<HashMap><database>BioModels</database><file_versions><headers><Content-Type>application/xml</Content-Type></headers><body><files><Txt>https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000047?filename=curation_notes.txt</Txt><Pdf>https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000047?filename=BIOMD0000000047.pdf</Pdf><Svg>https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000047?filename=BIOMD0000000047.svg</Svg><Owl>https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000047?filename=BIOMD0000000047-biopax3.owl</Owl><Owl>https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000047?filename=BIOMD0000000047-biopax2.owl</Owl><Xml>https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000047?filename=BIOMD0000000047_url.xml</Xml><Xml>https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000047?filename=manifest.xml</Xml><Other>https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000047?filename=BIOMD0000000047.ode</Other><Other>https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000047?filename=curation_image.jpeg</Other><Other>https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000047?filename=BIOMD0000000047.png</Other><Other>https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000047?filename=metadata.rdf</Other><Other>https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000047?filename=BIOMD0000000047_url.sedml</Other><Other>https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000047?filename=BIOMD0000000047-octave.m</Other><Other>https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000047?filename=BIOMD0000000047.m</Other><Other>https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000047?filename=BIOMD0000000047-matlab.m</Other></files><type>primary</type></body><statusCodeValue>200</statusCodeValue><statusCode>OK</statusCode></file_versions><scores/><additional><submitter>Harish Dharuri</submitter><curationStatus>Manually curated</curationStatus><modellingApproach>ordinary differential equation model</modellingApproach><levelVersion>L2V1</levelVersion><full_dataset_link>https://www.ebi.ac.uk/biomodels/BIOMD0000000047</full_dataset_link><publication_pubmed>15596518</publication_pubmed><isPrivate>false</isPrivate><repository>BioModels</repository><modelFormat>SBML</modelFormat><omics_type>Models</omics_type><tokenised_name>Oxhamre2005 Ca oscillation</tokenised_name><publication_year>2005</publication_year><submissionId>MODEL6623415355</submissionId><publication_authors>Camilla Oxhamre, Agneta Richter-Dahlfors, Vladimir P Zhdanov, Bengt Kasemo</publication_authors><first_author>Camilla Oxhamre</first_author><publication>15596518,
                            The toxin alpha-hemolysin expressed by uropathogenic Escherichia coli bacteria was recently shown as the first pathophysiologically relevant protein to induce oscillations of the intracellular Ca(2+) concentration in target cells. Here, we propose a generic three-variable kinetic model describing the Ca(2+) oscillations induced in single rat renal epithelial cells by this toxin. Specifically, we take into account the interplay between 1), the cytosolic Ca(2+) concentration; 2), IP(3)-sensitive Ca(2+) channels located in the membrane separating the cytosol and endoplasmic reticulum; and 3), toxin-related activation of production of IP(3) by phospholipase C. With these ingredients, the predicted response of cells exposed to the toxin is in good agreement with the results of experiments.. 4, 88.
                            Microbiology and Tumor Biology Center, Karolinska Institute, Stockholm, Sweden.</publication><submitter_mail>hdharuri@cds.caltech.edu</submitter_mail><submitter_affiliation>California Institute of Technology</submitter_affiliation><publicationId>BIOMD0000000047</publicationId><pubmed_abstract>The toxin alpha-hemolysin expressed by uropathogenic Escherichia coli bacteria was recently shown as the first pathophysiologically relevant protein to induce oscillations of the intracellular Ca(2+) concentration in target cells. Here, we propose a generic three-variable kinetic model describing the Ca(2+) oscillations induced in single rat renal epithelial cells by this toxin. Specifically, we take into account the interplay between 1), the cytosolic Ca(2+) concentration; 2), IP(3)-sensitive Ca(2+) channels located in the membrane separating the cytosol and endoplasmic reticulum; and 3), toxin-related activation of production of IP(3) by phospholipase C. With these ingredients, the predicted response of cells exposed to the toxin is in good agreement with the results of experiments.</pubmed_abstract><pubmed_abstract>This review provides a comparative overview of recent developments in the modelling of cellular calcium oscillations. A large variety of mathematical models have been developed for this wide-spread phenomenon in intra- and intercellular signalling. From these, a general model is extracted that involves six types of concentration variables: inositol 1,4,5-trisphosphate (IP3), cytoplasmic, endoplasmic reticulum and mitochondrial calcium, the occupied binding sites of calcium buffers, and the fraction of active IP3 receptor calcium release channels. Using this framework, the models of calcium oscillations can be classified into 'minimal' models containing two variables and 'extended' models of three and more variables. Three types of minimal models are identified that are all based on calcium-induced calcium release (CICR), but differ with respect to the mechanisms limiting CICR. Extended models include IP3--calcium cross-coupling, calcium sequestration by mitochondria, the detailed gating kinetics of the IP3 receptor, and the dynamics of G-protein activation. In addition to generating regular oscillations, such models can describe bursting and chaotic calcium dynamics. The earlier hypothesis that information in calcium oscillations is encoded mainly by their frequency is nowadays modified in that some effect is attributed to amplitude encoding or temporal encoding. This point is discussed with reference to the analysis of the local and global bifurcations by which calcium oscillations can arise. Moreover, the question of how calcium binding proteins can sense and transform oscillatory signals is addressed. Recently, potential mechanisms leading to the coordination of oscillations in coupled cells have been investigated by mathematical modelling. For this, the general modelling framework is extended to include cytoplasmic and gap-junctional diffusion of IP3 and calcium, and specific models are compared. Various suggestions concerning the physiological significance of oscillatory behaviour in intra- and intercellular signalling are discussed. The article is concluded with a discussion of obstacles and prospects.</pubmed_abstract><pubmed_title>A minimal generic model of bacteria-induced intracellular Ca2+ oscillations in epithelial cells.</pubmed_title><pubmed_title>Modelling of simple and complex calcium oscillations. From single-cell responses to intercellular signalling.</pubmed_title><pubmed_authors>Oxhamre Camilla C, Richter-Dahlfors Agneta A, Zhdanov Vladimir P VP, Kasemo Bengt B</pubmed_authors><pubmed_authors>Schuster Stefan S, Marhl Marko M, Höfer Thomas T</pubmed_authors></additional><is_claimable>false</is_claimable><name>Oxhamre2005_Ca_oscillation</name><description>
      
        The model should reproduce the figure 1C of the article (successfully reproduced in MathSBML). If your software does not support the variable "time", you can replace the assignmentRule:      
          n = n0 * [ exp(-kbN*time) + kappa * (1 - exp(-kbN*time))]      
          by      
          n = n0 * kappa      
            
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          for more information.      
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            To cite BioModels Database, please use:      Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
                
            
      
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