Visualization of the simulation data of biochemical network models: a painted Petri net approach

The large quantity of data generated by the simulation of complex biochemical models is difficult to interpret with traditional plots of time series data. A visualization method has been developed and software tools have been implemented. They are efficient with models of metabolic networks in which molecules are produced, consumed and degraded. However, models of signaling pathways in which enzymes are either activated or deactivated to transmit a signal are not handled well with the existing method. In this paper, we present a new visualization method using the painted feature of Petri nets and their invariant properties. We apply the first steps of this method to the continuous Petri net model of the calmodulin pathway. We also present the methodology that we will use to compare the existing visualization method with our Petri net-based method and to verify if it is more suited for signaling pathways.