Title: Parameter discovery in stochastic biological models using simulated annealing and statistical model checking
Authors: Faraz Hussain; Sumit K. Jha; Susmit Jha; Christopher J. Langmead
Addresses: Computer Science Department, University of Central Florida, Orlando, FL 32816, USA ' Computer Science Department, University of Central Florida, Orlando, FL 32816, USA ' Intel Strategic CAD Labs, Portland, OR 9712, USA ' Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Abstract: 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: parameter discovery; biochemical systems; computational systems biology; behavioural specifications; statistical hypothesis testing; parameter synthesis; probabilistic verification; SPRT; temporal logic; statistical model checking; stochastic modelling; machine learning; bioinformatics; CUDA; glucose-insulin model; biomedical devices; cyber-physical systems; CPS; simulated annealing; artificial pancreata.
DOI: 10.1504/IJBRA.2014.062998
International Journal of Bioinformatics Research and Applications, 2014 Vol.10 No.4/5, pp.519 - 539
Published online: 24 Oct 2014 *
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