Mathematical models of signal transduction pathways are characterized by a large number of proteins and uncertain parameters. One challenge involving these models is parameter identifiability as only a limited amount of quantitative data is generally available. One potential solution to this problem is model simplification, as the parts of the model that cannot be identified in experiments can be reduced. It is the main goal of the presented work to derive a model simplification procedure for signal transduction pathways such that: 1) the model size is significantly reduced such that the model can be validated using available experimental data, and 2) the physical interpretation of the remaining states and parameters is retained. The presented technique is used to derive a simplified version of an IL-6 signal transduction model. The number of equations and parameters in the model has been reduced from 68 to 13 and from 118 to 19, respectively. It is shown that the identifiability of the model has improved significantly. The new model is able to adequately predict the dynamic behavior of key proteins of the signal transduction pathway both in simulations but also when compared to available experimental data.
Proceedings of the 2010 American Control Conference, Baltimore, Maryland, pp. 5131-5136 (2010)