TY - JOUR
T1 - Computational modeling of the EGF-receptor system
T2 - A paradigm for systems biology
AU - Wiley, H. Steven
AU - Shvartsman, Stanislav Y.
AU - Lauffenburger, Douglas A.
PY - 2003/1/1
Y1 - 2003/1/1
N2 - Computational models have rarely been used as tools by biologists but, when models provide experimentally testable predictions, they can be extremely useful. The epidermal growth factor receptor (EGFR) is probably the best-understood receptor system, and computational models have played a significant part in its elucidation. For many years, models have been used to analyze EGFR dynamics and to interpret mutational studies, and are now being used to understand processes including signal transduction, autocrine loops and developmental patterning. The success of EGFR modeling can be a guide to combining models and experiments productively to understand complex biological processes as integrated systems.
AB - Computational models have rarely been used as tools by biologists but, when models provide experimentally testable predictions, they can be extremely useful. The epidermal growth factor receptor (EGFR) is probably the best-understood receptor system, and computational models have played a significant part in its elucidation. For many years, models have been used to analyze EGFR dynamics and to interpret mutational studies, and are now being used to understand processes including signal transduction, autocrine loops and developmental patterning. The success of EGFR modeling can be a guide to combining models and experiments productively to understand complex biological processes as integrated systems.
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U2 - 10.1016/S0962-8924(02)00009-0
DO - 10.1016/S0962-8924(02)00009-0
M3 - Review article
C2 - 12480339
AN - SCOPUS:0037213006
VL - 13
SP - 43
EP - 50
JO - Trends in Cell Biology
JF - Trends in Cell Biology
SN - 0962-8924
IS - 1
ER -