Near-optimal experimental design for model selection in systems biology.
Bioinformatics
; 29(20): 2625-32, 2013 Oct 15.
Article
em En
| MEDLINE
| ID: mdl-23900189
ABSTRACT
MOTIVATION Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data. Motivated by biological applications, the method enables the design of crucial experiments it determines a highly informative selection of measurement readouts and time points. RESULTS:
We demonstrate formal guarantees of design efficiency on the basis of previous results. By reducing our task to the setting of graphical models, we prove that the method finds a near-optimal design selection with a polynomial number of evaluations. Moreover, the method exhibits the best polynomial-complexity constant approximation factor, unless P = NP. We measure the performance of the method in comparison with established alternatives, such as ensemble non-centrality, on example models of different complexity. Efficient design accelerates the loop between modeling and experimentation it enables the inference of complex mechanisms, such as those controlling central metabolic operation.AVAILABILITY:
Toolbox 'NearOED' available with source code under GPL on the Machine Learning Open Source Software Web site (mloss.org).
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Projetos de Pesquisa
/
Biologia de Sistemas
Tipo de estudo:
Prognostic_studies
Limite:
Animals
Idioma:
En
Ano de publicação:
2013
Tipo de documento:
Article