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10.1016/j.isci.2020.101565 http://scihub22266oqcxt.onion/10.1016/j.isci.2020.101565 33083733!7549069!33083733     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       iScience 2020 ; 23 (10): 101565 Nephropedia Template TP {{tp|p=33083733|t=2020. Diverse Cell Stimulation Kinetics Identify Predictive Signal Transduction Models |pdf=|usr=}}{{33083733}} gab.com Text Diverse Cell Stimulation Kinetics Identify Predictive Signal Transduction Models JO: iScience http://www.kidney.de/pget.php?&tw=1&pmid=33083733 #FOAvip Computationally understanding the molecular mechanisms that give rise to cell signaling responses upon different environmental, chemical, and genetic perturbations is a long-standing challenge that requires models that fit and predict quantitative responses for new biological conditions. Overcoming this challenge depends not only on good models and detailed experimental data but also on the rigorous integration of both. We propose a quantitative framework to perturb and model generic signaling networks using multiple and diverse changing environments (hereafter "kinetic stimulations") resulting in distinct pathway activation dynamics. We demonstrate that utilizing multiple diverse kinetic stimulations better constrains model parameters and enables predictions of signaling dynamics that would be impossible using traditional dose-response or individual kinetic stimulations. To demonstrate our approach, we use experimentally identified models to predict signaling dynamics in normal, mutated, and drug-treated conditions upon multitudes of kinetic stimulations and quantify which proteins and reaction rates are most sensitive to which extracellular stimulations. Twit Text FOAVip Diverse Cell Stimulation Kinetics Identify Predictive Signal Transduction Models ????iScience http://www.kidney.de/pget.php?&tw=1&pmid=33083733 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33083733 #FOAvip English Wikipedia <ref>{{Cite pmid|33083733}}</ref> Diverse Cell Stimulation Kinetics Identify Predictive Signal Transduction Models #MMPMID33083733 Jashnsaz H; Fox ZR; Hughes JJ; Li G; Munsky B; Neuert G iScience 2020[Oct]; 23 (10): 101565 PMID33083733show ga Computationally understanding the molecular mechanisms that give rise to cell signaling responses upon different environmental, chemical, and genetic perturbations is a long-standing challenge that requires models that fit and predict quantitative responses for new biological conditions. Overcoming this challenge depends not only on good models and detailed experimental data but also on the rigorous integration of both. We propose a quantitative framework to perturb and model generic signaling networks using multiple and diverse changing environments (hereafter "kinetic stimulations") resulting in distinct pathway activation dynamics. We demonstrate that utilizing multiple diverse kinetic stimulations better constrains model parameters and enables predictions of signaling dynamics that would be impossible using traditional dose-response or individual kinetic stimulations. To demonstrate our approach, we use experimentally identified models to predict signaling dynamics in normal, mutated, and drug-treated conditions upon multitudes of kinetic stimulations and quantify which proteins and reaction rates are most sensitive to which extracellular stimulations. äDiverse Cell Stimulation Kinetics Identify Predictive Signal Transduct(epmc).pdf DeepDyve Pubget Overpricing