Retrieving common dynamics of gene regulatory networks under various perturbations

Young Hwan Chang, Roel Dobbe, Palak Bhushan, Joe W. Gray, Claire J. Tomlin

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

2 Scopus citations

Abstract

Recently, with the growth of high-throughput proteomic data, in particular time series gene expression data from various perturbations, a general question that has arisen is how to extract meaningful structure from inherently heterogeneous data. Little is known about exactly how these gene regulatory networks (GRNs) operate under different stimuli. Challenges due to the lack of knowledge may cause bias or uncertainty in identifying parameters or inferring the GRN structure. We propose a new algorithm which enables us to estimate bias error due to the effect of perturbations, and correctly identify the common graph structure among biased inferred graph structures. To do this, we retrieve common dynamics of the GRN subject to various perturbations inspired by image repairing in computer vision [1].

Original languageEnglish (US)
Title of host publication54rd IEEE Conference on Decision and Control,CDC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2531-2536
Number of pages6
ISBN (Electronic)9781479978861
DOIs
StatePublished - Feb 8 2015
Event54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan
Duration: Dec 15 2015Dec 18 2015

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume54rd IEEE Conference on Decision and Control,CDC 2015
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Other

Other54th IEEE Conference on Decision and Control, CDC 2015
Country/TerritoryJapan
CityOsaka
Period12/15/1512/18/15

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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