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On Sparsity As a Criterion in Reconstructing Biochemical Networks
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control.
2011 (English)In: Proceedings of the 18th International Federation of Automatic Control (IFAC) World Congress, 2011, 2011, 11672-11678 p.Conference paper (Refereed)
Abstract [en]

A common problem in inference of gene regulatory networks from experimental response data is the relatively small number of samples available in relation to the number of nodes/states. In many cases the identification problem is underdetermined and prior knowledge is required for the network reconstruction. A specific prior that has gained widespread popularity is the assumption that the underlying network is sparsely connected. This has led to a flood of network reconstruction algorithms based on subset selection and regularization techniques, mainly adopted from the statistics and signal processing communities. In particular, methods based on \ell_1 and \ell_2-penalties on the interaction strengths, such as LASSO, have been widely proposed and applied. We briefly review some of these methods and discuss their suitability for inferring the structure of biochemical networks. A particular problem is the fact that these methods provide little or no information on the uncertainty of individual identified edges, combined with the fact that the identified networks usually have a large fraction of false positives as well as false negatives.To partly overcome these problems we consider conditions that can be used to classify edges into those that can be uniquely determined based on a given incomplete data set, those that cannot be uniquely determined due to collinearity in the data and those for which no information is available. Apart from providing a label of confidence for the individual edges in the identified network, the classification can be used to improve the reconstruction by employing standard unbiased identification methods to the identifiable edges while employing sparse approximation methods for the remaining network. The method is demonstrated through application to a synthetic network in yeast which has recently been proposed for in vivo assessment of network identification methods.

Place, publisher, year, edition, pages
2011. 11672-11678 p.
Keyword [en]
Gene regulatory networks, Modelling, Network inference, Regularization, Reverse engineering, Sparse networks, System identification
National Category
Control Engineering Bioinformatics and Systems Biology
URN: urn:nbn:se:kth:diva-80758DOI: 10.3182/20110828-6-IT-1002.03499ScopusID: 2-s2.0-84866766131OAI: diva2:496725
18th International Federation of Automatic Control (IFAC) World Congress, 2011, Milano Italy, August 28 - September 2, 2011
QC 20120330Available from: 2012-02-10 Created: 2012-02-10 Last updated: 2012-03-30Bibliographically approved

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Nordling, Torbjörn E. M.Jacobsen, Elling W.
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