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© Inria / Photo C. Morel
Quantitative biology: numbers and fluorescent cells. InBio team (Inria/Institut Pasteur)
Publication : iScience

Diverse Cell Stimulation Kinetics Identify Predictive Signal Transduction Models

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in iScience - 23 Oct 2020

Jashnsaz H, Fox ZR, Hughes JJ, Li G, Munsky B, Neuert G,

Link to Pubmed [PMID] – 33083733

Link to DOI – 10.1016/j.isci.2020.101565

iScience 2020 Oct; 23(10): 101565

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.

https://pubmed.ncbi.nlm.nih.gov/33083733