Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.
Keywords: CPS; CUDA; SPRT; artificial pancreata; behavioural specifications; biochemical systems; bioinformatics; biomedical devices; computational systems biology; cyber–physical systems; glucose–insulin model; machine learning; parameter discovery; parameter synthesis; probabilistic verification; simulated annealing; statistical hypothesis testing; statistical model checking; stochastic modelling; temporal logic.