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Center for Nonlinear Studies |
Predictive mass-action kinetic models of biomolecular networks could offer value to several areas of medicine, including cancer genomics. However, there are several challenges that hinder the development of models suitable for medical applications. The advantages of the rule-based modeling approach could facilitate the development of such models. We have developed rule-based models of receptor tyrosine kinase phosphotyrosine interaction networks that were characterized by proteomic methodologies. We have used the models to demonstrate that intuitive interpretations for the cancer promoting capabilities of different receptor tyrosine kinases are inconsistent with mass-action kinetics and the available data. We have also used the models to demonstrate that promiscuity, a signaling motif commonly ignored in mass-action kinetic models, may offer unappreciated biological significance. We have also demonstrated that commonly used simplifications that ignore promiscuity can result in dramatic differences, which calls into question the use of those simplifications for predictive modeling.
Host: Bill Hlavacek