2021-05-18T12:55:09Z
https://digital.csic.es/dspace-oai/request
oai:digital.csic.es:10261/55227
2020-05-29T11:01:50Z
com_10261_47
com_10261_8
col_10261_300
http://hdl.handle.net/10261/55227
http://dx.doi.org/10.1049/iet-syb:20070069
57488
Computational procedures for optimal experimental design in biological systems
Institution of Engineering and Technology
2008
Balsa-Canto, Eva
rp13652
Alonso, Antonio A.
Banga, Julio R.
2008
10 páginas
Mathematical 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
Systems Biology
2008
2
163
172