![]() ![]() The Bayesian method performed better than the method which ignored the parameter variability, producing a lumping scheme which, while not optimal for any parameter value, was optimal on average. An arbitrary variability of 20% CV was added to the nominal reported parameter values. We applied the methodology to a PBPK model for barbiturates taken from the literature. The method builds on our previously published algorithm for lumping that works stepwise, reducing the system’s dimension by one at each step and where each successive step is conditional to the previous ones. Applications of this method may include PBPK models for the drug distribution and/or Systems Biology models for the drug action. With the present method we address this problem by incorporating a prior parameter distribution in the determination of the optimal lumping scheme in a Bayesian manner. Model reduction is a potentially useful tool to simplify large systems but suffers from lack of robustness over the model parameter values. We present a Bayesian automated method to reduce by lumping, a large system described by differential equations which takes into account parameter variability.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |