Parameter uncertainty in biochemical models described by ordinary differential equations

• Published in: Mathematical biosciences 2013-12, Vol.246 (2), p.305-314
• Main Authors: Vanlier, J., Tiemann, C.A., Hilbers, P.A.J., van Riel, N.A.W.
• Format: Article
• Language: English
• Subjects: Bayesian; Computer Simulation; Dynamical systems; Inference; Models, Biological; Ordinary differential equations; Parameter estimation; Systems Biology - methods; Uncertainty analysis
• ISSN: 0025-5564; 1879-3134
• DOI: 10.1016/j.mbs.2013.03.006

•We provide an introduction to various methods for uncertainty analysis.•We list some of the issues in Systems Biology modeling.•We provide an example where differences between the different approaches occur.•We show how different approaches can complement each other Improved mechanistic understanding of biochemical networks is one of the driving ambitions of Systems Biology. Computational modeling allows the integration of various sources of experimental data in order to put this conceptual understanding to the test in a quantitative manner. The aim of computational modeling is to obtain both predictive as well as explanatory models for complex phenomena, hereby providing useful approximations of reality with varying levels of detail. As the complexity required to describe different system increases, so does the need for determining how well such predictions can be made. Despite efforts to make tools for uncertainty analysis available to the field, these methods have not yet found widespread use in the field of Systems Biology. Additionally, the suitability of the different methods strongly depends on the problem and system under investigation. This review provides an introduction to some of the techniques available as well as gives an overview of the state-of-the-art methods for parameter uncertainty analysis.