Date of Original Version

1-1-2014

Type

Article

PubMed ID

24989866

Rights Management

Copyright 2014 Inderscience Enterprices

Abstract or Description

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.

DOI

10.1504/IJBRA.2014.062998

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Published In

International Journal of Bioinformatics Research and Applications, 10, 4-5, 519-539.