In biological systems, reactions on different time scales exist and need to be considered for the analysis of physiological reactions such as proliferation. In this contribution we describe a two-level approach to decode biological reactions on a short time-scale, e. g. signaling, into long term cellular responses, e. g. proliferation. First, we derive a valid and parametrized dynamic model for signaling events on the short time-scale using set-based estimation methods allowing to take uncertainties into account. Second, the derived model candidate for early signaling events is fused with a model for long term proliferation using shape-based signaling properties. This approach is realized in the specific case study of Interleukin-6-induced signaling and proliferation. Our modeling approach enables us to consider both, dynamic early signaling events and static long term events. Furthermore, it allows a deeper understanding of how cells process information from early signaling events to long term cellular responses.
