Title

Parameter Discovery In Stochastic Biological Models Using Simulated Annealing And Statistical Model Checking

Keywords

Behavioural specifications; Biochemical systems; Bioinformatics; Biomedical devices; Computational systems biology; CPS; CUDA; Cyber-physical systems; Glucose-insulin model; Machine learning; Parameter discovery; Parameter synthesis; Probabilistic verification; SPRT; Statistical hypothesis testing; Statistical model checking; Stochastic models; Temporal logic

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

1-1-2014

Publication Title

International Journal of Bioinformatics Research and Applications

Volume

10

Issue

4-5

Number of Pages

519-539

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1504/IJBRA.2014.062998

Socpus ID

84903943470 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84903943470

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