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.

MOTIVATION: To create, verify and maintain pathway models, curators must discover and assess knowledge distributed over the vast body of biological literature. Methods supporting these tasks must understand both the pathway model representations and the natural language in the literature. These methods should identify and order documents by relevance to any given pathway reaction. No existing system has addressed all aspects of this challenge. METHOD: We present novel methods for associating pathway model reactions with relevant publications. Our approach extracts the reactions directly from the models and then turns them into queries for three text mining-based MEDLINE literature search systems. These queries are executed, and the resulting documents are combined and ranked according to their relevance to the reactions of interest. We manually annotate document-reaction pairs with the relevance of the document to the reaction and use this annotation to study several ranking methods, using various heuristic and machine-learning approaches. RESULTS: Our evaluation shows that the annotated document-reaction pairs can be used to create a rule-based document ranking system, and that machine learning can be used to rank documents by their relevance to pathway reactions. We find that a Support Vector Machine-based system outperforms several baselines and matches the performance of the rule-based system. The success of the query extraction and ranking methods are used to update our existing pathway search system, PathText. AVAILABILITY: An online demonstration of PathText 2 and the annotated corpus are available for research purposes at http://www.nactem.ac.uk/pathtext2/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics (2011) 27(8), 1108–1112. The above paper was incorrectly published without an Open Access copyright line. The publishers would like to apologize.

BACKGROUND: Research into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the textual context of events that can determine, amongst other things, whether an event represents a fact, hypothesis, experimental result or analysis of results, whether it describes new or previously reported knowledge, and whether it is speculated or negated. We refer to such contextual information as meta-knowledge. The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them. RESULTS: Based on a corpus of 1,000 MEDLINE abstracts, fully manually annotated with both events and associated meta-knowledge, we have constructed a machine learning-based system that automatically assigns meta-knowledge information to events. This system has been integrated into EventMine, a state-of-the-art event extraction system, in order to create a more advanced system (EventMine-MK) that not only extracts events from text automatically, but also assigns five different types of meta-knowledge to these events. The meta-knowledge assignment module of EventMine-MK performs with macro-averaged F-scores in the range of 57-87% on the BioNLP'09 Shared Task corpus. EventMine-MK has been evaluated on the BioNLP'09 Shared Task subtask of detecting negated and speculated events. Our results show that EventMine-MK can outperform other state-of-the-art systems that participated in this task. CONCLUSIONS: We have constructed the first practical system that extracts both events and associated, detailed meta-knowledge information from biomedical literature. The automatically assigned meta-knowledge information can be used to refine search systems, in order to provide an extra search layer beyond entities and assertions, dealing with phenomena such as rhetorical intent, speculations, contradictions and negations. This finer grained search functionality can assist in several important tasks, e.g., database curation (by locating new experimental knowledge) and pathway enrichment (by providing information for inference). To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA component that can be used in the interoperable text mining infrastructure, U-Compare.

MOTIVATION: The study of metabolites (metabolomics) is increasingly being applied to investigate microbial, plant, environmental and mammalian systems. One of the limiting factors is that of chemically identifying metabolites from mass spectrometric signals present in complex datasets. RESULTS: Three workflows have been developed to allow for the rapid, automated and high-throughput annotation and putative metabolite identification of electrospray LC-MS-derived metabolomic datasets. The collection of workflows are defined as PUTMEDID_LCMS and perform feature annotation, matching of accurate m/z to the accurate mass of neutral molecules and associated molecular formula and matching of the molecular formulae to a reference file of metabolites. The software is independent of the instrument and data pre-processing applied. The number of false positives is reduced by eliminating the inaccurate matching of many artifact, isotope, multiply charged and complex adduct peaks through complex interrogation of experimental data. AVAILABILITY: The workflows, standard operating procedure and further information are publicly available at http://www.mcisb.org/resources/putmedid.html. CONTACT: [email protected].

MOTIVATION: In recent years, the gulf between the mass of accumulating-research data and the massive literature describing and analyzing those data has widened. The need for intelligent tools to bridge this gap, to rescue the knowledge being systematically isolated in literature and data silos, is now widely acknowledged. RESULTS: To this end, we have developed Utopia Documents, a novel PDF reader that semantically integrates visualization and data-analysis tools with published research articles. In a successful pilot with editors of the Biochemical Journal (BJ), the system has been used to transform static document features into objects that can be linked, annotated, visualized and analyzed interactively (http://www.biochemj.org/bj/424/3/). Utopia Documents is now used routinely by BJ editors to mark up article content prior to publication. Recent additions include integration of various text-mining and biodatabase plugins, demonstrating the system's ability to seamlessly integrate on-line content with PDF articles. AVAILABILITY: http://getutopia.com.

MOTIVATION: Directed evolution, in addition to its principal application of obtaining novel biomolecules, offers significant potential as a vehicle for obtaining useful information about the topologies of biomolecular fitness landscapes. In this article, we make use of a special type of model of fitness landscapes-based on finite state machines-which can be inferred from directed evolution experiments. Importantly, the model is constructed only from the fitness data and phylogeny, not sequence or structural information, which is often absent. The model, called a landscape state machine (LSM), has already been used successfully in the evolutionary computation literature to model the landscapes of artificial optimization problems. Here, we use the method for the first time to simulate a biological fitness landscape based on experimental evaluation. RESULTS: We demonstrate in this study that LSMs are capable not only of representing the structure of model fitness landscapes such as NK-landscapes, but also the fitness landscape of real DNA oligomers binding to a protein (allophycocyanin), data we derived from experimental evaluations on microarrays. The LSMs prove adept at modelling the progress of evolution as a function of various controlling parameters, as validated by evaluations on the real landscapes. Specifically, the ability of the model to 'predict' optimal mutation rates and other parameters of the evolution is demonstrated. A modification to the standard LSM also proves accurate at predicting the effects of recombination on the evolution.

Abstract Summary: Arcadia translates text-based descriptions of biological networks (SBML files) into standardized diagrams (SBGN PD maps). Users can view the same model from different perspectives and easily alter the layout to emulate traditional textbook representations. Availability and Implementation: Arcadia is written in C++. The source code is available (along with Mac OS and Windows binaries) under the GPL from http://arcadiapathways.sourceforge.net/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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.

MOTIVATION: Most experimental evidence on kinetic parameters is buried in the literature, whose manual searching is complex, time consuming and partial. These shortcomings become particularly acute in systems biology, where these parameters need to be integrated into detailed, genome-scale, metabolic models. These problems are addressed by KiPar, a dedicated information retrieval system designed to facilitate access to the literature relevant for kinetic modelling of a given metabolic pathway in yeast. Searching for kinetic data in the context of an individual pathway offers modularity as a way of tackling the complexity of developing a full metabolic model. It is also suitable for large-scale mining, since multiple reactions and their kinetic parameters can be specified in a single search request, rather than one reaction at a time, which is unsuitable given the size of genome-scale models. RESULTS: We developed an integrative approach, combining public data and software resources for the rapid development of large-scale text mining tools targeting complex biological information. The user supplies input in the form of identifiers used in relevant data resources to refer to the concepts of interest, e.g. EC numbers, GO and SBO identifiers. By doing so, the user is freed from providing any other knowledge or terminology concerned with these concepts and their relations, since they are retrieved from these and cross-referenced resources automatically. The terminology acquired is used to index the literature by mapping concepts to their synonyms, and then to textual documents mentioning them. The indexing results and the previously acquired knowledge about relations between concepts are used to formulate complex search queries aiming at documents relevant to the user's information needs. The conceptual approach is demonstrated in the implementation of KiPar. Evaluation reveals that KiPar performs better than a Boolean search. The precision achieved for abstracts (60%) and full-text articles (48%) is considerably better than the baseline precision (44% and 24%, respectively). The baseline recall is improved by 36% for abstracts and by 100% for full text. It appears that full-text articles are a much richer source of information on kinetic data than are their abstracts. Finally, the combined results for abstracts and full text compared with the curated literature provide high values for relative recall (88%) and novelty ratio (92%), suggesting that the system is able to retrieve a high proportion of new documents. AVAILABILITY: Source code and documentation are available at: (http://www.mcisb.org/resources/kipar/).

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/

MOTIVATION: In the biological sciences, the need to analyse vast amounts of information has become commonplace. Such large-scale analyses often involve drawing together data from a variety of different databases, held remotely on the internet or locally on in-house servers. Supporting these tasks are ad hoc collections of data-manipulation tools, scripting languages and visualisation software, which are often combined in arcane ways to create cumbersome systems that have been customized for a particular purpose, and are consequently not readily adaptable to other uses. For many day-to-day bioinformatics tasks, the sizes of current databases, and the scale of the analyses necessary, now demand increasing levels of automation; nevertheless, the unique experience and intuition of human researchers is still required to interpret the end results in any meaningful biological way. Putting humans in the loop requires tools to support real-time interaction with these vast and complex data-sets. Numerous tools do exist for this purpose, but many do not have optimal interfaces, most are effectively isolated from other tools and databases owing to incompatible data formats, and many have limited real-time performance when applied to realistically large data-sets: much of the user's cognitive capacity is therefore focused on controlling the software and manipulating esoteric file formats rather than on performing the research. METHODS: To confront these issues, harnessing expertise in human-computer interaction (HCI), high-performance rendering and distributed systems, and guided by bioinformaticians and end-user biologists, we are building reusable software components that, together, create a toolkit that is both architecturally sound from a computing point of view, and addresses both user and developer requirements. Key to the system's usability is its direct exploitation of semantics, which, crucially, gives individual components knowledge of their own functionality and allows them to interoperate seamlessly, removing many of the existing barriers and bottlenecks from standard bioinformatics tasks. RESULTS: The toolkit, named Utopia, is freely available from http://utopia.cs.man.ac.uk/.

The main aim of landscape analysis has been to quantify the 'hardness' of problems. Early steps have been made towards extending this into Genetic Programming. However, few attempts have been made to extend the use of landscape analysis into the prediction of ways to make a problem easy, through the optimal setting of control parameters. This paper introduces a new class of landscape metrics, which we call 'Genotype-Fitness Correlations'. An example of this family of metrics is applied to six real-world regression problems. It is demonstrated that genotype-fitness correlations may be used to estimate optimum population sizes for the six problems. We believe that this application of a landscape metric as guidance in the setting of control parameters is an important step towards the development of an adaptive algorithm that can respond to the perceived landscape in 'real-time', i.e. during the evolutionary search process itself.

MOTIVATION: Genetic modifications or pharmaceutical interventions can influence multiple sites in metabolic pathways, and often these are 'distant' from the primary effect. In this regard, the ability to identify target and off-target effects of a specific compound or gene therapy is both a major challenge and critical in drug discovery. RESULTS: We applied Markov Chain Monte Carlo (MCMC) for parameter estimation and perturbation identification in the kinetic modeling of metabolic pathways. Variability in the steady-state measurements in cells taken from a population can be caused by differences in initial conditions within the population, by variation of parameters among individuals and by possible measurement noise. MCMC-based parameter estimation is proposed as a method to help in inferring parameter distributions, taking into account uncertainties in the initial conditions and in the measurement data. The inferred parameter distributions are then used to predict changes in the network via a simple classification method. The proposed technique is applied to analyze changes in the pathways of pyruvate metabolism of mutants of Lactococcus lactis, based on previously published experimental data. AVAILABILITY: MATLAB code used in the simulations is available from ftp://[email protected]/pub/Bioinformatics_BJ.zip

UNLABELLED: Many data manipulation processes involve the use of programming libraries. These processes may beneficially be automated due to their repeated use. A convenient type of automation is in the form of workflows that also allow such processes to be shared amongst the community. The Taverna workflow system has been extended to enable it to use and invoke Java classes and methods as tasks within Taverna workflows. These classes and methods are selected for use during workflow construction by a Java Doclet application called the API Consumer. This selection is stored as an XML file which enables Taverna to present the subset of the API for use in the composition of workflows. The ability of Taverna to invoke Java classes and methods is demonstrated by a workflow in which we use libSBML to map gene expression data onto a metabolic pathway represented as a SBML model. AVAILABILITY: Taverna and the API Consumer application can be freely downloaded from http://taverna.sourceforge.net

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.

BACKGROUND: Many bioinformatics applications rely on controlled vocabularies or ontologies to consistently interpret and seamlessly integrate information scattered across public resources. Experimental data sets from metabolomics studies need to be integrated with one another, but also with data produced by other types of omics studies in the spirit of systems biology, hence the pressing need for vocabularies and ontologies in metabolomics. However, it is time-consuming and non trivial to construct these resources manually. RESULTS: We describe a methodology for rapid development of controlled vocabularies, a study originally motivated by the needs for vocabularies describing metabolomics technologies. We present case studies involving two controlled vocabularies (for nuclear magnetic resonance spectroscopy and gas chromatography) whose development is currently underway as part of the Metabolomics Standards Initiative. The initial vocabularies were compiled manually, providing a total of 243 and 152 terms. A total of 5,699 and 2,612 new terms were acquired automatically from the literature. The analysis of the results showed that full-text articles (especially the Materials and Methods sections) are the major source of technology-specific terms as opposed to paper abstracts. CONCLUSIONS: We suggest a text mining method for efficient corpus-based term acquisition as a way of rapidly expanding a set of controlled vocabularies with the terms used in the scientific literature. We adopted an integrative approach, combining relatively generic software and data resources for time- and cost-effective development of a text mining tool for expansion of controlled vocabularies across various domains, as a practical alternative to both manual term collection and tailor-made named entity recognition methods.

Many scientists now manage the bulk of their bibliographic information electronically, thereby organizing their publications and citation material from digital libraries. However, a library has been described as "thought in cold storage," and unfortunately many digital libraries can be cold, impersonal, isolated, and inaccessible places. In this Review, we discuss the current chilly state of digital libraries for the computational biologist, including PubMed, IEEE Xplore, the ACM digital library, ISI Web of Knowledge, Scopus, Citeseer, arXiv, DBLP, and Google Scholar. We illustrate the current process of using these libraries with a typical workflow, and highlight problems with managing data and metadata using URIs. We then examine a range of new applications such as Zotero, Mendeley, Mekentosj Papers, MyNCBI, CiteULike, Connotea, and HubMed that exploit the Web to make these digital libraries more personal, sociable, integrated, and accessible places. We conclude with how these applications may begin to help achieve a digital defrost, and discuss some of the issues that will help or hinder this in terms of making libraries on the Web warmer places in the future, becoming resources that are considerably more useful to both humans and machines.

The accurate quantification of proteins is important in several areas of cell biology, biotechnology and medicine. Both relative and absolute quantification of proteins is often determined following mass spectrometric analysis of one or more of their constituent peptides. However, in order for quantification to be successful, it is important that the experimenter knows which peptides are readily detectable under the mass spectrometric conditions used for analysis. In this paper, genetic programming is used to develop a function which predicts the detectability of peptides from their calculated physico-chemical properties. Classification is carried out in two stages: the selection of a good classifier using the AUROC objective function and the setting of an appropriate threshold. This allows the user to select the balance point between conflicting priorities in an intuitive way. The success of this method is found to be highly dependent on the initial selection of input parameters. The use of brood recombination and a modified version of the multi-objective FOCUS method are also investigated. While neither has a significant effect on predictive accuracy, the use of the FOCUS method leads to considerably more compact solutions.

This paper reviews the application of multiobjective optimization in the fields of bioinformatics and computational biology. A survey of existing work, organized by application area, forms the main body of the review, following an introduction to the key concepts in multiobjective optimization. An original contribution of the review is the identification of five distinct "contexts," giving rise to multiple objectives: These are used to explain the reasons behind the use of multiobjective optimization in each application area and also to point the way to potential future uses of the technique.

BACKGROUND: The genome sequencing projects have shown our limited knowledge regarding gene function, e.g. S. cerevisiae has 5-6,000 genes of which nearly 1,000 have an uncertain function. Their gross influence on the behaviour of the cell can be observed using large-scale metabolomic studies. The metabolomic data produced need to be structured and annotated in a machine-usable form to facilitate the exploration of the hidden links between the genes and their functions. DESCRIPTION: MeMo is a formal model for representing metabolomic data and the associated metadata. Two predominant platforms (SQL and XML) are used to encode the model. MeMo has been implemented as a relational database using a hybrid approach combining the advantages of the two technologies. It represents a practical solution for handling the sheer volume and complexity of the metabolomic data effectively and efficiently. The MeMo model and the associated software are available at http://dbkgroup.org/memo/. CONCLUSION: The maturity of relational database technology is used to support efficient data processing. The scalability and self-descriptiveness of XML are used to simplify the relational schema and facilitate the extensibility of the model necessitated by the creation of new experimental techniques. Special consideration is given to data integration issues as part of the systems biology agenda. MeMo has been physically integrated and cross-linked to related metabolomic and genomic databases. Semantic integration with other relevant databases has been supported through ontological annotation. Compatibility with other data formats is supported by automatic conversion.

MOTIVATION: The discovery of novel biological knowledge from the ab initio analysis of post-genomic data relies upon the use of unsupervised processing methods, in particular clustering techniques. Much recent research in bioinformatics has therefore been focused on the transfer of clustering methods introduced in other scientific fields and on the development of novel algorithms specifically designed to tackle the challenges posed by post-genomic data. The partitions returned by a clustering algorithm are commonly validated using visual inspection and concordance with prior biological knowledge--whether the clusters actually correspond to the real structure in the data is somewhat less frequently considered. Suitable computational cluster validation techniques are available in the general data-mining literature, but have been given only a fraction of the same attention in bioinformatics. RESULTS: This review paper aims to familiarize the reader with the battery of techniques available for the validation of clustering results, with a particular focus on their application to post-genomic data analysis. Synthetic and real biological datasets are used to demonstrate the benefits, and also some of the perils, of analytical clustervalidation. AVAILABILITY: The software used in the experiments is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/. SUPPLEMENTARY INFORMATION: Enlarged colour plots are provided in the Supplementary Material, which is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/.

BACKGROUND: maxdLoad2 is a relational database schema and Java application for microarray experimental annotation and storage. It is compliant with all standards for microarray meta-data capture; including the specification of what data should be recorded, extensive use of standard ontologies and support for data exchange formats. The output from maxdLoad2 is of a form acceptable for submission to the ArrayExpress microarray repository at the European Bioinformatics Institute. maxdBrowse is a PHP web-application that makes contents of maxdLoad2 databases accessible via web-browser, the command-line and web-service environments. It thus acts as both a dissemination and data-mining tool. RESULTS: maxdLoad2 presents an easy-to-use interface to an underlying relational database and provides a full complement of facilities for browsing, searching and editing. There is a tree-based visualization of data connectivity and the ability to explore the links between any pair of data elements, irrespective of how many intermediate links lie between them. Its principle novel features are: the flexibility of the meta-data that can be captured, the tools provided for importing data from spreadsheets and other tabular representations, the tools provided for the automatic creation of structured documents, the ability to browse and access the data via web and web-services interfaces. Within maxdLoad2 it is very straightforward to customise the meta-data that is being captured or change the definitions of the meta-data. These meta-data definitions are stored within the database itself allowing client software to connect properly to a modified database without having to be specially configured. The meta-data definitions (configuration file) can also be centralized allowing changes made in response to revisions of standards or terminologies to be propagated to clients without user intervention.maxdBrowse is hosted on a web-server and presents multiple interfaces to the contents of maxd databases. maxdBrowse emulates many of the browse and search features available in the maxdLoad2 application via a web-browser. This allows users who are not familiar with maxdLoad2 to browse and export microarray data from the database for their own analysis. The same browse and search features are also available via command-line and SOAP server interfaces. This both enables scripting of data export for use embedded in data repositories and analysis environments, and allows access to the maxd databases via web-service architectures. CONCLUSION: maxdLoad2 http://www.bioinf.man.ac.uk/microarray/maxd/ and maxdBrowse http://dbk.ch.umist.ac.uk/maxdBrowse are portable and compatible with all common operating systems and major database servers. They provide a powerful, flexible package for annotation of microarray experiments and a convenient dissemination environment. They are available for download and open sourced under the Artistic License.

We present a method in which a genetic algorithm is used to optimise an expression in order to provide a supervised method for interpretation of the infrared analytical spectra of complex biological samples. The aim is to produce a model that can predict the value of a measurand of interest, such as the concentration of a particular chemical constituent, from a complex infrared spectrum of biological material. The method we describe is in some ways analogous to genetic programming but it more readily allows the output expression to be constrained in complexity and permits its general form to be specified by the user, thereby enhancing its explanatory ability. The quasi-continuous properties of optical spectra are exploited by mutations that explore spectral regions adjacent to selected variables, and provide adaptive averaging of spectral regions so as to provide selective optimisation of the tradeoff between spectral resolution and signal-to-noise ratio.

Abstract Summary: We describe a program for the construction of spatially distributed metabolic models, which may then be simulated using the metabolic simulator Gepasi. This is useful for the modelling of heterogeneous systems whether as liquid cultures or as spatially organised systems with specified interconnections. Availability: MEG runs under Windows 95, 98, 2000 and NT, with either Intel or Alpha processors, and is available from http://gepasi.dbs.aber.ac.uk/softw/meg.html Contact: [email protected] * To whom correspondence should be addressed. Present address: Virginia Bioinformatics Institute, Virginia Polytechnic and State University, 1750 Kraft Drive, Suite 1100, Blacksburg, VA 24061, USA.

MOTIVATION: The simulation of biochemical kinetic systems is a powerful approach that can be used for: (i) checking the consistency of a postulated model with a set of experimental measurements, (ii) answering 'what if?' questions and (iii) exploring possible behaviours of a model. Here we describe a generic approach to combine numerical optimization methods with biochemical kinetic simulations, which is suitable for use in the rational design of improved metabolic pathways with industrial significance (metabolic engineering) and for solving the inverse problem of metabolic pathways, i.e. the estimation of parameters from measured variables. RESULTS: We discuss the suitability of various optimization methods, focusing especially on their ability or otherwise to find global optima. We recommend that a suite of diverse optimization methods should be available in simulation software as no single one performs best for all problems. We describe how we have implemented such a simulation-optimization strategy in the biochemical kinetics simulator Gepasi and present examples of its application. AVAILABILITY: The new version of Gepasi (3.20), incorporating the methodology described here, is available on the Internet at http://gepasi.dbs.aber.ac.uk/softw/Gepasi. html. CONTACT: [email protected]