DescriptionParameter Estimation and Uncertainty Analysis in Systems Biology
Systems biology employs mathematical modelling of biological reaction networks by systems of nonlinear differential equations. Model parameters need to be estimated using experimental data. Given the complexity of the models in combination with the limited amount of quantitative data it is important to infer how well model parameters can be determined and how uncertainty in parameters propagates into model predictions. Model identifiability and uncertainty analysis are important topics in systems biology modeling. The workshop will address different concepts and views on these topics and recent progress will be discussed. Topics will include Maximum Likelihood and Bayesian based approaches, identifiability analysis, and optimal experimental design.
|7 Jun 2012
Project: Research direct