Differential equation based reverse-engineering algorithms: Pros and cons

Gennaro Gambardella, Roberto Pagliarini, Francesco Gregoretti, Gennaro Oliva, Diego Di Bernardo

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Ordinary Differential Equations (ODEs) represent a deterministic approach to model gene regulatory networks. ODEs can be used to model changes in gene transcription induced by an external perturbation, such as gene overexpression/downregulation or treatment with a drug. Reverse-engineering algorithms based on ODEs require a choice of a functional form describing the effect of a regulator on its target genes. Here, we focused on an ODE-based reverse engineering algorithm named Network Identification by multiple Regression (NIR) which is rooted on the hypothesis that the regulation exerted by one gene (i.e., a TF) on a target gene can be approximated by a linear function, i.e., the transcription rate of the target gene is proportional to the amount of TF. NIR uses steady-state gene expression measurements and requires knowledge of the genes perturbed in each experiment. We showed that even if originally NIR was created for a different purpose, it can be successfully used to infer gene regulation from an integrated genotype and phenotype dataset. Our results provide evidence of the feasibility of applying reverse-engineering algorithms, such as NIR, to infer gene regulatory networks by integrated analysis of genotype and phenotype.

Original languageEnglish
Title of host publicationGene Network Inference: Verification of Methods for Systems Genetics Data
PublisherSpringer-Verlag Berlin Heidelberg
Pages49-61
Number of pages13
ISBN (Print)9783642451614, 3642451608, 9783642451607
DOIs
Publication statusPublished - Dec 1 2013

Keywords

  • Gene networks
  • ODE
  • Reverse engineering

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

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