Reconstruction of gene regulatory networks with hidden nodes

Young Hwan Chang, Claire Tomlin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


In studying biological systems, identifying the underlying gene regulatory networks from data has been important and will continue to affect the study of gene regulatory networks. We consider the problem of reconstructing the network structure from observed data, and in turn uncovering the underlying mechanisms responsible for the observed behaviors. A key challenge inherent in the network reconstruction problem comes from the necessity to deal with noisy and partial measurements. In previous work [1], we have proposed a method based on compressive sensing (CS) for reconstructing a sparse network structure, without any a priori information of connectivity, based on the time-series gene expression data. In this paper, we extend our previous work to consider a more general problem in which there might be hidden nodes which affect system dynamics. Then, we ask whether it is still possible to reconstruct the graph structure reliably when the dynamics of a certain node is corrupted by arbitrarily large errors and in addition, all the measurements are contaminated by measurement noise. We show that we can infer the graph structure by solving a two-stage convex optimization problem and demonstrate our studies with numerical example to illustrate its performance.

Original languageEnglish (US)
Title of host publication2014 European Control Conference, ECC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9783952426913
StatePublished - Jul 22 2014
Externally publishedYes
Event13th European Control Conference, ECC 2014 - Strasbourg, France
Duration: Jun 24 2014Jun 27 2014

Publication series

Name2014 European Control Conference, ECC 2014


Other13th European Control Conference, ECC 2014

ASJC Scopus subject areas

  • Control and Systems Engineering


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