The CellML 1.1 Specification

Cuellar, Autumn; Hedley, Warren J.; Nelson, Melanie; Lloyd, Catherine M.; Halstead, Matt D. B.; Bullivant, David; Nickerson, David; Hunter, Peter; Nielsen, Poul

doi:10.1515/jib-2015-259

Cited by 25 publications

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“…For efficient analysis, simulation, and calibration (e.g., the estimation of parameter values) of biological network models a multiple-purpose and efficient numerical solver library is prerequisite. Although the language specifications of SBML [16-22] and CellML [23] describe the semantics of models in these formats and their interpretation, the algorithmic implementation is still not straightforward.…”

Section: Introductionmentioning

confidence: 99%

The systems biology simulation core algorithm

et al. 2013

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BackgroundWith the increasing availability of high dimensional time course data for metabolites, genes, and fluxes, the mathematical description of dynamical systems has become an essential aspect of research in systems biology. Models are often encoded in formats such as SBML, whose structure is very complex and difficult to evaluate due to many special cases.ResultsThis article describes an efficient algorithm to solve SBML models that are interpreted in terms of ordinary differential equations. We begin our consideration with a formal representation of the mathematical form of the models and explain all parts of the algorithm in detail, including several preprocessing steps. We provide a flexible reference implementation as part of the Systems Biology Simulation Core Library, a community-driven project providing a large collection of numerical solvers and a sophisticated interface hierarchy for the definition of custom differential equation systems. To demonstrate the capabilities of the new algorithm, it has been tested with the entire SBML Test Suite and all models of BioModels Database.ConclusionsThe formal description of the mathematics behind the SBML format facilitates the implementation of the algorithm within specifically tailored programs. The reference implementation can be used as a simulation backend for Java™-based programs. Source code, binaries, and documentation can be freely obtained under the terms of the LGPL version 3 from http://simulation-core.sourceforge.net. Feature requests, bug reports, contributions, or any further discussion can be directed to the mailing list simulation-core-development@lists.sourceforge.net.

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Section: Introductionmentioning

confidence: 99%

The systems biology simulation core algorithm

et al. 2013

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“…• CellML 1.1 [20] • CellML Metadata Framework 2.0 [21] SBOL (Synthetic Biology Open Language), to exchange knowledge about synthetic biology designs. SBOL covers both structural information, such as hierarchically annotated DNA, RNA, and protein sequences for design components; and behavioural information, such as the interactions between these components [22].…”

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confidence: 99%

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2016

Schreiber

Bader

Gleeson

et al. 2016

Journal of Integrative Bioinformatics

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SummaryStandards are essential to the advancement of science and technology. In systems and synthetic biology, numerous standards and associated tools have been developed over the last 16 years. This special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards, as well as to provide centralised and easily citable access to them.Norms and requirements for objects, methods, processes and practices shape our everyday lives. Often they are represented in formal documents each describing a standard for a particular domain. The use of standards offers many benefits, such as improving the precision and efficiency of information exchange, reducing the cost of production, allowing parts from different producers to be combined or interchanged, and much more. Standards are not static, but develop and evolve with the progress of science and technology.When new, innovative areas (such as systems and synthetic biology) first emerge, they often either lack proper standards altogether, or the documents describing the standards in use are not available from a centralised location. To help develop and disseminate standards for systems and synthetic biology, the COMBINE (the 'COmputational Modeling in BIology' NEtwork) initiative was formed in 2010 [1]. This initiative "… is a network formed by the communities developing standards and formats to share computational models. Working This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License (http:// creativecommons.org/licenses/by-nc-nd/3.0/).

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“…Terms of this branch can, for example, be used to capture changes in the annotations. 3 The ModelDefinition refers to the actual biological system, for example a reaction network. Terms of this branch can, for example, be used to specify the parts of a model that are affected by a change.…”

Section: Resultsmentioning

confidence: 99%

“…The combination of several statements using terms of the different branches allows users to be very specific. COMODI offers terms [2] creativecommons.org/licenses/by-sa/4.0/ [3] bioportal.bioontology.org/ontologies/COMODI describing the reason and the intention of a change. Following the example from the previous section, the annotation of the parameter update might look like [4] : #1 a comodi:Update ; comodi:appliesTo comodi:XmlAttribute ; comodi:affects comodi:ParameterSetup; comodi:hasIntention comodi:Correction ; comodi:hasReason comodi:MismatchWithPublication .…”

Section: ] />mentioning

confidence: 99%

“…This task is greatly supported by common languages to describe model-based results [1]. Computational models of biological systems are frequently described in XML standard formats such as the Systems Biology Markup Language (SBML, [2]) or CellML [3]. These formats have several advantages: Models can be simulated, analysed, and visualised using different software tools; models encoded in standard formats may survive the decease of the tool used to create the model; model exchange becomes feasible; and models can more easily be shared, published, and reused.…”

Section: Introductionmentioning

confidence: 99%

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Supplemental Information 2: Example COMBINE Archive of Annotated Differences

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Motivation:Open model repositories provide ready-to-reuse computational models of biological systems. Models within those repositories evolve over time, leading to many alternative and subsequent versions. Taken together, the underlying changes reflect a model's provenance and thus can give valuable insights into the studied biology.Currently, however, changes cannot be semantically interpreted. To improve this situation, we developed an ontology of terms describing changes in computational biology models.The ontology can be used by scientists and within software to characterise model updates at the level of single changes. When studying or reusing a model, these annotations help with determining the relevance of a change in a given context. Methods:We manually studied changes in selected models from BioModels and the Physiome Model Repository. Using the BiVeS tool for difference detection, we then performed an automatic analysis of changes in all models published in these repositories. The resulting set of concepts led us to define candidate terms for the ontology. In a final step, we aggregated and classified these terms and built the first version of the ontology.Results: We present COMODI, an ontology needed because COmputational MOdels DIffer.It empowers users and software to describe changes in a model on the semantic level.COMODI also enables software to implement user-specific filter options for the display of model changes. Finally, COMODI is the next step towards predicting how a change in a model influences the simulation study. Abstract Motivation: Open model repositories provide ready-to-reuse computational models of biological systems. Models within those repositories evolve over time, leading to different model versions. Taken together, the underlying changes reflect a model's provenance and thus can give valuable insights into the studied biology. Currently, however, changes cannot be semantically interpreted. To improve this situation, we developed an ontology of terms describing changes in models. The ontology can be used by scientists and within software to characterise model updates at the level of single changes. When studying or reusing a model, these annotations help with determining the relevance of a change in a given context. Conclusion

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The CellML 1.1 Specification

Cited by 25 publications

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The systems biology simulation core algorithm

The systems biology simulation core algorithm

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2016

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