English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONS
  This item is discarded!Release HistoryDetailsSummary

Discarded

Conference Paper

Model discrimination and parameter estimation via infeasibility certificates for dynamical biochemical reaction networks

MPS-Authors
/persons/resource/persons86153

Borchers,  S.
Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
International Max Planck Research School (IMPRS), Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

/persons/resource/persons125753

Rumschinski,  Philipp
Otto-von-Guericke-Universität Magdeburg, External Organizations;
International Max Planck Research School (IMPRS), Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

(No access)

Supplementary Material (public)
There is no public supplementary material available
Citation

Borchers, S., Rumschinski, P., Bosio, S., Weismantel, R., & Findeisen, R. (2009). Model discrimination and parameter estimation via infeasibility certificates for dynamical biochemical reaction networks. In 15th IFAC Symposium on System Identification 2009 (pp. 245-250).


Abstract
Current approaches to parameter estimation and model invalidation are often inappropriate for biochemical reaction networks. This is because often only noisy measurements and sparse experimental data is available, and since they do not take the special structure of biochemical reaction networks into account. In this work a new method to prove model invalidity and to estimate parameters is introduced. It is based on a certificate of non-existence of feasible parameterizations for a given models. This is done by reformulating the model invalidation task into a set-based feasibility problem. As shown, due to the polynomial structure of many biochemical reaction systems, it is possible to relax the non-convex feasibility problem into a semidefinite program and thus to obtain conclusive results on model invalidity and parameter estimation. Our framework allows us to consider the arising difficulties posed by biochemical reaction networks by taking the specific structure of the dynamics and model outputs into account. It also enables us to discard large parameter regions as infeasible. We also show on a well-known biological example, namely the Michaelis-Menten and the Henri kinetics, how with this method it is possible to discriminate between model hypotheses and how to estimate parameters.