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Open Access Highly Accessed Open Badges Research article

Hybrid optimization method with general switching strategy for parameter estimation

Eva Balsa-Canto3*, Martin Peifer12, Julio R Banga3, Jens Timmer12 and Christian Fleck12

Author Affiliations

1 Institute of Physics, University of Freiburg, Germany

2 Freiburg Centre for Systems Biology, Germany

3 Process Engineering Group, Spanish Council for Scientific Research, IIM-CSIC, Spain

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BMC Systems Biology 2008, 2:26  doi:10.1186/1752-0509-2-26

Published: 24 March 2008



Modeling and simulation of cellular signaling and metabolic pathways as networks of biochemical reactions yields sets of non-linear ordinary differential equations. These models usually depend on several parameters and initial conditions. If these parameters are unknown, results from simulation studies can be misleading. Such a scenario can be avoided by fitting the model to experimental data before analyzing the system. This involves parameter estimation which is usually performed by minimizing a cost function which quantifies the difference between model predictions and measurements. Mathematically, this is formulated as a non-linear optimization problem which often results to be multi-modal (non-convex), rendering local optimization methods detrimental.


In this work we propose a new hybrid global method, based on the combination of an evolutionary search strategy with a local multiple-shooting approach, which offers a reliable and efficient alternative for the solution of large scale parameter estimation problems.


The presented new hybrid strategy offers two main advantages over previous approaches: First, it is equipped with a switching strategy which allows the systematic determination of the transition from the local to global search. This avoids computationally expensive tests in advance. Second, using multiple-shooting as the local search procedure reduces the multi-modality of the non-linear optimization problem significantly. Because multiple-shooting avoids possible spurious solutions in the vicinity of the global optimum it often outperforms the frequently used initial value approach (single-shooting). Thereby, the use of multiple-shooting yields an enhanced robustness of the hybrid approach.