R. Blanquero, E. Carrizosa, O. Chis, N. Esteban, M. A. Jiménez Cordero, J. F. Rodríguez, M. R. Sillero Denamiel
Model inference is a challenging problem in the analysis of chemical reactions networks. In order to empirically test which, out of a catalogue of proposed kinetic models, is governing a network of chemical reactions, the user can compare the empirical data obtained in one experiment against the theoretical values suggested by the models under consideration. It is thus fundamental to make an adequate choice of the control variables in order to have maximal separation between sets of concentrations provided by the theoretical models, making then easier to identify which of the theoretical models yields data closest to those obtained empirically under identical conditions.
In this work we illustrate how Global Optimization tools can be successfully used to address the problem of model selection. Furthermore, these approaches can be used to deal with the problem of parameters inference when there exist missing values in the data. Some examples illustrate the usefulness of our methodology.
Palabras clave: model selection, missing data, chemical reactions networks, kinetic models, global optimization, Variable Neighborhood Search
Programado
L08.1 Aplicaciones de Investigación Operativa
5 de septiembre de 2016 15:40
0.02 - Aula de proyectos 1
