Encyclopedia of Systems Biology

2013 Edition
| Editors: Werner Dubitzky, Olaf Wolkenhauer, Kwang-Hyun Cho, Hiroki Yokota

Optimal Experiment Design, Ill-Posed Problems

  • André Bardow
Reference work entry
DOI: https://doi.org/10.1007/978-1-4419-9863-7_1229



Optimal experiment design refers to model-based methods to select experimental degrees of freedom such that the information gained from the experiment is maximized. For well-posed problems, optimal experiment design methods can focus on minimizing the variance of unknown parameters since suitable estimation methods ensure that the parameter estimates are unbiased. Ill-posed problems require the incorporation of a priori knowledge to regularize the solution which leads to stable but biased estimates. Optimal experimental design methods for ill-posed problems therefore have to incorporate the bias-variance trade-off in their objective function.


Ill-Posed Problems

A major goal of systems biology is the construction of predictive models from the combination of experimental techniques and computational methods. Typical inverse problems that arise in model building of biological systems are, e.g., the construction...

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© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  1. 1.Institute of Technical ThermodynamicsRWTH Aachen UniversityAachenGermany