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

Hussain, F, Jha, SK, Jha, S et al. (2014). Parameter discovery in stochastic biological models using simulated annealing and statistical model checking . 10(4-5), 519-539. 10.1504/IJBRA.2014.062998

cited authors

  • Hussain, F; Jha, SK; Jha, S; Langmead, CJ

authors

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. Copyright © 2014 Inderscience Enterprises Ltd.

publication date

  • January 1, 2014

Digital Object Identifier (DOI)

start page

  • 519

end page

  • 539

volume

  • 10

issue

  • 4-5