{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/MODEL9089914876?filename=MODEL9089914876.pdf"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/MODEL9089914876?filename=MODEL9089914876-biopax3.owl","https://www.ebi.ac.uk/biomodels/model/download/MODEL9089914876?filename=MODEL9089914876-biopax2.owl"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/MODEL9089914876?filename=MODEL9089914876.svg"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/MODEL9089914876?filename=MODEL9089914876_urn.xml","https://www.ebi.ac.uk/biomodels/model/download/MODEL9089914876?filename=MODEL9089914876_url.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/MODEL9089914876?filename=MODEL9089914876.xpp","https://www.ebi.ac.uk/biomodels/model/download/MODEL9089914876?filename=MODEL9089914876.sci","https://www.ebi.ac.uk/biomodels/model/download/MODEL9089914876?filename=MODEL9089914876.png","https://www.ebi.ac.uk/biomodels/model/download/MODEL9089914876?filename=MODEL9089914876.vcml","https://www.ebi.ac.uk/biomodels/model/download/MODEL9089914876?filename=MODEL9089914876.m"]},"type":"primary"},"statusCodeValue":200,"statusCode":"OK"}],"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/MODEL9089914876"],"publication_pubmed":["15548210"],"isPrivate":["false"],"repository":["BioModels"],"modelFormat":["SBML"],"omics_type":["Models"],"tokenised_name":["Ajay Bhalla 2004 Feedback Tuning"],"publication_year":["2004"],"submissionId":["MODEL9089914876"],"publication_authors":["Sriram M Ajay, Upinder S Bhalla"],"first_author":["Sriram M Ajay"],"publication":["15548210,\n                            Stimulus reinforcement strengthens learning. Intervals between reinforcement affect both the kind of learning that occurs and the amount of learning. Stimuli spaced by a few minutes result in more effective learning than when massed together. There are several synaptic correlates of repeated stimuli, such as different kinds of plasticity and the amplitude of synaptic change. Here we study the role of signalling pathways in the synapse on this selectivity for spaced stimuli. Using the in vitro hippocampal slice technique we monitored long-term potentiation (LTP) amplitude in CA1 for repeated 100-Hz, 1-s tetani. We observe the highest LTP levels when the inter-tetanus interval is 5-10 min. We tested biochemical activity in the slice following the same stimuli, and found that extracellular signal-regulated kinase type II (ERKII) but not CaMKII exhibits a peak at about 10 min. When calcium influx into the slice is buffered using AM-ester calcium dyes, amplitude of the physiological and biochemical response is reduced, but the timing is not shifted. We have previously used computer simulations of synaptic signalling to predict such temporal tuning from signalling pathways. In the current study we consider feedback and feedforward models that exhibit temporal tuning consistent with our experiments. We find that a model incorporating post-stimulus build-up of PKM zeta acting upstream of mitogen-activated protein kinase is sufficient to explain the observed temporal tuning. On the basis of these combined experimental and modelling results we propose that the dynamics of PKM activation and ERKII signalling may provide a mechanism for functionally important forms of synaptic pattern selectivity.. 10, 20.\n                            National Centre for Biological Sciences, Tata Institute of Fundamental Research, Gandhi Krishi Vignan Kendra Campus, Bangalore 560065, India."],"submitter_mail":["doqcs@ncbs.res.in"],"submitter_affiliation":["DOQCS"],"pubmed_abstract":["Stimulus reinforcement strengthens learning. Intervals between reinforcement affect both the kind of learning that occurs and the amount of learning. Stimuli spaced by a few minutes result in more effective learning than when massed together. There are several synaptic correlates of repeated stimuli, such as different kinds of plasticity and the amplitude of synaptic change. Here we study the role of signalling pathways in the synapse on this selectivity for spaced stimuli. Using the in vitro hippocampal slice technique we monitored long-term potentiation (LTP) amplitude in CA1 for repeated 100-Hz, 1-s tetani. We observe the highest LTP levels when the inter-tetanus interval is 5-10 min. We tested biochemical activity in the slice following the same stimuli, and found that extracellular signal-regulated kinase type II (ERKII) but not CaMKII exhibits a peak at about 10 min. When calcium influx into the slice is buffered using AM-ester calcium dyes, amplitude of the physiological and biochemical response is reduced, but the timing is not shifted. We have previously used computer simulations of synaptic signalling to predict such temporal tuning from signalling pathways. In the current study we consider feedback and feedforward models that exhibit temporal tuning consistent with our experiments. We find that a model incorporating post-stimulus build-up of PKM zeta acting upstream of mitogen-activated protein kinase is sufficient to explain the observed temporal tuning. On the basis of these combined experimental and modelling results we propose that the dynamics of PKM activation and ERKII signalling may provide a mechanism for functionally important forms of synaptic pattern selectivity."],"pubmed_title":["A role for ERKII in synaptic pattern selectivity on the time-scale of minutes."],"pubmed_authors":["Ajay Sriram M SM, Bhalla Upinder S US"],"additional_accession":[]},"is_claimable":false,"name":"Ajay_Bhalla_2004_Feedback_Tuning","description":"\n      \n    This model is taken from Ajay SM, Bhalla US. Eur J Neurosci. 2004 Nov;20(10):2671-80. This is the feedback model from Figure 8a.    This model originates from BioModels Database: A Database of Annotated Published Models. 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":"2009-10-07","publication":"2005-01-01","submission":"2008-03-13"},"accession":"MODEL9089914876","cross_references":{"pubmed":["15548210"],"biomodels__db":["MODEL9089914876"],"go":["GO:0070371"],"taxonomy":["10114"],"bto":["BTO:0000601"]}}