Inferring Time-Delayed Gene Regulatory Networks Using Cross-Correlation and Sparse Regression

  • Piyushkumar A. Mundra
  • Jie Zheng
  • Mahesan Niranjan
  • Roy E. Welsch
  • Jagath C. Rajapakse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7875)


Inferring a time-delayed gene regulatory network from microarray gene-expression is challenging due to the small numbers of time samples and requirements to estimate a large number of parameters. In this paper, we present a two-step approach to tackle this challenge: first, an unbiased cross-correlation is used to determine the probable list of time-delays and then, a penalized regression technique such as the LASSO is used to infer the time-delayed network. This approach is tested on several synthetic and one real dataset. The results indicate the efficacy of the approach with promising future directions.


LASSO gene regulation time-delayed interactions microarray analysis cross-correlation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Piyushkumar A. Mundra
    • 1
  • Jie Zheng
    • 1
    • 5
  • Mahesan Niranjan
    • 2
  • Roy E. Welsch
    • 3
    • 4
  • Jagath C. Rajapakse
    • 1
    • 3
    • 6
  1. 1.BioInformatics Research Centre, School of Computer EngineeringNanyang Technological UniversitySingapore
  2. 2.School of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUnited Kingdom
  3. 3.Computation and Systems Biology, Singapore-MIT AllianceNanyang Technological UniversitySingapore
  4. 4.Sloan School of ManagementMassachusetts Institute of TechnologyCambridgeUSA
  5. 5.Genome Institute of SingaporeSingapore
  6. 6.Department of Biological EngineeringMassachusetts Institute of TechnologyUSA

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