Reverse engineering of kinetic reaction networks by means of Cartesian Genetic Programming and Particle Swarm Optimization

The modeling of biochemical reaction networks is a fundamental but complex task in Systems Biology, which is traditionally performed exploiting human expertise and the available experimental data. Because of the general lack of knowledge on the molecular mechanisms occurring in living cells, an intense research activity focused on the development of reverse engineering methodologies is currently underway. This problem is further complicated by the fact that a proper parameterization needs to be associated to the reaction network, in order to investigate its dynamical behavior. In this work we propose a novel computational methodology for the reverse engineering of fully parameterized kinetic networks, based on the combined use of two evolutionary programming techniques: Cartesian Genetic Programming (CGP) and Particle Swarm Optimization (PSO). In particular, CGP is used to infer the network topology, while PSO performs the parameter estimation task. To the purpose of applying our methodology in routine laboratory environments, we designed it to exploit a small set of experimental time series as target. We show that our methodology is able to reconstruct kinetic networks that perfectly fit with the target data.