Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Exact Reconstruction of Gene Regulatory Networks using Compressive Sensing

Young Hwan Chang, Joe W. Gray, Claire Tomlin
doi: https://doi.org/10.1101/004242
Young Hwan Chang
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720 USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Joe W. Gray
2Department of Biomedical Engineering and the Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, OR, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Claire Tomlin
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720 USA
3Faculty Scientist, Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: tomlin@eecs.berkeley.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Background We consider the problem of reconstructing a gene regulatory network structure from limited time series gene expression data, without any a priori knowledge of connectivity. We assume that the network is sparse, meaning the connectivity among genes is much less than full connectivity. We develop a method for network reconstruction based on compressive sensing, which takes advantage of the network’s sparseness.

Results For the case in which all genes are accessible for measurement, and there is no measurement noise, we show that our method can be used to exactly reconstruct the network. For the more general problem, in which hidden genes exist and all measurements are contaminated by noise, we show that our method leads to reliable reconstruction. In both cases, coherence of the model is used to assess the ability to reconstruct the network and to design new experiments. For each problem, a set of numerical examples is presented.

Conclusions The method provides a guarantee on how well the inferred graph structure represents the underlying system, reveals deficiencies in the data and model, and suggests experimental directions to remedy the deficiencies.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted April 20, 2014.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Exact Reconstruction of Gene Regulatory Networks using Compressive Sensing
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Exact Reconstruction of Gene Regulatory Networks using Compressive Sensing
Young Hwan Chang, Joe W. Gray, Claire Tomlin
bioRxiv 004242; doi: https://doi.org/10.1101/004242
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Exact Reconstruction of Gene Regulatory Networks using Compressive Sensing
Young Hwan Chang, Joe W. Gray, Claire Tomlin
bioRxiv 004242; doi: https://doi.org/10.1101/004242

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Systems Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4119)
  • Biochemistry (8828)
  • Bioengineering (6532)
  • Bioinformatics (23484)
  • Biophysics (11805)
  • Cancer Biology (9223)
  • Cell Biology (13336)
  • Clinical Trials (138)
  • Developmental Biology (7442)
  • Ecology (11425)
  • Epidemiology (2066)
  • Evolutionary Biology (15173)
  • Genetics (10453)
  • Genomics (14056)
  • Immunology (9187)
  • Microbiology (22199)
  • Molecular Biology (8823)
  • Neuroscience (47626)
  • Paleontology (351)
  • Pathology (1431)
  • Pharmacology and Toxicology (2493)
  • Physiology (3736)
  • Plant Biology (8090)
  • Scientific Communication and Education (1438)
  • Synthetic Biology (2224)
  • Systems Biology (6042)
  • Zoology (1254)