As a starting point, we use logic-based models to represent the systems of interest to us. They are based on qualitative observations which are often more abundantly available than the quantitative data needed for constructing well-supported differential equation models. Analysis of such models can draw on a variety of mathematical methods, e.g., iteration and graph theory as well as methods from formal verification. This allows for comprehensive analysis of complex systems, often uncovering crucial structural and dynamical characteristics. It furthermore makes it possible to analyze multi-cell models as well as to test large numbers of smaller models for consistency with the available data, the latter addressing the parameter identification problem and allowing to evaluate analysis results with respect to data uncertainty. Our main application here is in the context of oncogenic signaling networks where we investigate, e.g., the influence of crosstalk on drug effects together with colleagues from Charité Berlin.