Empirical Study of Computational Intelligence Strategies for Biochemical Systems Modelling

Modelling biochemical networks can be achieved by iteratively analyzing parts of the systems via top-down or bottom-up approaches. It is feasible to piece-wise model the biochemical networks from scratch by employing strategies able to assemble reusable components. In this paper, we investigate a set of strategies that can be employed in a bottom-up piece-wise modelling framework, to obtain synthetic models with similar behaviour to the target systems. A combination of evolution strategies and simulated annealing is employed to optimize the structure of the system and its kinetic rates. Simulation results of different variants of those computational methods on a standard signaling pathway show that it is feasible to obtain a tradeoff between the generation of desired behaviour and similar and alternative topologies.