Catherine M Lloyd

University of Auckland, Auckland, Auckland, New Zealand

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Publications (31)46.51 Total impact

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    ABSTRACT: Exposed to a sufficiently high extracellular potassium concentration ([K( + )]₀), 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( + )]₀ 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( + )]₀-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.
    Journal of Computational Neuroscience 06/2011; 32(1):147-65. · 2.44 Impact Factor
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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
  • Bioinformatics. 01/2011; 27:743-744.
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    ABSTRACT: 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.
    Bioinformatics 09/2009; 25(22):3012-9. · 5.47 Impact Factor
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    ABSTRACT: 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.
    FEMS Yeast Research 08/2009; 9(8):1137-47. · 2.46 Impact Factor
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    ABSTRACT: MOTIVATION: CellML is an implementation-independent model description language for specifying and exchanging biological processes. The focus of CellML is the representation of mathematical formulations of biological processes. The language captures the mathematical and model building constructs well, but does not lend itself to capturing the biology these models represent. RESULTS: This article describes the development of an ontological framework for annotating CellML models with biophysical concepts. We demonstrate that, by using these ontological mappings, in combination with a set of graph reduction rules, it is possible to represent the underlying biological process described in a CellML model.
    Bioinformatics 07/2009; 25(17):2263-70. · 5.47 Impact Factor
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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. · 2.89 Impact Factor
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    ABSTRACT: Neuronal stimulation causes approximately 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(+)/HCO(3) (-) (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.
    PLoS Computational Biology 02/2009; 5(1):e1000272. · 4.87 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
  • Bioinformatics. 01/2009; 25:3012-3019.
  • Journal of Physiological Sciences. 01/2009; 59:352-352.
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    ABSTRACT: Many phenomenological models of cerebral aneurysm formation have been proposed. Such studies have focused on modeling the structural adaption of the arterial wall. However, further development is required to accurately represent the underlying mechanobiology during growth and remodeling processes. Here, we present a general framework for modeling the interplay of fluid dynamics, molecular signaling pathways and arterial wall mechanics.
    12/2008: pages 1894-1898;
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    Jiangjun Cui, Jaap A Kaandorp, Catherine M Lloyd
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    ABSTRACT: The zinc homeostasis system in Escherichia coli is one of the most intensively studied prokaryotic zinc homeostasis systems. Its underlying regulatory machine consists of repression on zinc influx through ZnuABC by Zur (Zn2+ uptake regulator) and activation on zinc efflux via ZntA by ZntR (a zinc-responsive regulator). Although these transcriptional regulations seem to be well characterized, and there is an abundance of detailed in vitro experimental data available, as yet there is no mathematical model to help interpret these data. To our knowledge, the work described here is the first attempt to use a mathematical model to simulate these regulatory relations and to help explain the in vitro experimental data. We develop a unified mathematical model consisting of 14 reactions to simulate the in vitro transcriptional response of the zinc homeostasis system in E. coli. Firstly, we simulate the in vitro Zur-DNA interaction by using two of these reactions, which are expressed as 4 ordinary differential equations (ODEs). By imposing the conservation restraints and solving the relevant steady state equations, we find that the simulated sigmoidal curve matches the corresponding experimental data. Secondly, by numerically solving the ODEs for simulating the Zur and ZntR run-off transcription experiments, and depicting the simulated concentrations of zntA and znuC transcripts as a function of free zinc concentration, we find that the simulated curves fit the corresponding in vitro experimental data. Moreover, we also perform simulations, after taking into consideration the competitive effects of ZntR with the zinc buffer, and depict the simulated concentration of zntA transcripts as a function of the total ZntR concentration, both in the presence and absence of Zn(II). The obtained simulation results are in general agreement with the corresponding experimental data. Simulation results show that our model can quantitatively reproduce the results of several of the in vitro experiments conducted by Outten CE and her colleagues. Our model provides a detailed insight into the dynamics of the regulatory system and also provides a general framework for simulating in vitro metal-binding and transcription experiments and interpreting the relevant experimental data.
    BMC Systems Biology 11/2008; 2:89. · 2.98 Impact Factor
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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. · 2.23 Impact Factor
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    ABSTRACT: The CellML repository now contains a number of well curated CellML models of cardiac biology and physiology at the cellular and subcellular level. Recently this resource has been growing rapidly in both quality and quantity and includes models of cardiac electrophysiology, excitation-contraction coupling, myofilament mechanics, signalling systems and combinations thereof. Herein we describe the CellML model repository, its range of models, the tools used to develop and test these models and the processes and aims of curating them. The relevance of this resource to multi-scale modelling of the heart in the present and the future is then discussed. Poster presented at Waiheke 2008 Multiscale Modelling of the Heart Workshop.
    01/2008;
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    ABSTRACT: 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.
    SIMULATION: Transactions of The Society for Modeling and Simulation International 01/2003; 79:740-747. · 0.69 Impact Factor
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    Catherine M Lloyd, Matt D B Halstead, Poul F Nielsen
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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. · 2.91 Impact Factor
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    ABSTRACT: To facilitate model reuse among European researchers in a new Network of Excellence (NoE) for the EuroPhysiome or "Virtual Physiological Human" (VPH) project, two XML markup languages for encoding biological models, CellML (www.cellml.org) & FieldML (www.fieldml.org), are being further developed. CellML deals with models of so-called ‘lumped parameter’ systems, where spatial effects are averaged, and typically involves systems of ordinary differential equations and algebraic equations. FieldML addresses the spatial variations in cell or tissue properties where the models typically rely on partial differential equations. The two standards can be used together. These languages, which define the structure of a model, the mathematical equations and the associated metadata, enable (i) automated checking to ensure consistency of physical units used in the model equations, (ii) models developed by different groups to be combined using commonly agreed ontological terms within the metadata, (iii) models to be modularized and used in libraries to make it easier to create complex models by importing simpler ones. Model repositories based on these standards and implementing a wide variety of models from peer-reviewed publications have been developed (www.cellml.org/models) and open source software tools for creating, visualizing and executing these models are currently available (www.cellml.org/tools) and under continuous development. This talk will describe the new VPH NoE, new developments in CellML and also briefly describe recent progress on FieldML.
    2008 SIAM Conference on Life Sciences;
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    ABSTRACT: CellML (http://www.cellml.org) is an XML standard for encoding differential-algebraic equation (DAE) models and facilitating the building of model repositories and general purpose software tools. The CellML 1.1 standard (available at http://www.cellml.org/specifications/cellml_1.1) supports public and private interfaces that enable encapsulation hierarchies and provide mechanisms for information hiding and abstraction. Model reuse is facilitated by the import element, enabling new models to be constructed by combining existing models into model hierarchies. The process of importing models, developed by different research groups, into a more complex integrated model will be illustrated with three cardiac myocyte models. The 1st is the Pandit et al model [1] of cardiac excitability, the 2nd is the Hinch et al model [2] of calcium dynamics and the 3rd is the Niederer et al model [3] of myofilament mechanics. The resulting coupled electromechanics model is reported in Terkildsen et al [4]. We will also discuss the decomposition of these three models into a library of separate modular components (e.g. individual ion channel models). 1. http://www.cellml.org/models/pandit_clark_giles_demir_2001_version07 2. http://www.cellml.org/models/hinch_greenstein_tanskanen_xu_winslow_2004_version01 3. http://www.cellml.org/models/niederer_hunter_smith_2006_version01 4. Terkildsen, J.R., Niederer, S., Crampin E.J., Hunter, P.J., and Smith N.P. Using Physiome standards to couple cellular functions for cardiac excitation contraction. Exp Physiol; 93, pp919-929, 2008. http://www.cellml.org/models/terkildsen_niederer_crampin_hunter_smith_2008_version01
    2008 SIAM Conference on Life Sciences;