BioModelsapplication/xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL9147975215?filename=MODEL9147975215.pdfhttps://www.ebi.ac.uk/biomodels/model/download/MODEL9147975215?filename=MODEL9147975215-biopax2.owlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL9147975215?filename=MODEL9147975215-biopax3.owlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL9147975215?filename=MODEL9147975215_url.xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL9147975215?filename=MODEL9147975215_urn.xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL9147975215?filename=MODEL9147975215.vcmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL9147975215?filename=MODEL9147975215.mhttps://www.ebi.ac.uk/biomodels/model/download/MODEL9147975215?filename=MODEL9147975215.scihttps://www.ebi.ac.uk/biomodels/model/download/MODEL9147975215?filename=MODEL9147975215.pnghttps://www.ebi.ac.uk/biomodels/model/download/MODEL9147975215?filename=MODEL9147975215.xppprimaryOK200Sharat VayttadenNon-curatedordinary differential equation modelL2V1https://www.ebi.ac.uk/biomodels/MODEL914797521511312698falseBioModelsSBMLModelsAsthagiri2001 MAPK Asthagiri adapt fb2001MODEL9147975215A R Asthagiri, D A LauffenburgerA R Asthagiri11312698, Exploiting signaling pathways for the purpose of controlling cell function entails identifying and manipulating the information content of intracellular signals. As in the case of the ubiquitously expressed, eukaryotic mitogen-activated protein kinase (MAPK) signaling pathway, this information content partly resides in the signals' dynamical properties. Here, we utilize a mathematical model to examine mechanisms that govern MAPK pathway dynamics, particularly the role of putative negative feedback mechanisms in generating complete signal adaptation, a term referring to the reset of a signal to prestimulation levels. In addition to yielding adaptation of its direct target, feedback mechanisms implemented in our model also indirectly assist in the adaptation of signaling components downstream of the target under certain conditions. In fact, model predictions identify conditions yielding ultra-desensitization of signals in which complete adaptation of target and downstream signals culminates even while stimulus recognition (i.e., receptor-ligand binding) continues to increase. Moreover, the rate at which signal decays can follow first-order kinetics with respect to signal intensity, so that signal adaptation is achieved in the same amount of time regardless of signal intensity or ligand dose. All of these features are consistent with experimental findings recently obtained for the Chinese hamster ovary (CHO) cell lines (Asthagiri et al., J. Biol. Chem. 1999, 274, 27119-27127). Our model further predicts that although downstream effects are independent of whether an enzyme or adaptor protein is targeted by negative feedback, adaptor-targeted feedback can "back-propagate" effects upstream of the target, specifically resulting in increased steady-state upstream signal. Consequently, where these upstream components serve as nodes within a signaling network, feedback can transfer signaling through these nodes into alternate pathways, thereby promoting the sort of signaling cross-talk that is becoming more widely appreciated.. 2, 17. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. anand_asthagiri@hms.harvard.edudoqcs@ncbs.res.inDOQCSExploiting signaling pathways for the purpose of controlling cell function entails identifying and manipulating the information content of intracellular signals. As in the case of the ubiquitously expressed, eukaryotic mitogen-activated protein kinase (MAPK) signaling pathway, this information content partly resides in the signals' dynamical properties. Here, we utilize a mathematical model to examine mechanisms that govern MAPK pathway dynamics, particularly the role of putative negative feedback mechanisms in generating complete signal adaptation, a term referring to the reset of a signal to prestimulation levels. In addition to yielding adaptation of its direct target, feedback mechanisms implemented in our model also indirectly assist in the adaptation of signaling components downstream of the target under certain conditions. In fact, model predictions identify conditions yielding ultra-desensitization of signals in which complete adaptation of target and downstream signals culminates even while stimulus recognition (i.e., receptor-ligand binding) continues to increase. Moreover, the rate at which signal decays can follow first-order kinetics with respect to signal intensity, so that signal adaptation is achieved in the same amount of time regardless of signal intensity or ligand dose. All of these features are consistent with experimental findings recently obtained for the Chinese hamster ovary (CHO) cell lines (Asthagiri et al., J. Biol. Chem. 1999, 274, 27119-27127). Our model further predicts that although downstream effects are independent of whether an enzyme or adaptor protein is targeted by negative feedback, adaptor-targeted feedback can "back-propagate" effects upstream of the target, specifically resulting in increased steady-state upstream signal. Consequently, where these upstream components serve as nodes within a signaling network, feedback can transfer signaling through these nodes into alternate pathways, thereby promoting the sort of signaling cross-talk that is becoming more widely appreciated.A computational study of feedback effects on signal dynamics in a mitogen-activated protein kinase (MAPK) pathway model.Asthagiri A R AR, Lauffenburger D A DAfalseAsthagiri2001_MAPK_Asthagiri_adapt_fb This is a complex model to examine mechanisms that govern MAPK pathway dynamics in Chinese hamster ovary (CHO) cell lines, particularly the role of adapter targeted negative feedback mechanism in generating complete signal adaptation. This model simulates the results as per the figure 7A of the paper by Asthagiri AR and Lauffenburger DA. Biotechnol Prog. 17(2):227-39. 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. 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 for more information. 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.. 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. 2011-07-042005-01-012008-03-13MODEL914797521511312698MODEL9147975215GO:000016510029