Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modeling

By I. Swameye, T. Müller, Jens Timmer, O. Sandra, U. Klingmüller. Proceedings of the National Academy of Sciences of the United States of America, 2003

370 citations recorded by the source corpus.

Considerable progress has been made in identifying the molecular composition of complex signaling networks controlling cell proliferation, differentiation, and survival. However, to discover general building principles and predict the dynamic behavior of signaling networks, it is necessary to develop quantitative models based on experimental observations. Here we report a mathematical model of the core module of the Janus family of kinases (JAK)–signal transducer and activator of transcription (STAT) signaling pathway based on time-resolved measurements of receptor and STAT5 phosphorylation. Applying the fitted model, we can determine the quantitative behavior of STAT5 populations not accessible to experimental measurement. By in silico investigations, we identify the parameters of nuclear shuttling as the most sensitive to perturbations and verify experimentally the model prediction that inhibition of nuclear export results in a reduced transcriptional yield. The model reveals that STAT5 undergoes rapid nucleocytoplasmic cycles, continuously coupling receptor activation and target gene transcription, thereby forming a remote sensor between nucleus and receptor. Thus, dynamic modeling of signaling pathways can promote functional understanding at the systems level.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d45be32e2d7efd647e13058accf4b321a9200508

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Citations in the corpus

Optimization and uncertainty analysis of ODE models using 2nd order adjoint sensitivity analysis (2018, methodology): “…second order forward sensitivity analysis (Vassiliadis et al., 1999) second order adjoint sensitivity analysis The FIM is related to the asymptotic covariance of maximum likelihood estimates (Swameye et al., 2003) and provides an approximation to the Hessian of the negative log-likelihood function.”
Bayesian Solution Uncertainty Quantification for Differential Equations (2013, methodology): “Analysis is based on experimental data (Swameye et al., 2003; Raue et al., 2009).”
Parameter estimation in biochemical pathways: a comparison of global optimization methods. (2003, background): “The correct solution of inverse problems plays a key role in the development of dynamic models, which, in turn, can promote functional understanding at the systems level, as shown by, for example, Swameye et al. (2003) and Cho et al. (2003) for signaling pathways.”
Lessons Learned from Quantitative Dynamical Modeling in Systems Biology (2013, background): “This allows to test hypothesis about the supposed network structure of the molecular interactions [1] and to predict systems behavior that is not accessible by experiments directly [2].”