The study of large models of biological networks by means of analysis and simulation tools leads to large amounts of predictions. This raises the question of how to identify interesting predictions of novel phenomena that can be confronted with experimental data. Formal verification techniques based on model-checking have recently been used to the analysis of these networks, providing a powerful technology to keep up with this increase in scale and complexity. The application of these techniques is hampered, however, by several key issues. First, the systems biology domain brought to the fore a few properties of the network dynamics like multistability and oscillations, that are not easily expressed using classical temporal logics. Second, the problem of posing relevant and interesting questions in temporal logic, is difficult for non-expert users. Finally, most of the existing modeling and simulation tools are not capable of applying model-checking techniques in a transparent way. The approaches developed in this work lower the obstacles to the use of formal verification in systems biology. They have been validated on the analysis and simulation of two real and complex biological models.
