| 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 |
|