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Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution., Over the past two decades, scientists from different fields have been developing SBML, a standard format for encoding computational models in biology and medicine. This article summarizes recent progress and gives perspectives on emerging challenges.

Data Resource Profile: Accessible Resource for Integrated Epigenomic Studies (ARIES) Caroline L Relton, Tom Gaunt, Wendy McArdle, Karen Ho, Aparna Duggirala, Hashem Shihab, Geoff Woodward, Oliver Lyttleton, David M Evans, Wolf Reik, Yu-Lee Paul, Gabriella Ficz, Susan E Ozanne, Anil Wipat, Keith Flanagan, Allyson Lister, Bastiaan T Heijmans, Susan M Ring and George Davey Smith MRC Integrative Epidemiology Unit, and School of Social and Community Medicine, University of Bristol, Bristol, UK, Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK, University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, WA, Australia, Babraham Institute, Cambridge, UK, Wellcome Trust Sanger Institute, Cambridge, UK, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK, University of Cambridge Institute of Metabolic Sciences and MRC Metabolic Diseases Unit, Cambridge, UK, School of Computer Science, Newcastle University, Newcastle upon Tyne, UK and Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands

While the Web Ontology Language (OWL) provides a mechanism to import ontologies, this mechanism is not always suitable. First, given the current state of editing tools and the issues they have working with large ontologies, direct OWL imports have sometimes proven impractical for day-to-day development. Second, ontologies chosen for integration may be under active development and not aligned with the chosen design principles. Importing heterogeneous ontologies in their entirety may lead to inconsistencies or unintended inferences. In this paper we propose a set of guidelines for importing required terms from an external resource into a target ontology. We describe the guidelines, their implementation, present some examples of application, and outline future work and extensions.

The Functional Genomics Experiment data model (FuGE) has been developed to increase the consistency and efficiency of experimental data modeling in the life sciences, and it has been adopted by a number of high-profile standardization organizations. FuGE can be used: (1) directly, whereby generic modeling constructs are used to represent concepts from specific experimental activities; or (2) as a framework within which method-specific models can be developed. FuGE is both rich and flexible, providing a considerable number of modeling constructs, which can be used in a range of different ways. However, such richness and flexibility also mean that modelers and application developers have choices to make when applying FuGE in a given context. This paper captures emerging best practice in the use of FuGE in the light of the experience of several groups by: (1) proposing guidelines for the use and extension of the FuGE data model; (2) presenting design patterns that reflect recurring requirements in experimental data modeling; and (3) describing a community software tool kit (STK) that supports application development using FuGE. We anticipate that these guidelines will encourage consistent usage of FuGE, and as such, will contribute to the development of convergent data standards in omics research.

This meeting report summarizes the proceedings of the fifth Genomic Standards Consortium (GSC) workshop held December 12-14, 2007, at the European Bioinformatics Institute (EBI), Cambridge, UK. This fifth workshop served as a milestone event in the evolution of the GSC (launched in September 2005); the key outcome of the workshop was the finalization of a stable version of the MIGS specification (v2.0) for publication. This accomplishment enables, and also in some cases necessitates, downstream activities, which are described in the multiauthor, consensus-driven articles in this special issue of OMICS produced as a direct result of the workshop. This report briefly summarizes the workshop and overviews the special issue. In particular, it aims to explain how the various GSC-led projects are working together to help this community achieve its stated mission of further standardizing the descriptions of genomes and metagenomes and implementing improved mechanisms of data exchange and integration to enable more accurate comparative analyses. Further information about the GSC and its range of activities can be found at http://gensc.org.

This article summarizes the motivation for, and the proceedings of, the first ISA-TAB workshop held December 6–8, 2007, at the EBI, Cambridge, UK. This exploratory workshop, organized by members of the Microarray Gene Expression Data (MGED) Society's Reporting Structure for Biological Investigations (RSBI) working group, brought together a group of developers of a range of collaborative systems to discuss the use of a common format to address the pressing need of reporting and communicating data and metadata from biological, biomedical, and environmental studies employing combinations of genomics, transcriptomics, proteomics, and metabolomics technologies along with more conventional methodologies. The expertise of the participants comprised database development, data management, and hands-on experience in the development of data communication standards. The workshop's outcomes are set to help formalize the proposed Investigation, Study, Assay (ISA)-TAB tab-delimited format for representing and c...

The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments., Molecular Systems Biology, 7 (1), ISSN:1744-4292

The annotation of Systems Biology Markup Language (SBML) models with semantic terms has been supported for a number of years. The prevalence of such annotated models is growing, with repositories such as Biomodels.net and an increasing number of software tools supporting and encouraging their use and development. With the increasing use of semantic annotations in the context of systems biology modeling has come the realization that the current Core SBML specification defining their use contains limitations that reduce the scope of metadata that can be captured in such models. SBML Level 3 provides the facility to propose and develop optional extensions to the Core specification. One such extension is described here, with an initial proposal of an Annotation package. This proposal extends the current Core annotation specification to provide support for a richer set of semantic annotations while adhering more closely to the existing specification of Resource Description Framework (RDF).

To better understand telomere biology in budding yeast, we have performed systematic suppressor/enhancer analyses on yeast strains containing a point mutation in the essential telomere capping gene CDC13 (cdc13-1) or containing a null mutation in the DNA damage response and telomere capping gene YKU70 (yku70Δ). We performed Quantitative Fitness Analysis (QFA) on thousands of yeast strains containing mutations affecting telomere-capping proteins in combination with a library of systematic gene deletion mutations. To perform QFA, we typically inoculate 384 separate cultures onto solid agar plates and monitor growth of each culture by photography over time. The data are fitted to a logistic population growth model; and growth parameters, such as maximum growth rate and maximum doubling potential, are deduced. QFA reveals that as many as 5% of systematic gene deletions, affecting numerous functional classes, strongly interact with telomere capping defects. We show that, while Cdc13 and Yku70 perform complementary roles in telomere capping, their genetic interaction profiles differ significantly. At least 19 different classes of functionally or physically related proteins can be identified as interacting with cdc13-1, yku70Δ, or both. Each specific genetic interaction informs the roles of individual gene products in telomere biology. One striking example is with genes of the nonsense-mediated RNA decay (NMD) pathway which, when disabled, suppress the conditional cdc13-1 mutation but enhance the null yku70Δ mutation. We show that the suppressing/enhancing role of the NMD pathway at uncapped telomeres is mediated through the levels of Stn1, an essential telomere capping protein, which interacts with Cdc13 and recruitment of telomerase to telomeres. We show that increased Stn1 levels affect growth of cells with telomere capping defects due to cdc13-1 and yku70Δ. QFA is a sensitive, high-throughput method that will also be useful to understand other aspects of microbial cell biology., Author Summary Telomeres, specialized structures at the end of linear chromosomes, ensure that chromosome ends are not mistakenly treated as DNA double-strand breaks. Defects in the telomere cap contribute to ageing and cancer. In yeast, defects in telomere capping proteins can cause telomeres to behave like double-strand breaks. To better understand the telomere and responses to capping failure, we have combined a systematic yeast gene deletion library with mutations affecting important yeast telomere capping proteins, Cdc13 or Yku70. Quantitative Fitness Analysis (QFA) was used to accurately measure the fitness of thousands of different yeast strains containing telomere capping defects and additional deletion mutations. Interestingly, we find that many gene deletions suppress one type of telomere capping defect while enhancing another. Through QFA, we can begin to define the roles of different gene products in contributing to different aspects of the telomere cap. Strikingly, mutations in nonsense-mediated mRNA decay pathways, which degrade many RNA molecules, suppress the cdc13-1 defect while enhancing the yku70Δ defect. QFA is widely applicable and will be useful for understanding other aspects of yeast cell biology.

In last year's report on microblogging ISMB 2008 [1], the authors anticipated that new methods of using the Web and of reporting the conference would make live blogging even easier (http://www.bork.embl.de/~jensen/ismb2008/keynotes.php.html). This year, the ISMB/ECCB 2009 Web site contained all of the features of the mock-up, far more live bloggers participated than last year, and there was increased coverage of talks and special sessions. We, in turn, look forward to the new technologies appearing on the horizon (such as Google Wave), and how both tools and bloggers will make next year's conference an even greater success. In summary, conference organizers found that microblogging added value for all conference attendees, and allowed attendees to follow the thoughts of others as well as to follow presentations that conflicted with others they wished to see. The usefulness of live blogging extends beyond the duration of the conference, remaining accessible long after the conference has closed.

91Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle uponTyne, United Kingdom, 2School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom, 3QB3 Institute, University of California, Berkeley,California, United States of America, 4Harvard School of Public Health, Department of Biostatistics, Boston, Massachusetts, United States of America, 5Department ofComputer Science, Free University Berlin, Berlin, Germany, 6Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin,Germany, 7Biotec, TU Dresden, Dresden, Germany, 8International Society for Computational Biology, La Jolla, California, United States of America, 9Computational andStructural Biology Unit, EMBL, Heidelberg, Germany

Data management and sharing in omics science is highly challenging due to the constant evolution of experimental techniques, the range of instrument types and software used for analysis, and the high volumes of data produced. The Functional Genomics Experiment (FuGE) Model was created to provide a model for capturing descriptions of sample processing, experimental protocols and multidimensional data for any kind of omics experiment. FuGE has two modes of action: (a) as a storage architecture for experimental workflows and (b) as a framework for building new technology-specific data standards.FuGE is an object model that is converted into an XML implementation for data exchange. Software toolkits have been developed for data handling and for bridging between XML data files and relational database implementations. FuGE has been adopted by the Proteomics Standards Initiative (PSI, http://www.psidev.info ) for building several new data formats, and it is being used in a variety of other experimental contexts, thus allowing data to be integrated across a range of experimental types to support Systems Biology approaches. This chapter provides a practical guide for laboratories or groups wishing to manage their data, and for developers wishing to create new data formats using FuGE.

Data management and sharing in omics science is highly challenging due to the constant evolution of experimental techniques, the range of instrument types and software used for analysis, and the high volumes of data produced. The Functional Genomics Experiment (FuGE) Model was created to provide a model for capturing descriptions of sample processing, experimental protocols and multidimensional data for any kind of omics experiment. FuGE has two modes of action: (a) as a storage architecture for experimental workflows and (b) as a framework for building new technology-specific data standards.FuGE is an object model that is converted into an XML implementation for data exchange. Software toolkits have been developed for data handling and for bridging between XML data files and relational database implementations. FuGE has been adopted by the Proteomics Standards Initiative (PSI, http://www.psidev.info ) for building several new data formats, and it is being used in a variety of other experimental contexts, thus allowing data to be integrated across a range of experimental types to support Systems Biology approaches. This chapter provides a practical guide for laboratories or groups wishing to manage their data, and for developers wishing to create new data formats using FuGE.

With the quantity of genomic data increasing at an exponential rate, it is imperative that these data be captured electronically, in a standard format. Standardization activities must proceed within the auspices of open-access and international working bodies. To tackle the issues surrounding the development of better descriptions of genomic investigations, we have formed the Genomic Standards Consortium (GSC). Here, we introduce the minimum information about a genome sequence (MIGS) specification with the intent of promoting participation in its development and discussing the resources that will be required to develop improved mechanisms of metadata capture and exchange. As part of its wider goals, the GSC also supports improving the 'transparency' of the information contained in existing genomic databases.

This meeting report summarizes the proceedings of the “eGenomics: Cataloguing our Complete Genome Collection III” workshop held September 11–13, 2006, at the National Institute for Environmental eScience (NIEeS), Cambridge, United Kingdom. This 3rd workshop of the Genomic Standards Consortium was divided into two parts. The first half of the three-day workshop was dedicated to reviewing the genomic diversity of our current and future genome and metagenome collection, and exploring linkages to a series of existing projects through formal presentations. The second half was dedicated to strategic discussions. Outcomes of the workshop include a revised “Minimum Information about a Genome Sequence” (MIGS) specification (v1.1), consensus on a variety of features to be added to the Genome Catalogue (GCat), agreement by several researchers to adopt MIGS for imminent genome publications, and an agreement by the EBI and NCBI to input their genome collections into GCat for the purpose of quantifying the amount of optional data already available (e.g., for geographic location coordinates) and working towards a single, global list of all public genomes and metagenomes.

A report of BioSysBio 2009, the IET conference on Synthetic Biology, Systems Biology and Bioinformatics, Cambridge, UK, 23-25 March 2009.