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Near-optimal experimental design for model selection in systems biology.


Busetto, Alberto Giovanni; Hauser, Alain; Krummenacher, Gabriel; Sunnåker, Mikael; Dimopoulos, Sotiris; Ong, Cheng Soon; Stelling, Jörg; Buhmann, Joachim M (2013). Near-optimal experimental design for model selection in systems biology. Bioinformatics:online.

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).

CONTACT:  busettoa@inf.ethz.ch SUPPLEMENTARY INFORMATION:  Supplementary data are available at Bioinformatics online.

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).

CONTACT:  busettoa@inf.ethz.ch SUPPLEMENTARY INFORMATION:  Supplementary data are available at Bioinformatics online.

Citations

8 citations in Web of Science®
8 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:Special Collections > SystemsX.ch
Special Collections > SystemsX.ch > Research, Technology and Development Projects > YeastX
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2013
Deposited On:12 Sep 2013 15:59
Last Modified:05 Apr 2016 16:58
Publisher:Oxford University Press
ISSN:1367-4803
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1093/bioinformatics/btt436
PubMed ID:23900189
Permanent URL: https://doi.org/10.5167/uzh-80925

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