Target driven biochemical network reconstruction based on petri nets and simulated annealing

In this paper we describe a method and an associated computational tool to modify and piecewise enlarge the topology of a biological network model, using a set of biochemical components, in order to generate one or more models whose behaviours simulate that of a target biological system. These components are defined as continuous Petri nets and stored in a library for ease of reuse. An optimization algorithm is proposed which exploits Simulated Annealing in order to alter an initial model by reference to the desired behaviour of the target model. Simulation results on a realistic illustrative example signalling pathway show that the proposed method performs well in terms of exploiting the characteristics of simulated annealing in order to generate interesting models with behaviours close to that of the target biochemical system without any pre-knowledge on the target topology itself. In future work we plan to use the generated topologies as population candidates when using an evolutionary approach to further tune the network structure and kinetic parameters.