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Diverse Cell Stimulation Kinetics Identify Predictive Signal Transduction Models

Abstract : Computationally understanding the molecular mechanisms that give rise to cellsignaling responses upon different environmental, chemical, and genetic pertur-bations is a long-standing challenge that requires models that fit and predictquantitative responses for new biological conditions. Overcoming this challengedepends not only on good models and detailed experimental data but also on therigorous integration of both. We propose a quantitative framework to perturband model generic signaling networks using multiple and diverse changing envi-ronments (hereafter ‘‘kinetic stimulations’’) resulting in distinct pathway activa-tion dynamics. We demonstrate that utilizing multiple diverse kinetic stimulationsbetter constrains model parameters and enables predictions of signaling dy-namics that would be impossible using traditional dose-response or individual ki-netic stimulations. To demonstrate our approach, we use experimentally identi-fied models to predict signaling dynamics in normal, mutated, and drug-treatedconditions upon multitudes of kinetic stimulations and quantify which proteinsand reaction rates are most sensitive to which extracellular stimulations.
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Submitted on : Thursday, March 18, 2021 - 3:46:54 PM
Last modification on : Monday, April 11, 2022 - 4:54:02 PM
Long-term archiving on: : Monday, June 21, 2021 - 8:54:09 AM


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Hossein Jashnsaz, Zachary Fox, Jason Hughes, Guoliang Li, Brian Munsky, et al.. Diverse Cell Stimulation Kinetics Identify Predictive Signal Transduction Models. iScience, Elsevier, 2020, 23 (10), pp.101565. ⟨10.1016/j.isci.2020.101565⟩. ⟨hal-03155416⟩



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