Paper Info
Title
Near-optimal experimental design for model selection in systems biology.
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.
Year
DOI
Venue
2013
10.1093/bioinformatics/btt436
BIOINFORMATICS
Keywords
Field
DocType
probability,systems biology,signal transduction
Data mining,Polynomial,Source code,Computer science,Toolbox,Software,Artificial intelligence,Inference,Systems biology,Model selection,Bioinformatics,Graphical model,Machine learning
Journal
Volume
Issue
ISSN
29
20
1367-4803
Citations 
PageRank 
References 
11
0.65
15
Authors
8
Name
Order
Citations
PageRank
Alberto Giovanni Busetto1646.74
Alain Hauser2493.59
Gabriel Krummenacher3231.87
Mikael Sunnåker4443.02
Sotiris Dimopoulos5171.12
Cheng Soon Ong6123286.27
Jörg Stelling728034.55
joachim m buhmann84363730.34