GPU-Powered Multi-Swarm Parameter Estimation of Biological Systems: A Master-Slave Approach

In silico investigation of biological systems requires the knowledge of numerical parameters that cannot be easily measured in laboratory experiments, leading to the Parameter Estimation (PE) problem, in which the unknown parameters are automatically inferred by means of optimization algorithms exploiting the available experimental data. Here we present MS 2 PSO, an efficient parallel and distributed implementation of a PE method based on Particle Swarm Optimization (PSO) for the estimation of reaction constants in mathematical models of biological systems, considering as target for the estimation a set of discrete-time measurements of molecular species amounts. In particular, such PE method accounts for the availability of experimental data typically measured under different experimental conditions, by considering a multi-swarm PSO in which the best particles of the swarms can migrate. This strategy allows to infer a common set of reaction constants that simultaneously fits all target data used in the PE. To the aim of efficiently tackling the PE problem, MS 2 PSO embeds the execution of cupSODA, a deterministic simulator that relies on Graphics Processing Units to achieve a massive parallelization of the simulations required in the fitness evaluation of particles. In addition, a further level of parallelism is realized by exploiting the Master-Slave distributed programming paradigm. We apply MS 2 PSO for the PE of synthetic biochemical models with 10, 20 and 30 parameters to be estimated, and compare the performances obtained with different GPUs and different configurations (i.e., numbers of processes) of the Master-Slave