Reverse engineering of gene networks with LASSO and nonlinear basis functions

Mika Gustafsson, Michael Hörnquist*, Jesper Lundström, Johan Björkegren, Jesper Tegnér

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

28 Scopus citations


The quest to determine cause from effect is often referred to as reverse engineering in the context of cellular networks. Here we propose and evaluate an algorithm for reverse engineering a gene regulatory network from time-series and steady-state data. Our algorithmic pipeline, which is rather standard in its parts but not in its integrative composition, combines ordinary differential equations, parameter estimations by least angle regression, and cross-validation procedures for determining the in-degrees and selection of nonlinear transfer functions. The result of the algorithm is a complete directed network, in which each edge has been assigned a score from a bootstrap procedure. To evaluate the performance, we submitted the outcome of the algorithm to the reverse engineering assessment competition DREAM2, where we used the data corresponding to the InSilico1 and InSilico2 networks as input. Our algorithm outperformed all other algorithms when inferring one of the directed gene-to-gene networks.

Original languageEnglish (US)
Title of host publicationThe Challenges of Systems Biology Community Efforts to Harness Biological Complexity
PublisherBlackwell Publishing Inc.
Number of pages11
ISBN (Print)9781573317511
StatePublished - Mar 2009
Externally publishedYes

Publication series

NameAnnals of the New York Academy of Sciences
ISSN (Print)0077-8923
ISSN (Electronic)1749-6632


  • DREAM conference
  • LARS
  • Network inference
  • Nonlinear
  • Reverse engineering

ASJC Scopus subject areas

  • General Neuroscience
  • General Biochemistry, Genetics and Molecular Biology
  • History and Philosophy of Science


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