Parameter discovery in stochastic biological models using simulated annealing and statistical model checking

Faraz Hussain, Sumit K. Jha, Susmit Jha, Christopher J. Langmead
2014 International Journal of Bioinformatics Research and Applications  
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
more » ... othesis 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.
doi:10.1504/ijbra.2014.062998 pmid:24989866 pmcid:PMC4438994 fatcat:br2druphvjea3bslzoh6ja63ky