Demo Data from VLDB 2026 / reviewers in the wild / expert
Catherine M. Lloyd
dblp:35/3147
· DBLP profile ↗
5ranked-venue papers
1as first author
0since 2021 · last 2011
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 5 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
4 papers |
Bioinformatics and computational biology · 95% Computational science and engineering · 5% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 100% |
Topics — the 7 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › systems biology
biological model representation |
0.2 | 2 | 2009 | Biophysical annotation and representation of CellML models · Bioinform. 2009 A method for visualizing CellML models · Bioinform. 2009 |
Bioinformatics and computational biology
computational physiology |
0.0 | 1 | 2011 | The Physiome Model Repository 2 · Bioinform. 2011 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
ontology |
0.0 | 1 | 2009 | Biophysical annotation and representation of CellML models · Bioinform. 2009 |
Visualization and visual analytics
model visualization |
0.0 | 1 | 2009 | A method for visualizing CellML models · Bioinform. 2009 |
Visualization and visual analytics
scientific visualization |
0.0 | 1 | 2009 | A method for visualizing CellML models · Bioinform. 2009 |
Computational science and engineering
information retrieval |
0.0 | 1 | 2008 | The CellML Model Repository · Bioinform. 2008 |
Bioinformatics and computational biology › systems biology › biological model representation
model annotation |
0.0 | 1 | 2008 | The CellML Model Repository · Bioinform. 2008 |
Methods — techniques the papers use, named apart from their topics
visual language · 0.2ontology annotation · 0.2graph reduction · 0.2web-based collaboration · 0.1version control · 0.1model curation · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | The Physiome Model Repository 2abstractMOTIVATION: 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/. 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 |
Bioinform. | 2 |
| 2009 | A method for visualizing CellML modelsabstractMOTIVATION: 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. Sarala M. Wimalaratne, Matt D. B. Halstead, Catherine M. Lloyd, Mike T. Cooling, Edmund J. Crampin, Poul M. F. Nielsen |
Bioinform. | 3 |
| 2009 | Biophysical annotation and representation of CellML modelsabstractMOTIVATION: 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. Sarala M. Wimalaratne, Matt D. B. Halstead, Catherine M. Lloyd, Edmund J. Crampin, Poul M. F. Nielsen |
Bioinform. | 3 |
| 2009 | Astrocytic Mechanisms Explaining Neural-Activity-Induced Shrinkage of Extraneuronal SpaceabstractNeuronal 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. Ivar Østby, Leiv Øyehaug, Gaute T. Einevoll, Erlend A. Nagelhus, Erik Plahte, Thomas Zeuthen, Catherine M. Lloyd, Ole Petter Ottersen, Stig W. Omholt |
PLoS Comput. Biol. | 7 |
| 2008 | The CellML Model RepositoryabstractSUMMARY: 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. Catherine M. Lloyd, James R. Lawson, Peter J. Hunter, Poul M. F. Nielsen |
Bioinform. | 1 |