Resolving crosstalk between signaling pathways using mathematical modeling and time-resolved single cell data

Item Type:	Article
Title:	Resolving crosstalk between signaling pathways using mathematical modeling and time-resolved single cell data
Creators Name:	Konrath, F. and Loewer, A. and Wolf, J.
Abstract:	Crosstalk between signaling pathways can modulate the cellular response to stimuli and is therefore an important part of signal transduction. For a comprehensive understanding of cellular responses, identifying points of interaction between the underlying molecular networks is essential. Here, we present an approach that allows the systematic prediction of such interactions by perturbing one pathway and quantifying the concomitant alterations in the response of a second pathway. As the observed alterations contain information about the crosstalk, we use an ordinary differential equation-based model to extract this information by linking altered dynamics to individual processes. Consequently, we can predict the interaction points between two pathways. As an example, we employed our approach to investigate the crosstalk between the NF-κB and p53 signaling pathway. We monitored the response of p53 to genotoxic stress using time-resolved single cell data and perturbed NF-κB signaling by inhibiting the kinase IKK2. Employing a subpopulation-based modeling approach enabled us to identify multiple interaction points that are simultaneously affected by perturbation of NF-κB signaling. Hence, our approach can be used to analyze crosstalk between two signaling pathways in a systematic manner.
Keywords:	Signaling Crosstalk, Quantitative Modeling, p53 Signaling, Genotoxic Stress, Single Cell Analysis, Live Cell Imaging, Subpopulation-Specific Modeling, IKKβ, NF-κB Signaling
Source:	Methods in Molecular Biology
Series Name:	Methods in Molecular Biology
Title of Book:	Computational modeling of signaling networks
ISSN:	1064-3745
ISBN:	978-1-0716-3007-5
Publisher:	Springer / Humana Press
Volume:	2634
Page Range:	267-284
Date:	2023
Official Publication:	https://doi.org/10.1007/978-1-0716-3008-2_12
PubMed:	View item in PubMed

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