Assessing uncertainty in model parameters based on sparse and noisy experimental data.

ABSTRACT: To perform parametric identification of mathematical models of biological events, experimental data are rare to be sufficient to estimate target behaviors produced by complex non-linear systems. We performed parameter fitting to a cell cycle model with experimental data as an in silico experiment. We calibrated model parameters with the generalized least squares method with randomized initial values and checked local and global sensitivity of the model. Sensitivity analyses showed that parameter optimization induced less sensitivity except for those related to the metabolism of the transcription factors c-Myc and E2F, which are required to overcome a restriction point (R-point). We performed bifurcation analyses with the optimized parameters and found the bimodality was lost. This result s

SUBMITTER: Hiroi N 

PROVIDER: S-EPMC3983526 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature