Colored Petri Nets-Based Biological Network Reconstruction for Systems Biology

The reconstruction of biological networks from experimental data is one of the most important challenges in systems biology. Currently, a network reconstruction algorithm for automatically generating possible Petri net models for a set of experimental data has been given; however, although it is interesting and helpful, it usually results in a number of possible models for a set of experimental data, and the resulted Petri net models are difficult to manage. In order to offer a compact representation of network reconstruction results, in this paper we propose to use colored Petri nets to model all possible networks for a set of experimental data. Specifically, we present a colored Petri nets-based network reconstruction algorithm and implement it in our modeling tool Snoopy. We also give an application of our algorithm by taking the sensory control of sporulation in Physarum polycephalum as an example.