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Título : Hybrid optimization method with general switching strategy for parameter estimation
Autor : Balsa-Canto, Eva ; Peifer, Martin; Banga, Julio R. ; Timmer, Jens; Fleck, Christian
Fecha de publicación : 24-mar-2008
Editor: BioMed Central
Citación : BMC Systems Biology 2:26 (2008)
Resumen: [Background] 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.
[Results] 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.
[Conclusion] 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.
Descripción : This article is available from: http://www.biomedcentral.com/1752-0509/2/26
Versión del editor: http://dx.doi.org/10.1186/1752-0509-2-26
URI : http://hdl.handle.net/10261/3495
DOI: 10.1186/1752-0509-2-26
ISSN: 1752-0509
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