Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks
1 Institute for Automation Engineering, Otto-von-Guericke-Universitisät Magdeburg, Magdeburg, Germany
2 Institute for Mathematical Optimization, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany
3 Magdeburg Centre for Systems Biology (MaCS), Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany
4 International Max Planck Research School (IMPRS), Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
BMC Systems Biology 2010, 4:69 doi:10.1186/1752-0509-4-69Published: 25 May 2010
Mathematical modeling and analysis have become, for the study of biological and cellular processes, an important complement to experimental research. However, the structural and quantitative knowledge available for such processes is frequently limited, and measurements are often subject to inherent and possibly large uncertainties. This results in competing model hypotheses, whose kinetic parameters may not be experimentally determinable. Discriminating among these alternatives and estimating their kinetic parameters is crucial to improve the understanding of the considered process, and to benefit from the analytical tools at hand.
In this work we present a set-based framework that allows to discriminate between competing model hypotheses and to provide guaranteed outer estimates on the model parameters that are consistent with the (possibly sparse and uncertain) experimental measurements. This is obtained by means of exact proofs of model invalidity that exploit the polynomial/rational structure of biochemical reaction networks, and by making use of an efficient strategy to balance solution accuracy and computational effort.
The practicability of our approach is illustrated with two case studies. The first study shows that our approach allows to conclusively rule out wrong model hypotheses. The second study focuses on parameter estimation, and shows that the proposed method allows to evaluate the global influence of measurement sparsity, uncertainty, and prior knowledge on the parameter estimates. This can help in designing further experiments leading to improved parameter estimates.