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http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3495| Title: | Hybrid optimization method with general switching strategy for parameter estimation |
| Publisher: | BioMed Central |
| Description: | This article is available from: http://www.biomedcentral.com/1752-0509/2/26 [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. This work was supported by the European Community as part of the FP6 COSBICS Project (STREP FP6-512060), the German Federal Ministry of Education and Research, BMBF-project FRISYS (grant 0313921) and Xunta de Galicia (PGIDIT05PXIC40201PM). Peer reviewed |
| URI: | http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3495 |
| Other Identifiers: | BMC Systems Biology 2:26 (2008) 1752-0509 http://hdl.handle.net/10261/3495 10.1186/1752-0509-2-26 |
| Appears in Collections: | Digital Csic |
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