This thesis addresses three challenges in modeling regulatory and signal transduction networks. Starting point is the generalized logical formalism as introduced by R. Thomas and further developed by D. Thieffry, E. H. Snoussi and M. Kaufman. We introduce the fundamental concepts that make up such models, the interaction graph and the state transition graph, as well as model checking, a computer science technique for deciding whether a finite transition system satisfies a given temporal specification. The first problem we turn to is that of whether a given model is consistent with time series data. To do so we introduce query patterns that can be automatically derived from discretized data. Time series data, being such an abundant source of information for reverse engineering, has previously been used in the context of logical models but only under the synchronous, transition-based notion of consistency. The arguably more realistic asynchronous transition relation has so far been excluded from such data driven reverse engineering, probably because the corresponding non-determinism in the transition system introduces additional obstacles to the already hard problem. Our contribution here is a path-based notion of consistency between model and data that works for any transition relation. In particular, we discuss linear time properties like monotony and branching time properties like robustness. The result are several query patterns, similar to but more complex than the ones proposed by P. T. Monteiro et al. A toolbox, called TemporalLogicTimeSeries for the automated construction of queries from data is also presented. The second problem we turn to concerns the two types of long-term behaviors that logical models are capable of producing: steady states, in which the activity levels of all network components are kept at a fixed value, and cyclic attractors in which some components are unsteady and produce sustained oscillations. We attempt to understand the emergence of these behaviors by searching for symbolic steady states as defined by H. Siebert. Our main contribution is the introduction of the prime implicant graph, which describes all minimal conditions under which components may change their activities, and an optimization-based algorithm for the enumeration of all maximal and minimal symbolic steady states. Essentially, we generalize the canalizing effects and forcing structure that were first introduced and studied by S. Kauffman and F. Fogelman in the context of random Boolean networks. The chapter includes a theorem that relates symbolic steady states to the existence of positive feedback circuits in the interaction graph. A toolbox, called BoolNetFixpoints that implements our algorithm is also described. The theme of the last chapter is how to deal with uncertainties that inevitably appear during the modeling of biological systems. One is often forced to resolve them since most types of analysis require a single, fully specified model. The knowledge gap is usually filled by making simplifications or by introducing additional assumptions that are hard to justify and therefore somewhat arbitrary. The alternative is to work with and analyze sets of alternative models, rather than single models. This idea entails additional theoretical and practical challenges: With which language should we describe our partial knowledge about a system? How can predictions be made given that each model in the set may behave differently? How can hypotheses and additional data be added to the current knowledge in a systematic manner? It seems that there are in principle two different approaches. The first one is constraint-based and studied by F. Corblin et al. It translates the partial knowledge and modeling formalism into facts and rules of a logic program. Common solvers can then deduce additional properties or test the validity of given queries across all models. In contrast, we propose to study the pros and cons of an explicit approach that enumerates all models that agree with a given partial specification. During the first step, models are enumerated and stored in a database. During a second step, models are annotated with additional information that is obtained from custom algorithms. The relationships between the annotations are then analyzed in a third step. The chapter is based on the prototype implemention LogicModelClassifier that performs the discussed steps. Throughout, we apply our results to two previously published models of biological systems. The first one is a small model of the galactose switch which regulates the transcription of genes that are involved in the metabolism of yeast. We address questions that arise during the construction of the model, for example the number of involved components and their interactions, as well as issues related to model validation and model revision with time series data. The case study also discusses different approaches to data discretization. The second one is a medium size model of the MAPK network studied by D. Thieffry et al. that is used to predict the cell fate response to different stimuli involving the growth factors EGF, TGFB, FGF and DNA damage. With the methods developed in this thesis we can prove that the model is capable of 18 different asymptotic behaviors, 12 of them steady states and 6 cyclic attractors. The question of which attractor is reached from which initial state is answered and we can show that the response in terms of proliferation or growth arrest and apoptosis is fully determined by the input stimulus.