding motif. The C-terminal fragment, which accommodates a Fe(Nepsilon-His)3(Ndelta-His)(S-Cys) center, was shown to have properties similar to neelaredoxin, except for the reaction with superoxide. The activities of desulfoferrodoxin and of the expressed C-terminal fragment were tested with superoxide in the presence and absence of cytochrome c. The results are consistent with superoxide reductase activity and a possible explanation for the low superoxide consumption in the superoxide dismutase activity assays is proposed.     

CellLine, a stochastic cell lineage simulator

Summary: We present CellLine, a simulator of the dynamics ofgene regulatory networks (GRN) in the cells of a lineage. Fromuser-defined reactions and initial substance quantities, itgenerates cell lineages, i.e. genealogic pedigrees of cellsrelated through mitotic division. Each cell's dynamics is drivenby a delayed stochastic simulation algorithm (delayed SSA),allowing multiple time delayed reactions. The cells of the lineage can be individually subject to ‘perturbations’,such as gene deletion, duplication and mutation. External interventions,such as adding or removing a substance at a given moment, canbe specified. Cell differentiation lineages, where differentiationis stochastically driven or externally induced, can be modeledas well. Finally, CellLine can generate and simulate the dynamicsof multiple copies of any given cell of the lineage. As examples of CellLine use, we simulate the following systems:cell lineages containing a model of the P53-Mdm2 feedback loop,a differentiation lineage where each cell contains a 4 generepressilator (a bistable circuit), a model of the differentiationof the cells of the retinal mosaic required for color visionin Drosophila melanogaster, where the differentiation pathwaydepends on one substance's concentration that is controlledby a stochastic process, and a 9 gene GRN to illustrate theadvantage of using CellLine rather than simulating multipleindependent cells, in cases where the cells of the lineage aredynamically correlated. Availability: The CellLine program, instructions and examplesare available at http://www.cs.tut.fi/~sanchesr/CellLine/CellLine.html Contact: andre.sanchesribeiro{at}tut.fi Associate Editor: Limsoon Wong     

Mini-scale bioprocessing systems for highly parallel animal cell cultures

Animal cells have been used extensively in therapeutic protein production. The growth of animal cells and the expression of therapeutic proteins are highly dependent on the culturing environments. A large number of experimental permutations need to be explored to identify the optimal culturing conditions. Miniaturized bioreactors are well suited for such tasks as they offer high-throughput parallel operation and reduce cost of reagents. They can also be automated and be coupled to downstream analytical units for online measurements of culture products. This review summarizes the current status of miniaturized bioreactors for animal cell cultivation based on the design categories: microtiter plates, flasks, stirred tank reactors, novel designs with active mixing, and microfluidic cell culture devices. We compare cell density and product titer, for batch or fed-batch modes for each system. Monitoring/controlling devices for engineering parameters such as pH, dissolved oxygen, and dissolved carbon dioxide, which could be applied to such systems, are summarized. Finally, mini-scale tools for process performance evaluation for animal cell cultures are discussed: total cell density, cell viability, product titer and quality, substrates, and metabolites profiles.     

Cost-intelligent application-specific data layout optimization for parallel file systems

Parallel file systems have been developed in recent years to ease the I/O bottleneck of high-end computing system. These advanced file systems offer several data layout strategies in order to meet the performance goals of specific I/O workloads. However, while a layout policy may perform well on some I/O workload, it may not perform as well for another. Peak I/O performance is rarely achieved due to the complex data access patterns. Data access is application dependent. In this study, a cost-intelligent data access strategy based on the application-specific optimization principle is proposed. This strategy improves the I/O performance of parallel file systems. We first present examples to illustrate the difference of performance under different data layouts. By developing a cost model which estimates the completion time of data accesses in various data layouts, the layout can better match the application. Static layout optimization can be used for applications with dominant data access patterns, and dynamic layout selection with hybrid replications can be used for applications with complex I/O patterns. Theoretical analysis and experimental testing have been conducted to verify the proposed cost-intelligent layout approach. Analytical and experimental results show that the proposed cost model is effective and the application-specific data layout approach can provide up to a 74% performance improvement for data-intensive applications.     

Energy cost evaluation of parallel algorithms for multiprocessor systems

With the continuous development of hardware and software, Graphics Processor Units (GPUs) have been used in the general-purpose computation field. They have emerged as a computational accelerator that dramatically reduces the application execution time with CPUs. To achieve high computing performance, a GPU typically includes hundreds of computing units. The high density of computing resource on a chip brings in high power consumption. Therefore power consumption has become one of the most important problems for the development of GPUs. This paper analyzes the energy consumption of parallel algorithms executed in GPUs and provides a method to evaluate the energy scalability for parallel algorithms. Then the parallel prefix sum is analyzed to illustrate the method for the energy conservation, and the energy scalability is experimentally evaluated using Sparse Matrix-Vector Multiply (SpMV). The results show that the optimal number of blocks, memory choice and task scheduling are the important keys to balance the performance and the energy consumption of GPUs.     

ART: a next-generation sequencing read simulator

ART is a set of simulation tools that generate synthetic next-generation sequencing reads. This functionality is essential for testing and benchmarking tools for next-generation sequencing data analysis including read alignment, de novo assembly and genetic variation discovery. ART generates simulated sequencing reads by emulating the sequencing process with built-in, technology-specific read error models and base quality value profiles parameterized empirically in large sequencing datasets. We currently support all three major commercial next-generation sequencing platforms: Roche's 454, Illumina's Solexa and Applied Biosystems' SOLiD. ART also allows the flexibility to use customized read error model parameters and quality profiles. AVAILABILITY: Both source and binary software packages are available at http://www.niehs.nih.gov/research/resources/software/art.     

GenomeMixer: a complex genetic cross simulator

GenomeMixer is a cross-platform application that simulates meiotic recombination events for large and complex multigenerational genetic crosses among sexually reproducing diploid species and outputs simulated progeny to several standard mapping programs. AVAILABILITY: Documentation, C++ source, and binaries for Mac OS X and x86 Linux are freely available at http://www.nervenet.org/genome_mixer/. GenomeMixer can be compiled on any system with support for the Trolltech Qt toolkit, including Windows.     

indel-Seq-Gen: a new protein family simulator incorporating domains, motifs, and indels

Reconstructing the evolutionary history of protein sequences will provide a better understanding of divergence mechanisms of protein superfamilies and their functions. Long-term protein evolution often includes dynamic changes such as insertion, deletion, and domain shuffling. Such dynamic changes make reconstructing protein sequence evolution difficult and affect the accuracy of molecular evolutionary methods, such as multiple alignments and phylogenetic methods. Unfortunately, currently available simulation methods are not sufficiently flexible and do not allow biologically realistic dynamic protein sequence evolution. We introduce a new method, indel-Seq-Gen (iSG), that can simulate realistic evolutionary processes of protein sequences with insertions and deletions (indels). Unlike other simulation methods, iSG allows the user to simulate multiple subsequences according to different evolutionary parameters, which is necessary for generating realistic protein families with multiple domains. iSG tracks all evolutionary events including indels and outputs the "true" multiple alignment of the simulated sequences. iSG can also generate a larger sequence space by allowing the use of multiple related root sequences. With all these functions, iSG can be used to test the accuracy of, for example, multiple alignment methods, phylogenetic methods, evolutionary hypotheses, ancestral protein reconstruction methods, and protein family classification methods. We empirically evaluated the performance of iSG against currently available methods by simulating the evolution of the G protein-coupled receptor and lipocalin protein families. We examined their true multiple alignments, reconstruction of the transmembrane regions and beta-strands, and the results of similarity search against a protein database using the simulated sequences. We also presented an example of using iSG for examining how phylogenetic reconstruction is affected by high indel rates.     

Reverse computation for rollback-based fault tolerance in large parallel systems

Reverse computation is presented here as an important future direction in addressing the challenge of fault tolerant execution on very large cluster platforms for parallel computing. As the scale of parallel jobs increases, traditional checkpointing approaches suffer scalability problems ranging from computational slowdowns to high congestion at the persistent stores for checkpoints. Reverse computation can overcome such problems and is also better suited for parallel computing on newer architectures with smaller, cheaper or energy-efficient memories and file systems. Initial evidence for the feasibility of reverse computation in large systems is presented with detailed performance data from a particle (ideal gas) simulation scaling to 65,536 processor cores and 950 accelerators (GPUs). Reverse computation is observed to deliver very large gains relative to checkpointing schemes when nodes rely on their host processors/memory to tolerate faults at their accelerators. A comparison between reverse computation and checkpointing with measurements such as cache miss ratios, TLB misses and memory usage indicates that reverse computation is hard to ignore as a future alternative to be pursued in emerging architectures.     

Synthetic spider silk: a modular fiber

Spiders make their webs and perform a wide range of tasks with up to seven different types of silk fiber. These different fibers allow a comparison of structure with function, because each silk has distinct mechanical properties and is composed of peptide modules that confer those properties. By using genetic engineering to mix the modules in specific proportions, proteins with defined strength and elasticity can be designed, which have many potential medical and engineering uses.     

Logos: a modular bayesian model for de novo motif detection

The complexity of the global organization and internal structure of motifs in higher eukaryotic organisms raises significant challenges for motif detection techniques. To achieve successful de novo motif detection, it is necessary to model the complex dependencies within and among motifs and to incorporate biological prior knowledge. In this paper, we present LOGOS, an integrated LOcal and GlObal motif Sequence model for biopolymer sequences, which provides a principled framework for developing, modularizing, extending and computing expressive motif models for complex biopolymer sequence analysis. LOGOS consists of two interacting submodels: HMDM, a local alignment model capturing biological prior knowledge and positional dependency within the motif local structure; and HMM, a global motif distribution model modeling frequencies and dependencies of motif occurrences. Model parameters can be fit using training motifs within an empirical Bayesian framework. A variational EM algorithm is developed for de novo motif detection. LOGOS improves over existing models that ignore biological priors and dependencies in motif structures and motif occurrences, and demonstrates superior performance on both semi-realistic test data and cis-regulatory sequences from yeast and Drosophila genomes with regard to sensitivity, specificity, flexibility and extensibility.