A Bayesian approach to model checking biological systems

Recently, there has been considerable interest in the
use of Model Checking for Systems Biology.
Unfortunately, the state space of stochastic biological
models is often too large for classical Model Checking
techniques. For these models, a statistical approach to
Model Checking has been shown to be an effective
alternative. Extending our earlier work, we present the
first algorithm for performing statistical Model
Checking using Bayesian Sequential Hypothesis Testing.
We show that our Bayesian approach outperforms current
statistical Model Checking techniques, which rely on
tests from Classical (aka Frequentist) statistics, by
requiring fewer system simulations. Another advantage
of our approach is the ability to incorporate prior
Biological knowledge about the model being verified. We
demonstrate our algorithm on a variety of models from
the Systems Biology literature and show that it enables
faster verification than state-of-the-art techniques,
even when no prior knowledge is available.

@inproceedings{DBLP:conf/cmsb/JhaCLLPZ09,
	pdf = {pub/bayesmcbio.pdf},
	TR = {DBLP:conf/cmsb/JhaCLLPZ09:TR},
	author = {['Sumit Kumar Jha', 'Edmund Clarke', 'Christopher Langmead', 'Axel Legay', 'André Platzer', 'Paolo Zuliani']},
	title = {A Bayesian Approach to Model Checking
               Biological Systems},
	booktitle = {CMSB},
	year = {2009},
	pages = {218-234},
	editor = {['Pierpaolo Degano', 'Roberto Gorrieri']},
	longbooktitle = {Computational Methods in Systems
               Biology, 7th International Conference, CMSB
               2009, Bologna, Italy, Proceedings},
	publisher = {Springer},
	series = {LNCS},
	volume = {5688},
	doi = {10.1007/978-3-642-03845-7_15},
	abstract = {
    Recently, there has been considerable interest in the
    use of Model Checking for Systems Biology.
    Unfortunately, the state space of stochastic biological
    models is often too large for classical Model Checking
    techniques. For these models, a statistical approach to
    Model Checking has been shown to be an effective
    alternative. Extending our earlier work, we present the
    first algorithm for performing statistical Model
    Checking using Bayesian Sequential Hypothesis Testing.
    We show that our Bayesian approach outperforms current
    statistical Model Checking techniques, which rely on
    tests from Classical (aka Frequentist) statistics, by
    requiring fewer system simulations. Another advantage
    of our approach is the ability to incorporate prior
    Biological knowledge about the model being verified. We
    demonstrate our algorithm on a variety of models from
    the Systems Biology literature and show that it enables
    faster verification than state-of-the-art techniques,
    even when no prior knowledge is available.}
}