Automatic network reconstruction from experimental time-series data: A survey

Abstract

Abstract The reconstruction of models from experimental data is a challenging problem due to the inherited complexity of the studied biological systems. We discuss an exact, exclusively data-driven approach to reconstruct Petri nets, a framework which turned out to coherently model static interactions and dynamic processes. The reconstructed models shall reproduce the experimentally observed dynamic behavior in a simulation. For that, we consider Petri nets with two types of extensions, priority relations among the transitions and control-arcs, to obtain additional activation rules that control the dynamic behavior. Here, we give an overview on results concerning the reconstruction of standard networks with and without priorities, extended Petri nets, and extended Petri nets with priorities. All these approaches aim at reconstructing all networks of the studied type that fit the given experimental data, to provide all possible alternatives of mechanisms behind the experimentally observed phenomena.