{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/MODEL9071773985?filename=MODEL9071773985.pdf"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/MODEL9071773985?filename=MODEL9071773985-biopax3.owl","https://www.ebi.ac.uk/biomodels/model/download/MODEL9071773985?filename=MODEL9071773985-biopax2.owl"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/MODEL9071773985?filename=MODEL9071773985.svg"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/MODEL9071773985?filename=MODEL9071773985_urn.xml","https://www.ebi.ac.uk/biomodels/model/download/MODEL9071773985?filename=MODEL9071773985_url.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/MODEL9071773985?filename=MODEL9071773985.sci","https://www.ebi.ac.uk/biomodels/model/download/MODEL9071773985?filename=MODEL9071773985.png","https://www.ebi.ac.uk/biomodels/model/download/MODEL9071773985?filename=MODEL9071773985.xpp","https://www.ebi.ac.uk/biomodels/model/download/MODEL9071773985?filename=MODEL9071773985.m","https://www.ebi.ac.uk/biomodels/model/download/MODEL9071773985?filename=MODEL9071773985.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/MODEL9071773985"],"publication_pubmed":["12779455"],"isPrivate":["false"],"repository":["BioModels"],"modelFormat":["SBML"],"omics_type":["Models"],"tokenised_name":["Bhalla2001 MAPK MKP1 oscillation"],"publication_year":["2001"],"submissionId":["MODEL9071773985"],"publication_authors":["Upinder S Bhalla, Ravi Iyengar"],"first_author":["Upinder S Bhalla"],"publication":["12779455,\n                            Biological signaling networks comprised of cellular components including signaling proteins and small molecule messengers control the many cell function in responses to various extracellular and intracellular signals including hormone and neurotransmitter inputs, and genetic events. Many signaling pathways have motifs familiar to electronics and control theory design. Feedback loops are among the most common of these. Using experimentally derived parameters, we modeled a positive feedback loop in signaling pathways used by growth factors to trigger cell proliferation. This feedback loop is bistable under physiological conditions, although the system can move to a monostable state as well. We find that bistability persists under a wide range of regulatory conditions, even when core enzymes in the feedback loop deviate from physiological values. We did not observe any other phenomena in the core feedback loop, but the addition of a delayed inhibitory feedback was able to generate oscillations under rather extreme parameter conditions. Such oscillations may not be of physiological relevance. We propose that the kinetic properties of this feedback loop have evolved to support bistability and flexibility in going between bistable and monostable modes, while simultaneously being very refractory to oscillatory states. (c) 2001 American Institute of Physics.. 1, 11.\n                            National Centre for Biological Sciences, Bangalore 560065, India."],"submitter_mail":["doqcs@ncbs.res.in"],"submitter_affiliation":["DOQCS"],"pubmed_abstract":["Biological signaling networks comprised of cellular components including signaling proteins and small molecule messengers control the many cell function in responses to various extracellular and intracellular signals including hormone and neurotransmitter inputs, and genetic events. Many signaling pathways have motifs familiar to electronics and control theory design. Feedback loops are among the most common of these. Using experimentally derived parameters, we modeled a positive feedback loop in signaling pathways used by growth factors to trigger cell proliferation. This feedback loop is bistable under physiological conditions, although the system can move to a monostable state as well. We find that bistability persists under a wide range of regulatory conditions, even when core enzymes in the feedback loop deviate from physiological values. We did not observe any other phenomena in the core feedback loop, but the addition of a delayed inhibitory feedback was able to generate oscillations under rather extreme parameter conditions. Such oscillations may not be of physiological relevance. We propose that the kinetic properties of this feedback loop have evolved to support bistability and flexibility in going between bistable and monostable modes, while simultaneously being very refractory to oscillatory states. (c) 2001 American Institute of Physics."],"pubmed_title":["Robustness of the bistable behavior of a biological signaling feedback loop."],"pubmed_authors":["Bhalla Upinder S. US, Iyengar Ravi R"],"additional_accession":[]},"is_claimable":false,"name":"Bhalla2001_MAPK_MKP1_oscillation","description":"\n      \n    This model relates to figure 5 in Bhalla US, Iyengar R. Chaos (2001) 11(1):221-226. It includes the model used for figures 2-4 and also has MKP-1 induction by MAPK activity in the synapse. PP2A is set to 0.16 uM and MKP synthesis is varied from 5x to 40 x basal to get a range of interesting behaviours.    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":"MODEL9071773985","cross_references":{"pubmed":["12779455"],"biomodels__db":["MODEL9071773985"],"go":["GO:0000165"],"taxonomy":["9606"]}}