Near-optimal experimental design for model selection in systems biology.
- Busetto AG Department of Computer Science, ETH Zurich, Competence Center for Systems Physiology and Metabolic Diseases, Department of Mathematics, ETH Zurich, Department of Biosystems Science and Engineering, ETH Zurich, Swiss Institute of Bioinformatics, Zurich, Switzerland and National ICT Australia, Melbourne, Australia.
- Hauser A
- Krummenacher G
- Sunnåker M
- Dimopoulos S
- Ong CS
- Stelling J
- Buhmann JM
- 2013-08-01
Published in:
- Bioinformatics (Oxford, England). - 2013
Animals
Models, Theoretical
Probability
Research Design
Signal Transduction
Software
Systems Biology
TOR Serine-Threonine Kinases
English
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).
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).
- Language
-
- English
- Open access status
- hybrid
- Identifiers
-
- DOI 10.1093/bioinformatics/btt436
- PMID 23900189
- Persistent URL
- https://sonar.ch/global/documents/253705
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