Please use this identifier to cite or link to this item:
http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3495Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.creator | Balsa-Canto, Eva | - |
| dc.creator | Peifer, Martin | - |
| dc.creator | Banga, Julio R. | - |
| dc.creator | Timmer, Jens | - |
| dc.creator | Fleck, Christian | - |
| dc.date | 2008-04-10T07:06:07Z | - |
| dc.date | 2008-04-10T07:06:07Z | - |
| dc.date | 2008-03-24 | - |
| dc.date.accessioned | 2017-01-31T01:01:40Z | - |
| dc.date.available | 2017-01-31T01:01:40Z | - |
| dc.identifier | BMC Systems Biology 2:26 (2008) | - |
| dc.identifier | 1752-0509 | - |
| dc.identifier | http://hdl.handle.net/10261/3495 | - |
| dc.identifier | 10.1186/1752-0509-2-26 | - |
| dc.identifier.uri | http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3495 | - |
| dc.description | This article is available from: http://www.biomedcentral.com/1752-0509/2/26 | - |
| dc.description | [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. | - |
| dc.description | [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. | - |
| dc.description | [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. | - |
| dc.description | 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). | - |
| dc.description | Peer reviewed | - |
| dc.format | 326865 bytes | - |
| dc.format | application/pdf | - |
| dc.language | eng | - |
| dc.publisher | BioMed Central | - |
| dc.relation | Publisher’s version | - |
| dc.relation | http://dx.doi.org/10.1186/1752-0509-2-26 | - |
| dc.rights | openAccess | - |
| dc.title | Hybrid optimization method with general switching strategy for parameter estimation | - |
| dc.type | Artículo | - |
| Appears in Collections: | Digital Csic | |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.