Combining Probabilistic Dependency Models and Particle Swarm Optimization for Parameter Inference in Stochastic Biological Systems

In this work we present an efficient method to tackle the problem of parameter inference for stochastic biological models. We develop a variant of the Particle Swarm Optimization algorithm by including Probabilistic Dependency statistical models to detect the parameter dependencies. This results in a more efficient parameter inference of the biological model.We test the Probabilistic Dependency- PSO on a well-known benchmark problem: the thermal isomerization of α-pinene © 2012 Springer-Verlag GmbH.