Catherine M. Lloyd

An Overview of CellML 1.1, a Biological Model Description Language

CellML is an XML-based exchange format developed by the University of Auckland in collaboration with Physiome Sciences, Inc. CellML 1.1 has a component-based architecture allowing a modeller to build complex systems of models that expand and reuse previously published models. CellML Metadata is a format for encoding contextual information for a model. CellML 1.1 can be used in conjunction with CellML Metadata to provide a complete description of the structure and underlying mathematics of biological models. A repository of over 200 electrophysiological, mechanical, signal transduction, and metabolic pathway models is available at www.cellml.org.

The CellML Model Repository

SUMMARY 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.

The Physiome Model Repository 2

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/.

Astrocytic Mechanisms Explaining Neural-Activity-Induced Shrinkage of Extraneuronal Space

Neuronal stimulation causes ∼30% shrinkage of the extracellular space (ECS) between neurons and surrounding astrocytes in grey and white matter under experimental conditions. Despite its possible implications for a proper understanding of basic aspects of potassium clearance and astrocyte function, the phenomenon remains unexplained. Here we present a dynamic model that accounts for current experimental data related to the shrinkage phenomenon in wild-type as well as in gene knockout individuals. We find that neuronal release of potassium and uptake of sodium during stimulation, astrocyte uptake of potassium, sodium, and chloride in passive channels, action of the Na/K/ATPase pump, and osmotically driven transport of water through the astrocyte membrane together seem sufficient for generating ECS shrinkage as such. However, when taking into account ECS and astrocyte ion concentrations observed in connection with neuronal stimulation, the actions of the Na+/K+/Cl− (NKCC1) and the Na+/HCO3 − (NBC) cotransporters appear to be critical determinants for achieving observed quantitative levels of ECS shrinkage. Considering the current state of knowledge, the model framework appears sufficiently detailed and constrained to guide future key experiments and pave the way for more comprehensive astroglia–neuron interaction models for normal as well as pathophysiological situations.

CellML metadata standards, associated tools and repositories

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.

Calcium homeostasis and signaling in yeast cells and cardiac myocytes

Calcium ions are the most ubiquitous and versatile signaling molecules in eukaryotic cells. Calcium homeostasis and signaling systems are crucial for both the normal growth of the budding yeast Saccharomyces cerevisiae and the intricate working of the mammalian heart. In this paper, we make a detailed comparison between the calcium homeostasis/signaling networks in yeast cells and those in mammalian cardiac myocytes. This comparison covers not only the components, structure and function of the networks but also includes existing knowledge on the measured and simulated network dynamics using mathematical models. Surprisingly, most of the factors known in the yeast calcium homeostasis/signaling network are conserved and operate similarly in mammalian cells, including cardiac myocytes. Moreover, the budding yeast S. cerevisiae is a simple organism that affords powerful genetic and genomic tools. Thus, exploring and understanding the calcium homeostasis/signaling system in yeast can provide a shortcut to help understand calcium homeostasis/signaling systems in mammalian cardiac myocytes. In turn, this knowledge can be used to help treat relevant human diseases such as pathological cardiac hypertrophy and heart failure.

Three-colour fluorescence immunohistochemistry reveals the diversity of cells staining for macrophage markers in murine spleen and liver

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.

Dependence of spontaneous neuronal firing and depolarisation block on astroglial membrane transport mechanisms

Exposed to a sufficiently high extracellular potassium concentration ([K + ]o), the neuron can fire spontaneous discharges or even become inactivated due to membrane depolarisation (‘depolarisation block’). Since these phenomena likely are related to the maintenance and propagation of seizure discharges, it is of considerable importance to understand the conditions under which excess [K + ]o causes them. To address the putative effect of glial buffering on neuronal activity under elevated [K + ]o conditions, we combined a recently developed dynamical model of glial membrane ion and water transport with a Hodgkin–Huxley type neuron model. In this interconnected glia-neuron model we investigated the effects of natural heterogeneity or pathological changes in glial membrane transporter density by considering a large set of models with different, yet empirically plausible, sets of model parameters. We observed both the high [K + ]o-induced duration of spontaneous neuronal firing and the prevalence of depolarisation block to increase when reducing the magnitudes of the glial transport mechanisms. Further, in some parameter regions an oscillatory bursting spiking pattern due to the dynamical coupling of neurons and glia was observed. Bifurcation analyses of the neuron model and of a simplified version of the neuron-glia model revealed further insights about the underlying mechanism behind these phenomena. The above insights emphasise the importance of combining neuron models with detailed astroglial models when addressing phenomena suspected to be influenced by the astroglia-neuron interaction. To facilitate the use of our neuron-glia model, a CellML version of it is made publicly available.

Facilitating modularity and reuse: guidelines for structuring CellML 1.1 models by isolating common biophysical concepts

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.

A method for visualizing CellML models

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.