Reboot strategies in particle swarm optimization and their impact on parameter estimation of biochemical systems

Computational methods adopted in the field of Systems Biology require the complete knowledge of reaction kinetic constants to perform simulations of the dynamics and understand the emergent behavior of biochemical systems. However, kinetic parameters of biochemical reactions are often difficult or impossible to measure, thus they are generally inferred from experimental data, in a process known as Parameter Estimation (PE). We consider here a PE methodology that exploits Particle Swarm Optimization (PSO) to estimate an appropriate kinetic parameterization, by comparing experimental time-series target data with in silica dynamics, simulated by using the parameterization encoded by each particle. In this work we present three different reboot strategies for PSO, whose aim is to reinitialize particle positions to avoid particles to get trapped in local optima, and we compare the performance of PSO coupled with the reboot strategies with respect to standard PSO in the case of the PE of two biochemical systems. Since the PE requires a huge number of simulations at each iteration, in this work we exploit a GPU-powered deterministic simulator, cupSODA, which performs in a parallel fashion all simulations and fitness evaluations. Finally, we show that the performances of our implementation scale sublinearly with respect to the swarm size, even on outdated GPUs