{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/MODEL9086953089?filename=MODEL9086953089.pdf"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/MODEL9086953089?filename=MODEL9086953089.svg"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/MODEL9086953089?filename=MODEL9086953089-biopax2.owl","https://www.ebi.ac.uk/biomodels/model/download/MODEL9086953089?filename=MODEL9086953089-biopax3.owl"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/MODEL9086953089?filename=MODEL9086953089_urn.xml","https://www.ebi.ac.uk/biomodels/model/download/MODEL9086953089?filename=MODEL9086953089_url.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/MODEL9086953089?filename=MODEL9086953089.sci","https://www.ebi.ac.uk/biomodels/model/download/MODEL9086953089?filename=MODEL9086953089.png","https://www.ebi.ac.uk/biomodels/model/download/MODEL9086953089?filename=MODEL9086953089.m","https://www.ebi.ac.uk/biomodels/model/download/MODEL9086953089?filename=MODEL9086953089.xpp","https://www.ebi.ac.uk/biomodels/model/download/MODEL9086953089?filename=MODEL9086953089.vcml"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"submitter":["Sharat Vayttaden"],"curationStatus":["Non-curated"],"modellingApproach":["ordinary differential equation model"],"levelVersion":["L2V1"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/MODEL9086953089"],"publication_pubmed":["16110334"],"isPrivate":["false"],"repository":["BioModels"],"modelFormat":["SBML"],"omics_type":["Models"],"tokenised_name":["Hayer2005 CaMKII model3"],"publication_year":["2005"],"submissionId":["MODEL9086953089"],"publication_authors":["Arnold Hayer, Upinder S Bhalla"],"first_author":["Arnold Hayer"],"publication":["16110334,\n                            Changes in the synaptic connection strengths between neurons are believed to play a role in memory formation. An important mechanism for changing synaptic strength is through movement of neurotransmitter receptors and regulatory proteins to and from the synapse. Several activity-triggered biochemical events control these movements. Here we use computer models to explore how these putative memory-related changes can be stabilised long after the initial trigger, and beyond the lifetime of synaptic molecules. We base our models on published biochemical data and experiments on the activity-dependent movement of a glutamate receptor, AMPAR, and a calcium-dependent kinase, CaMKII. We find that both of these molecules participate in distinct bistable switches. These simulated switches are effective for long periods despite molecular turnover and biochemical fluctuations arising from the small numbers of molecules in the synapse. The AMPAR switch arises from a novel self-recruitment process where the presence of sufficient receptors biases the receptor movement cycle to insert still more receptors into the synapse. The CaMKII switch arises from autophosphorylation of the kinase. The switches may function in a tightly coupled manner, or relatively independently. The latter case leads to multiple stable states of the synapse. We propose that similar self-recruitment cycles may be important for maintaining levels of many molecules that undergo regulated movement, and that these may lead to combinatorial possible stable states of systems like the synapse.. 2, 1.\n                            National Centre for Biological Sciences, Bangalore, India."],"submitter_mail":["doqcs@ncbs.res.in"],"submitter_affiliation":["DOQCS"],"pubmed_abstract":["Changes in the synaptic connection strengths between neurons are believed to play a role in memory formation. An important mechanism for changing synaptic strength is through movement of neurotransmitter receptors and regulatory proteins to and from the synapse. Several activity-triggered biochemical events control these movements. Here we use computer models to explore how these putative memory-related changes can be stabilised long after the initial trigger, and beyond the lifetime of synaptic molecules. We base our models on published biochemical data and experiments on the activity-dependent movement of a glutamate receptor, AMPAR, and a calcium-dependent kinase, CaMKII. We find that both of these molecules participate in distinct bistable switches. These simulated switches are effective for long periods despite molecular turnover and biochemical fluctuations arising from the small numbers of molecules in the synapse. The AMPAR switch arises from a novel self-recruitment process where the presence of sufficient receptors biases the receptor movement cycle to insert still more receptors into the synapse. The CaMKII switch arises from autophosphorylation of the kinase. The switches may function in a tightly coupled manner, or relatively independently. The latter case leads to multiple stable states of the synapse. We propose that similar self-recruitment cycles may be important for maintaining levels of many molecules that undergo regulated movement, and that these may lead to combinatorial possible stable states of systems like the synapse."],"pubmed_title":["Molecular switches at the synapse emerge from receptor and kinase traffic."],"pubmed_authors":["Hayer Arnold A, Bhalla Upinder S US"],"additional_accession":[]},"is_claimable":false,"name":"Hayer2005_CaMKII_model3","description":"\n      \n    This is the complete model of CaMKII bistability, model 3. It exhibits bistability in CaMKII activation due to autophosphorylation at the PSD and local saturation of PP1. This version of model 3 includes PKA regulatory input. This has little effect on the deterministic calculations, but the PKA pathway introduces a lot of noise which causes a difference in stochastic runs.    This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.      \n          To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to      CC0 Public Domain Dedication\n          for more information.      \n    In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..      \n    \n          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.\n\n\n","dates":{"last_modification":"2011-07-04","publication":"2005-01-01","submission":"2008-03-13"},"accession":"MODEL9086953089","cross_references":{"pubmed":["16110334"],"biomodels__db":["MODEL9086953089"],"go":["GO:2000311"],"taxonomy":["40674"],"bto":["BTO:0000938"]}}