Model parameter estimation and analysis: understanding parametric structure.
We developed three algorithms to facilitate an analysis of the parameter combinations (PASS points) that fit experimental data to a desired degree of accuracy. The clustering algorithm separates PASS points into clusters (PASS clusters) as a preliminary step for the following geometrical parametric analyses. The PASS region reconstruction algorithm defines the space of a PASS cluster to allow further parametric structural analysis. The feasible parameter space expansion algorithm produces a complete PASS cluster to be used for model predictions to evaluate the effects of variability and uncertainty. These algorithms are demonstrated using two pharmacokinetic models; a single compartment model for procainamide and a three-compartment physiologically based model for benzene. We found a more thorough representation of the parameter space than previously considered. Thus, we obtained model predictions that describe better the variability in population responses. In addition, we also parametrically identified a subpopulation that may have a higher risk for cancer.
Li
H
H
Department of Mechanical Engineering, University of California, Berkeley 94720.
Watanabe
K
K
Auslander
D
D
Spear
R C
RC
D000465:Algorithms; D001554:Benzene / Q000009:adverse effects / Q000493:pharmacokinetics; D015982:Bias; D001822:Body Burden; D016000:Cluster Analysis; D004784:Environmental Monitoring; D062665:Epidemiological Monitoring; D005069:Evaluation Studies as Topic; D006801:Humans; D008956:Models, Chemical; D009010:Monte Carlo Method; D009369:Neoplasms / Q000139:chemically induced / Q000453:epidemiology; D011237:Predictive Value of Tests; D011342:Procainamide / Q000097:blood / Q000493:pharmacokinetics; D012307:Risk Factors; D014018:Tissue Distribution
D001554:Benzene; D011342:Procainamide