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Tommy Yu, Catherine M Lloyd,
David P Nickerson,
Michael T Cooling,
Andrew K Miller,
Alan Garny,
Jonna R Terkildsen,
James Lawson,
Randall D Britten,
Peter J Hunter,
Poul M F Nielsen
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ABSTRACT: 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/.
Bioinformatics 03/2011; 27(5):743-4. · 5.47 Impact Factor
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Tommy Yu, Catherine M. Lloyd,
David P. Nickerson,
Mike T. Cooling,
Andrew K. Miller,
Alan Garny,
Jonna R. Terkildsen,
James R. Lawson,
Randall Britten,
Peter J. Hunter,
Poul M. F. Nielsen
Bioinformatics. 01/2011; 27:743-744.
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Daniel A Beard,
Randall Britten,
Mike T Cooling,
Alan Garny,
Matt D B Halstead,
Peter J Hunter,
James Lawson, Catherine M Lloyd,
Justin Marsh,
Andrew Miller,
David P Nickerson,
Poul M F Nielsen,
Taishin Nomura,
Shankar Subramanium,
Sarala M Wimalaratne,
Tommy Yu
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ABSTRACT: The development of standards for encoding mathematical models is an important component of model building and model sharing among scientists interested in understanding multi-scale physiological processes. CellML provides such a standard, particularly for models based on biophysical mechanisms, and a substantial number of models are now available in the CellML Model Repository. However, there is an urgent need to extend the current CellML metadata standard to provide biological and biophysical annotation of the models in order to facilitate model sharing, automated model reduction and connection to biological databases. This paper gives a broad overview of a number of new developments on CellML metadata and provides links to further methodological details available from the CellML website.
Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences 06/2009; 367(1895):1845-67. · 2.77 Impact Factor
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ABSTRACT: The CellML language was developed in response to the need for a high-level language to represent and exchange mathematical models of biological processes. The flexible structure of CellML allows modellers to construct mathematical models of the same biological system in many different ways. However, some modelling styles do not naturally lead to clear abstractions of the biophysical concepts and produce CellML models that are hard to understand and from which it is difficult to isolate parts that may be useful for constructing other models. In this article, we advocate building CellML models which isolate common biophysical concepts and, using these, to build mathematical models of biological processes that provide a close correspondence between the CellML model and the underlying biological process. Subsequently, models of higher complexity can be constructed by reusing these modularized CellML models in part or in whole. Development of CellML models that best describe the underlying biophysical concepts thus avoids the need to code models from scratch and enhances the extensibility, reusability, consistency and interpretation of the models.
Experimental physiology 02/2009; 94(5):472-85. · 3.17 Impact Factor
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Bioinformatics. 01/2009; 25:2263-2270.
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Bioinformatics. 01/2009; 25:3012-3019.
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ABSTRACT: The CellML Model Repository provides free access to over 330 biological models. The vast majority of these models are derived from published, peer-reviewed papers. Model curation is an important and ongoing process to ensure the CellML model is able to accurately reproduce the published results. As the CellML community grows, and more people add their models to the repository, model annotation will become increasingly important to facilitate data searches and information retrieval. AVAILABILITY: The CellML Model Repository is publicly accessible at http://www.cellml.org/models.
Bioinformatics 09/2008; 24(18):2122-3. · 5.47 Impact Factor
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ABSTRACT: Macrophages have traditionally been identified in murine tissues using a small range of markers, typically F4/80, CD68 and CD11b. However many studies have suggested that substantial heterogeneity exists in macrophage populations, and no single marker, nor even pair of markers, can necessarily identify all the populations. Further, many of the key monoclonal antibodies have been raised in the same species, making it difficult to combine them in histochemical studies. Here we have optimised a triple colour immunofluorescent staining protocol, utilising an anti-FITC technique, to allow antibodies to macrophage markers to be used simultaneously. We highlight the substantial heterogeneity of cells in both normal liver and spleen that stain for F4/80, CD68, CD11b, and CD11c. Using diet-induced steatohepatitis as a model of liver inflammation, we show that CD11b is expressed by newly migrating macrophage precursors, but is an unreliable marker for macrophage precursors when used alone because it is also expressed by migrating neutrophils. In healthy livers CD11c expression is a unique feature of a population of cells immediately surrounding the sinusoids. However, during hepatic inflammation CD11c can also be co-expressed by other cells, including both infiltrating cells and F4/80+ cells within the liver parenchyma. While no one marker alone is sufficient to account for all macrophage populations, we confirm that F4/80 marks the majority of the tissue-resident macrophages in both the liver and the spleen, although F4/80- populations that are positive for CD68, CD11b, or CD11c also exist. Distinguishing between tissue macrophages and dendritic cells with these markers remains problematic.
Journal of Immunological Methods 06/2008; 334(1-2):70-81. · 2.20 Impact Factor
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Simulation. 01/2003; 79:740-747.
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ABSTRACT: Advances in biotechnology and experimental techniques have lead to the elucidation of vast amounts of biological data. Mathematical models provide a method of analysing this data; however, there are two issues that need to be addressed: (1) the need for standards for defining cell models so they can, for example, be exchanged across the World Wide Web, and also read into simulation software in a consistent format and (2) eliminating the errors which arise with the current method of model publication. CellML has evolved to meet these needs of the modelling community. CellML is a free, open-source, eXtensible markup language based standard for defining mathematical models of cellular function. In this paper we summarise the structure of CellML, its current applications (including biological pathway and electrophysiological models), and its future development—in particular, the development of toolsets and the integration of ontologies.
Progress in Biophysics and Molecular Biology.
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[show abstract]
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ABSTRACT: Advances in biotechnology and experimental techniques have lead to the elucidation of vast amounts of biological data. Mathematical models provide a method of analysing this data; however, there are two issues that need to be addressed: (1) the need for standards for defining cell models so they can, for example, be exchanged across the World Wide Web, and also read into simulation software in a consistent format and (2) eliminating the errors which arise with the current method of model publication. CellML has evolved to meet these needs of the modelling community. CellML is a free, open-source, eXtensible markup language based standard for defining mathematical models of cellular function. In this paper we summarise the structure of CellML, its current applications (including biological pathway and electrophysiological models), and its future development--in particular, the development of toolsets and the integration of ontologies.
Progress in Biophysics and Molecular Biology 85(2-3):433-50. · 3.20 Impact Factor
