English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/55227
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:

Title

Computational procedures for optimal experimental design in biological systems

AuthorsBalsa-Canto, Eva ; Alonso, Antonio A. ; Banga, Julio R.
Issue Date2008
PublisherInstitution of Engineering and Technology
CitationSystems Biology 2(4): 163-172 (2008)
AbstractMathematical models of complex biological systems, such as metabolic or cell-signalling pathways, usually consist of sets of nonlinear ordinary differential equations which depend on several non-measurable parameters that can be hopefully estimated by fitting the model to experimental data. However, the success of this fitting is largely conditioned by the quantity and quality of data. Optimal experimental design (OED) aims to design the scheme of actuations and measurements which will result in data sets with the maximum amount and/or quality of information for the subsequent model calibration. New methods and computational procedures for OED in the context of biological systems are presented. The OED problem is formulated as a general dynamic optimisation problem where the time-dependent stimuli profiles, the location of sampling times, the duration of the experiments and the initial conditions are regarded as design variables. Its solution is approached using the control vector parameterisation method. Since the resultant nonlinear optimisation problem is in most of the cases non-convex, the use of a robust global nonlinear programming solver is proposed. For the sake of comparing among different experimental schemes, a Monte-Carlo-based identifiability analysis is then suggested. The applicability and advantages of the proposed techniques are illustrated by considering an example related to a cell-signalling pathway
Description10 páginas
Publisher version (URL)http://dx.doi.org/10.1049/iet-syb:20070069
URIhttp://hdl.handle.net/10261/55227
DOIhttp://dx.doi.org/10.1049/iet-syb:20070069
ISSN1741-2471
Appears in Collections:(IIM) Artículos
Files in This Item:
There are no files associated with this item.
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.