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

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

To perform parametric identification of mathematical models of biological events, experimental data are rare to be sufficient to estimate target behaviors produced by complex nonlinear systems. We performed parameter fitting to a cell cycle model with experimental data as an in silico experiment. We...

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Bibliographic Details
Main Authors: Noriko eHiroi, Maciej eSwat, Akira eFunahashi
Format: Article
Language:English
Published: Frontiers Media S.A. 2014-04-01
Series:Frontiers in Physiology
Subjects:
sensitivity analysis
bifurcation analysis
parametric identification
Generalized Least Squares
Fisher Information Matrix
Online Access:http://journal.frontiersin.org/Journal/10.3389/fphys.2014.00128/full

Internet

http://journal.frontiersin.org/Journal/10.3389/fphys.2014.00128/full

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