Summary: Synthetic biology applies the principles of engineering to biology in order to create biological functionalities not seen before in nature. One of the most exciting applications of synthetic biology is the design of new organisms with the ability to produce valuable chemicals including pharmaceuticals and biomaterials in a greener; sustainable fashion. Selecting the right enzymes to catalyze each reaction step in order to produce a desired target compound is, however, not trivial. Here, we present Selenzyme, a free online enzyme selection tool for metabolic pathway design. The user is guided through several decision steps in order to shortlist the best candidates for a given pathway step. The tool graphically presents key information about enzymes based on existing databases and tools such as: similarity of sequences and of catalyzed reactions; phylogenetic distance between source organism and intended host species; multiple alignment highlighting conserved regions, predicted catalytic site, and active regions and relevant properties such as predicted solubility and transmembrane regions. Selenzyme provides bespoke sequence selection for automated workflows in biofoundries. Availability and implementation: The tool is integrated as part of the pathway design stage into the design-build-test-learn SYNBIOCHEM pipeline. The Selenzyme web server is available at http://selenzyme.synbiochem.co.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Synthetic biology is typified by developing novel genetic constructs from the assembly of reusable synthetic DNA parts, which contain one or more features such as promoters, ribosome binding sites, coding sequences and terminators. PartsGenie is introduced to facilitate the computational design of such synthetic biology parts, bridging the gap between optimization tools for the design of novel parts, the representation of such parts in community-developed data standards such as Synthetic Biology Open Language, and their sharing in journal-recommended data repositories. Consisting of a drag-and-drop web interface, a number of DNA optimization algorithms, and an interface to the well-used data repository JBEI ICE, PartsGenie facilitates the design, optimization and dissemination of reusable synthetic biology parts through an integrated application. Availability and implementation: PartsGenie is freely available at https://parts.synbiochem.co.uk.

BACKGROUND: The behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources. RESULTS: Taverna workflows have been developed for the automated assembly of quantitative parameterised metabolic networks in the Systems Biology Markup Language (SBML). A SBML model is built in a systematic fashion by the workflows which starts with the construction of a qualitative network using data from a MIRIAM-compliant genome-scale model of yeast metabolism. This is followed by parameterisation of the SBML model with experimental data from two repositories, the SABIO-RK enzyme kinetics database and a database of quantitative experimental results. The models are then calibrated and simulated in workflows that call out to COPASIWS, the web service interface to the COPASI software application for analysing biochemical networks. These systems biology workflows were evaluated for their ability to construct a parameterised model of yeast glycolysis. CONCLUSIONS: Distributed information about metabolic reactions that have been described to MIRIAM standards enables the automated assembly of quantitative systems biology models of metabolic networks based on user-defined criteria. Such data integration processes can be implemented as Taverna workflows to provide a rapid overview of the components and their relationships within a biochemical system.

SUMMARY: The Systems Biology Markup Language (SBML) is an established community XML format for the markup of biochemical models. With the introduction of SBML level 2 version 3, specific model entities, such as species or reactions, can now be annotated using ontological terms. These annotations, which are encoded using the resource description framework (RDF), provide the facility to specify definite terms to individual components, allowing software to unambiguously identify such components and thus link the models to existing data resources. libSBML is an application programming interface library for the manipulation of SBML files. While libSBML provides the facilities for reading and writing such annotations from and to models, it is beyond the scope of libSBML to provide interpretation of these terms. The libAnnotationSBML library introduced here acts as a layer on top of libSBML linking SBML annotations to the web services that describe these ontological terms. Two applications that use this library are described: SbmlSynonymExtractor finds name synonyms of SBML model entities and SbmlReactionBalancer checks SBML files to determine whether specifed reactions are elementally balanced.

BACKGROUND: High content live cell imaging experiments are able to track the cellular localisation of labelled proteins in multiple live cells over a time course. Experiments using high content live cell imaging will generate multiple large datasets that are often stored in an ad-hoc manner. This hinders identification of previously gathered data that may be relevant to current analyses. Whilst solutions exist for managing image data, they are primarily concerned with storage and retrieval of the images themselves and not the data derived from the images. There is therefore a requirement for an information management solution that facilitates the indexing of experimental metadata and results of high content live cell imaging experiments. RESULTS: We have designed and implemented a data model and information management solution for the data gathered through high content live cell imaging experiments. Many of the experiments to be stored measure the translocation of fluorescently labelled proteins from cytoplasm to nucleus in individual cells. The functionality of this database has been enhanced by the addition of an algorithm that automatically annotates results of these experiments with the timings of translocations and periods of any oscillatory translocations as they are uploaded to the repository. Testing has shown the algorithm to perform well with a variety of previously unseen data. CONCLUSION: Our repository is a fully functional example of how high throughput imaging data may be effectively indexed and managed to address the requirements of end users. By implementing the automated analysis of experimental results, we have provided a clear impetus for individuals to ensure that their data forms part of that which is stored in the repository. Although focused on imaging, the solution provided is sufficiently generic to be applied to other functional proteomics and genomics experiments. The software is available from: fhttp://code.google.com/p/livecellim/

BACKGROUND: The proliferation of data repositories in bioinformatics has resulted in the development of numerous interfaces that allow scientists to browse, search and analyse the data that they contain. Interfaces typically support repository access by means of web pages, but other means are also used, such as desktop applications and command line tools. Interfaces often duplicate functionality amongst each other, and this implies that associated development activities are repeated in different laboratories. Interfaces developed by public laboratories are often created with limited developer resources. In such environments, reducing the time spent on creating user interfaces allows for a better deployment of resources for specialised tasks, such as data integration or analysis. Laboratories maintaining data resources are challenged to reconcile requirements for software that is reliable, functional and flexible with limitations on software development resources. RESULTS: This paper proposes a model-driven approach for the partial generation of user interfaces for searching and browsing bioinformatics data repositories. Inspired by the Model Driven Architecture (MDA) of the Object Management Group (OMG), we have developed a system that generates interfaces designed for use with bioinformatics resources. This approach helps laboratory domain experts decrease the amount of time they have to spend dealing with the repetitive aspects of user interface development. As a result, the amount of time they can spend on gathering requirements and helping develop specialised features increases. The resulting system is known as Pierre, and has been validated through its application to use cases in the life sciences, including the PEDRoDB proteomics database and the e-Fungi data warehouse. CONCLUSION: MDAs focus on generating software from models that describe aspects of service capabilities, and can be applied to support rapid development of repository interfaces in bioinformatics. The Pierre MDA is capable of supporting common database access requirements with a variety of auto-generated interfaces and across a variety of repositories. With Pierre, four kinds of interfaces are generated: web, stand-alone application, text-menu, and command line. The kinds of repositories with which Pierre interfaces have been used are relational, XML and object databases.