Discrete event networks are used to study the combinatorics of incoming signals in discrete event networks. We explore the specificities of the Signal language and the associated model-checking tool-based Sigali, which manipulates ILTS: Implicit Labeled Transition Systems (which can be seen as an equational representation of an automaton). Both Signal and Sigali are developed at Irisa.
  Qualitative differential models appear to be appropriate to study genetically regulated metabolisms such as lipid metabolism.
  Qualitative differential models are also used to assess if qualitative experimental measurements about two steady states of a biological process are compatible with what is known about the interactions among the targets. The method consists in comparing qualitative data to the predictions of a qualitative behavioral regulation model, represented by a labelled oriented graph called the interaction graph. The interaction graph is interpreted qualitatively as a linear system in the sign algebra.

These approaches are embedded in a graphical analyzer tool named GARMeN : graphical analyzer tool for genetic and biological networks, generation of MatLab simulation models, analysis based on qualitative methods.