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An extension of integral equation time-domain analysis to treat electromagnetic scattering from imperfect conductors embedded in a dielectric is presented. It employs isoparametric curvilinear representations, and an implicit timestepping approach is used to achieve stability. The principal difficulties arise from the diffusive 'wake' term which the finite conductivity introduces into the Green function. The consequent computational complexity and costs, both significantly greater than for perfectly conducting scatterers, are discussed. Examples of the application of the methods are presented. Index Terms--Conducting bodies, electromagnetic scattering, integral equation methods, time-domain analysis.

People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.3 of SBOL Visual, which builds on the prior SBOL Visual 2.2 in several ways. First, the specification now includes higher-level “interactions with interactions,” such as an inducer molecule stimulating a repression interaction. Second, binding with a nucleic acid backbone can be shown by overlapping glyphs, as with other molecular complexes. Finally, a new “unspecified interaction” glyph is added for visualizing interactions whose nature is unknown, the “insulator” glyph is deprecated in favor of a new “inert DNA spacer” glyph, and the polypeptide region glyph is recommended for showing 2A sequences.

People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.2 of SBOL Visual, which builds on the prior SBOL Visual 2.1 in several ways. First, the grounding of molecular species glyphs is changed from BioPAX to SBO, aligning with the use of SBO terms for interaction glyphs. Second, new glyphs are added for proteins, introns, and polypeptide regions (e. g., protein domains), the prior recommended macromolecule glyph is deprecated in favor of its alternative, and small polygons are introduced as alternative glyphs for simple chemicals.

People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species . Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.1 of SBOL Visual, which builds on the prior SBOL Visual 2.0 standard by expanding diagram syntax to include methods for showing modular structure and mappings between elements of a system, interactions arrows that can split or join (with the glyph at the split or join indicating either superposition or a chemical process), and adding new glyphs for indicating genomic context (e.g., integration into a plasmid or genome) and for stop codons.

Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems is to improve the exchange of information about designed systems between laboratories. The synthetic biology open language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.3.0 of SBOL, which builds upon version 2.2.0 published in last year’s JIB Standards in Systems Biology special issue. In particular, SBOL 2.3.0 includes means of succinctly representing sequence modifications, such as insertion, deletion, and replacement, an extension to support organization and attachment of experimental data derived from designs, and an extension for describing numerical parameters of design elements. The new version also includes specifying types of synthetic biology activities, unambiguous locations for sequences with multiple encodings, refinement of a number of validation rules, improved figures and examples, and clarification on a number of issues related to the use of external ontology terms.

Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems would be to improve the exchange of information about designed systems between laboratories. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.1 of SBOL that builds upon version 2.0 published in last year’s JIB special issue. In particular, SBOL 2.1 includes improved rules for what constitutes a valid SBOL document, new role fields to simplify the expression of sequence features and how components are used in context, and new best practices descriptions to improve the exchange of basic sequence topology information and the description of genetic design provenance, as well as miscellaneous other minor improvements.

Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems would be to improve the exchange of information about designed systems between laboratories. The synthetic biology open language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.2.0 of SBOL that builds upon version 2.1.0 published in last year’s JIB special issue. In particular, SBOL 2.2.0 includes improved description and validation rules for genetic design provenance, an extension to support combinatorial genetic designs, a new class to add non-SBOL data as attachments, a new class for genetic design implementations, and a description of a methodology to describe the entire design-build-test-learn cycle within the SBOL data model.

People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.0 of SBOL Visual, which builds on the prior SBOL Visual 1.0 standard by expanding diagram syntax to include functional interactions and molecular species, making the relationship between diagrams and the SBOL data model explicit, supporting families of symbol variants, clarifying a number of requirements and best practices, and significantly expanding the collection of diagram glyphs.

Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems would be to improve the exchange of information about designed systems between laboratories. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.0 of SBOL, introducing a standardized format for the electronic exchange of information on the structural and functional aspects of biological designs. The standard has been designed to support the explicit and unambiguous description of biological designs by means of a well defined data model. The standard also includes rules and best practices on how to use this data model and populate it with relevant design details. The publication of this specification is intended to make these capabilities more widely accessible to potential developers and users in the synthetic biology community and beyond.

BacillOndex is an extension of the Ondex data integration system, providing a semantically annotated, integrated knowledge base for the model Gram-positive bacterium Bacillus subtilis. This application allows a user to mine a variety of B. subtilis data sources, and analyse the resulting integrated dataset, which contains data about genes, gene products and their interactions. The data can be analysed either manually, by browsing using Ondex, or computationally via a Web services interface. We describe the process of creating a BacillOndex instance, and describe the use of the system for the analysis of single nucleotide polymorphisms in B. subtilis Marburg. The Marburg strain is the progenitor of the widely-used laboratory strain B. subtilis 168. We identified 27 SNPs with predictable phenotypic effects, including genetic traits for known phenotypes. We conclude that BacillOndex is a valuable tool for the systems-level investigation of, and hypothesis generation about, this important biotechnology workhorse. Such understanding contributes to our ability to construct synthetic genetic circuits in this organism.

As bioinformatics datasets grow ever larger, and analyses become increasingly complex, there is a need for data handling infrastructures to keep pace with developing technology. One solution is to apply Grid and Cloud technologies to address the computational requirements of analysing high throughput datasets. We present an approach for writing new, or wrapping existing applications, and a reference implementation of a framework, Microbase2.0, for executing those applications using Grid and Cloud technologies. We used Microbase2.0 to develop an automated Cloud-based bioinformatics workflow executing simultaneously on two different Amazon EC2 data centres and the Newcastle University Condor Grid. Several CPU years’ worth of computational work was performed by this system in less than two months. The workflow produced a detailed dataset characterising the cellular localisation of 3,021,490 proteins from 867 taxa, including bacteria, archaea and unicellular eukaryotes. Microbase2.0 is freely available from http://www.microbase.org.uk/.

Drug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.

The creation of quantitative, simulatable, Systems Biology Markup Language (SBML) models that accurately simulate the system under study is a time-intensive manual process that requires careful checking. Currently, the rules and constraints of model creation, curation, and annotation are distributed over at least three separate documents: the SBML schema document (XSD), the Systems Biology Ontology (SBO), and the “Structures and Facilities for Model Definition” document. The latter document contains the richest set of constraints on models, and yet it is not amenable to computational processing. We have developed a Web Ontology Language (OWL) knowledge base that integrates these three structure documents, and that contains a representative sample of the information contained within them. This Model Format OWL (MFO) performs both structural and constraint integration and can be reasoned over and validated. SBML Models are represented as individuals of OWL classes, resulting in a single computationally amenable resource for model checking. Knowledge that was only accessible to humans is now explicitly and directly available for computational approaches. The integration of all structural knowledge for SBML models into a single resource creates a new style of model development and checking.

Abstract Background The creation of accurate quantitative Systems Biology Markup Language (SBML) models is a time-intensive, manual process often complicated by the many data sources and formats required to annotate even a small and well-scoped model. Ideally, the retrieval and integration of biological knowledge for model annotation should be performed quickly, precisely, and with a minimum of manual effort. Results Here we present rule-based mediation, a method of semantic data integration applied to systems biology model annotation. The heterogeneous data sources are first syntactically converted into ontologies, which are then aligned to a small domain ontology by applying a rule base. We demonstrate proof-of-principle of this application of rule-based mediation using off-the-shelf semantic web technology through two use cases for SBML model annotation. Existing tools and technology provide a framework around which the system is built, reducing development time and increasing usability. Conclusions Integrating resources in this way accommodates multiple formats with different semantics, and provides richly-modelled biological knowledge suitable for annotation of SBML models. This initial work establishes the feasibility of rule-based mediation as part of an automated SBML model annotation system. Availability Detailed information on the project files as well as further information on and comparisons with similar projects is available from the project page at http://cisban-silico.cs.ncl.ac.uk/RBM/.

Abstract Web services have become a key technology for bioinformatics, since life science databases are globally decentralized and the exponential increase in the amount of available data demands for efficient systems without the need to transfer entire databases for every step of an analysis. However, various incompatibilities among database resources and analysis services make it difficult to connect and integrate these into interoperable workflows. To resolve this situation, we invited domain specialists from web service providers, client software developers, Open Bio* projects, the BioMoby project and researchers of emerging areas where a standard exchange data format is not well established, for an intensive collaboration entitled the BioHackathon 2008. The meeting was hosted by the Database Center for Life Science (DBCLS) and Computational Biology Research Center (CBRC) and was held in Tokyo from February 11th to 15th, 2008. In this report we highlight the work accomplished and the common issues arisen from this event, including the standardization of data exchange formats and services in the emerging fields of glycoinformatics, biological interaction networks, text mining, and phyloinformatics. In addition, common shared object development based on BioSQL, as well as technical challenges in large data management, asynchronous services, and security are discussed. Consequently, we improved interoperability of web services in several fields, however, further cooperation among major database centers and continued collaborative efforts between service providers and software developers are still necessary for an effective advance in bioinformatics web service technologies.

Abstract Background There has been a dramatic increase in the amount of quantitative data derived from the measurement of changes at different levels of biological complexity during the post-genomic era. However, there are a number of issues associated with the use of computational tools employed for the analysis of such data. For example, computational tools such as R and MATLAB require prior knowledge of their programming languages in order to implement statistical analyses on data. Combining two or more tools in an analysis may also be problematic since data may have to be manually copied and pasted between separate user interfaces for each tool. Furthermore, this transfer of data may require a reconciliation step in order for there to be interoperability between computational tools. Results Developments in the Taverna workflow system have enabled pipelines to be constructed and enacted for generic and ad hoc analyses of quantitative data. Here, we present an example of such a workflow involving the statistical identification of differentially-expressed genes from microarray data followed by the annotation of their relationships to cellular processes. This workflow makes use of customised maxdBrowse web services, a system that allows Taverna to query and retrieve gene expression data from the maxdLoad2 microarray database. These data are then analysed by R to identify differentially-expressed genes using the Taverna RShell processor which has been developed for invoking this tool when it has been deployed as a service using the RServe library. In addition, the workflow uses Beanshell scripts to reconcile mismatches of data between services as well as to implement a form of user interaction for selecting subsets of microarray data for analysis as part of the workflow execution. A new plugin system in the Taverna software architecture is demonstrated by the use of renderers for displaying PDF files and CSV formatted data within the Taverna workbench. Conclusion Taverna can be used by data analysis experts as a generic tool for composing ad hoc analyses of quantitative data by combining the use of scripts written in the R programming language with tools exposed as services in workflows. When these workflows are shared with colleagues and the wider scientific community, they provide an approach for other scientists wanting to use tools such as R without having to learn the corresponding programming language to analyse their own data.