Kholodenko1999 - EGFR signaling
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ABSTRACT:
Kholodenko1999 - EGFR signaling
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To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based
modelling using JWS Online
, Bioinformatics, 20:2143-2144
This model is described in the article:
Quantification of short term signaling by the epidermal growth factor receptor.
Kholodenko BN, Demin OV, Moehren G, Hoek JB
J. Biol. Chem. 1999 Oct; 274(42): 30169-30181
Abstract:
During the past decade, our knowledge of molecular mechanisms involved in growth factor signaling has proliferated almost explosively. However, the kinetics and control of information transfer through signaling networks remain poorly understood. This paper combines experimental kinetic analysis and computational modeling of the short term pattern of cellular responses to epidermal growth factor (EGF) in isolated hepatocytes. The experimental data show transient tyrosine phosphorylation of the EGF receptor (EGFR) and transient or sustained response patterns in multiple signaling proteins targeted by EGFR. Transient responses exhibit pronounced maxima, reached within 15-30 s of EGF stimulation and followed by a decline to relatively low (quasi-steady-state) levels. In contrast to earlier suggestions, we demonstrate that the experimentally observed transients can be accounted for without requiring receptor-mediated activation of specific tyrosine phosphatases, following EGF stimulation. The kinetic model predicts how the cellular response is controlled by the relative levels and activity states of signaling proteins and under what conditions activation patterns are transient or sustained. EGFR signaling patterns appear to be robust with respect to variations in many elemental rate constants within the range of experimentally measured values. On the other hand, we specify which changes in the kinetic scheme, rate constants, and total amounts of molecular factors involved are incompatible with the experimentally observed kinetics of signal transfer. Quantitation of signaling network responses to growth factors allows us to assess how cells process information controlling their growth and differentiation.
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SUBMITTER: Lu Li
PROVIDER: BIOMD0000000048 | BioModels | 2024-09-02
REPOSITORIES: BioModels
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