This thesis is a contribution to the field of systems biology, where complex processes such as metabolism, gene regulation, or immune responses are formulated as mathematical representations to gain a comprehensive view. In order to create such a representation, called model, main characteristics of the system need to idealized and simplified, where different modeling formalisms require different levels of simplification. This level can be seen as a trade of between loosing details and the amount of necessary information to validate this model. Often models are built even though there is not enough information about the biological system available, which is circumvented by making assumptions. In this thesis, an alternative approach is presented, where the lack of information is included as uncertainty in the system. This uncertainty is used as constraints to create not one but every possible model that lies within these constraints giving rise to a pool of models. In our group, software for building and analyzing these model pools in form of logical models was available, thus my work focuses on the biological application of this approach. The main task was to define how biology is translated into the mathematical formalism, to identify which kind of biological questions can be addressed and to interpret the mathematical results for gaining new biological insight. These tasks were collected in a toolbox and applied to three different signaling systems that are interesting for cancer research. I investigated the effect of mutations on a signaling processes, connected two pathways with uncertain crosstalk and investigated the controversial regulation of a protein complex involved in metabolism and cancer signaling.