This work proposes a fuzzy modeling-based approach for describing signal transduction networks. Many key steps in signal transduction mechanisms have been investigated qualitatively in the literature, however, only little quantitative information is available. Fuzzy models can make use of this situation as fuzzy rules can be based upon the qualitative information that is found in the literature whereas training of the model can be performed with data that is available. This combination of a fuzzy rule set based upon qualitative information with parameters to be determined from data can result in models where fewer parameters need to be estimated than if fundamental or black-box models were used. This work investigates the use of fuzzy modeling to describe an IL-6 signal transduction mechanism as it plays a key role in the body's response to inflammation. The resulting model is capable of capturing the dynamics of key components of the IL-6 signal transduction pathway.
Proceedings of the 2008 IFAC World Congress, Seoul, Korea, pp. 15867-15872 (2008) Invited Presentation