Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.

Biological researchers increasingly rely on computational models to integrate biological systems knowledge, test hypotheses, and forecast system behavior.The expanding size of these models requires solutions for managing their complexity.Modularity, a time-tested design principle for managing complexity, can be applied within the biological modeling field to parallelize work, automate composition, and promote effective model sharing.As modelers of complex biological systems, we aim to apply modular production to accelerate our efforts and have therefore investigated several currently available approaches for modular modeling.We argue that some traditional features of modularity, in particular the isolation of a module's contents from the rest of the system, can impede model sharing and composition when applied within the context of biological simulation.Alternative approaches that can automatically interface model components based on the biological meaning of their contents (their semantics) avoid these limitations.Our conclusions have strategic implications for the design of systems biology, synthetic biology, and integrated physiological modeling technologies, as well as communitylevel model curation efforts.

MOTIVATION: The need for the automated computational design of genetic circuits is becoming increasingly apparent with the advent of ever more complex and ambitious synthetic biology projects. Currently, most circuits are designed through the assembly of models of individual parts such as promoters, ribosome binding sites and coding sequences. These low level models are combined to produce a dynamic model of a larger device that exhibits a desired behaviour. The larger model then acts as a blueprint for physical implementation at the DNA level. However, the conversion of models of complex genetic circuits into DNA sequences is a non-trivial undertaking due to the complexity of mapping the model parts to their physical manifestation. Automating this process is further hampered by the lack of computationally tractable information in most models. RESULTS: We describe a method for automatically generating DNA sequences from dynamic models implemented in CellML and Systems Biology Markup Language (SBML). We also identify the metadata needed to annotate models to facilitate automated conversion, and propose and demonstrate a method for the markup of these models using RDF. Our algorithm has been implemented in a software tool called MoSeC. AVAILABILITY: The software is available from the authors' web site http://research.ncl.ac.uk/synthetic_biology/downloads.html.

MOTIVATION: The Physiome Model Repository 2 (PMR2) software was created as part of the IUPS Physiome Project (Hunter and Borg, 2003), and today it serves as the foundation for the CellML model repository. Key advantages brought to the end user by PMR2 include: facilities for model exchange, enhanced collaboration and a detailed change history for each model. AVAILABILITY: PMR2 is available under an open source license at http://www.cellml.org/tools/pmr/; a fully functional instance of this software can be accessed at http://models.physiomeproject.org/.

BACKGROUND: Building repositories of computational models of biological systems ensures that published models are available for both education and further research, and can provide a source of smaller, previously verified models to integrate into a larger model. One problem with earlier repositories has been the limitations in facilities to record the revision history of models. Often, these facilities are limited to a linear series of versions which were deposited in the repository. This is problematic for several reasons. Firstly, there are many instances in the history of biological systems modelling where an 'ancestral' model is modified by different groups to create many different models. With a linear series of versions, if the changes made to one model are merged into another model, the merge appears as a single item in the history. This hides useful revision history information, and also makes further merges much more difficult, as there is no record of which changes have or have not already been merged. In addition, a long series of individual changes made outside of the repository are also all merged into a single revision when they are put back into the repository, making it difficult to separate out individual changes. Furthermore, many earlier repositories only retain the revision history of individual files, rather than of a group of files. This is an important limitation to overcome, because some types of models, such as CellML 1.1 models, can be developed as a collection of modules, each in a separate file. The need for revision history is widely recognised for computer software, and a lot of work has gone into developing version control systems and distributed version control systems (DVCSs) for tracking the revision history. However, to date, there has been no published research on how DVCSs can be applied to repositories of computational models of biological systems. RESULTS: We have extended the Physiome Model Repository software to be fully revision history aware, by building it on top of Mercurial, an existing DVCS. We have demonstrated the utility of this approach, when used in conjunction with the model composition facilities in CellML, to build and understand more complex models. We have also demonstrated the ability of the repository software to present version history to casual users over the web, and to highlight specific versions which are likely to be useful to users. CONCLUSIONS: Providing facilities for maintaining and using revision history information is an important part of building a useful repository of computational models, as this information is useful both for understanding the source of and justification for parts of a model, and to facilitate automated processes such as merges. The availability of fully revision history aware repositories, and associated tools, will therefore be of significant benefit to the community.

This FAIRsharing record describes: The MIASE Guidelines, initiated by the BioModels.net effort, are a community effort to identify the Minimal Information About a Simulation Experiment, necessary to enable the reproducible simulation experiments. Consequently, the MIASE Guidelines list the information that a modeller needs to provide to enable the execution and reproduction of a numerical simulation experiment, derived from a given set of quantitative models. MIASE is a set of guidelines suitable for use with any structured format for simulation experiments. As such, MIASE is designed to help modelers and software tools to exchange their simulation settings and to foster collaboration.

MOTIVATION: Fabrication of synthetic biological systems is greatly enhanced by incorporating engineering design principles and techniques such as computer-aided design. To this end, the ongoing standardization of biological parts presents an opportunity to develop libraries of standard virtual parts in the form of mathematical models that can be combined to inform system design. RESULTS: We present an online Repository, populated with a collection of standardized models that can readily be recombined to model different biological systems using the inherent modularity support of the CellML 1.1 model exchange format. The applicability of this approach is demonstrated by modeling gold-medal winning iGEM machines. AVAILABILITY AND IMPLEMENTATION: The Repository is available online as part of http://models.cellml.org. We hope to stimulate the worldwide community to reuse and extend the models therein, and contribute to the Repository of Standard Virtual Parts thus founded. Systems Model architecture information for the Systems Model described here, along with an additional example and a tutorial, is also available as Supplementary information. The example Systems Model from this manuscript can be found at http://models.cellml.org/workspace/bugbuster. The Template models used in the example can be found at http://models.cellml.org/workspace/SVP_Templates200906.

MOTIVATION: The Physiome Project was established in 1997 to develop tools to facilitate international collaboration in the physiological sciences and the sharing of biological models and experimental data. The CellML language was developed to represent and exchange mathematical models of biological processes. CellML models can be very complicated, making it difficult to interpret the underlying physical and biological concepts and relationships captured/described in the mathematical model. RESULTS: To address this issue a set of ontologies was developed to explicitly annotate the biophysical concepts represented in the CellML models. This article presents a framework that combines a visual language, together with CellML ontologies, to support the visualization of the underlying physical and biological concepts described by the mathematical model and also their relationships with the CellML model. Automated CellML model visualization assists in the interpretation of model concepts and facilitates model communication and exchange between different communities.