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Improved Parameter Estimation for Completely Observed Ordinary Differential Equations with Application to Biological Systems

  • Peter Gennemark
  • Dag Wedelin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5688)

Abstract

We consider parameter estimation in ordinary differential equations (ODEs) from completely observed systems, and describe an improved version of our previously reported heuristic algorithm (IET Syst. Biol., 2007). Basically, in that method, estimation based on decomposing the problem to simulation of one ODE, is followed by estimation based on simulation of all ODEs of the system.

The main algorithmic improvement compared to the original version, is that we decompose not only to single ODEs, but also to arbitrary subsets of ODEs, as a complementary intermediate step. The subsets are selected based on an analysis of the interaction between the variables and possible common parameters.

We evaluate our algorithm on a number of well-known hard test problems from the biological literature. The results show that our approach is more accurate and considerably faster compared to other reported methods on these problems. Additionally, we find that the algorithm scales favourably with problem size.

Keywords

ordinary differential equations parameter estimation decomposition 

Supplementary material

All problems solutions on-line software and supplementary information are available at www.odeidentification.org 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Peter Gennemark
    • 1
    • 2
  • Dag Wedelin
    • 3
  1. 1.University of GöteborgGöteborgSweden
  2. 2.Uppsala UniversityUppsalaSweden
  3. 3.Chalmers University of TechnologyGöteborgSweden

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