Theory in Biosciences

, Volume 131, Issue 2, pp 95–102 | Cite as

Integrating heterogeneous gene expression data for gene regulatory network modelling

Original Paper

Abstract

Gene regulatory networks (GRNs) are complex biological systems that have a large impact on protein levels, so that discovering network interactions is a major objective of systems biology. Quantitative GRN models have been inferred, to date, from time series measurements of gene expression, but at small scale, and with limited application to real data. Time series experiments are typically short (number of time points of the order of ten), whereas regulatory networks can be very large (containing hundreds of genes). This creates an under-determination problem, which negatively influences the results of any inferential algorithm. Presented here is an integrative approach to model inference, which has not been previously discussed to the authors’ knowledge. Multiple heterogeneous expression time series are used to infer the same model, and results are shown to be more robust to noise and parameter perturbation. Additionally, a wavelet analysis shows that these models display limited noise over-fitting within the individual datasets.

Keywords

Gene expression Wavelets Data integration Genetic regulatory networks Complex systems Mathematical modelling 

List of abbreviations

GRN

Gene regulatory network

mRNA

Messenger RNA

cDNA

Complementary DNA

RSS

Residual sum of squares

S

Spellman dataset

Pl

PramilaL dataset

Ps

PramilaS dataset

H

Hasse dataset

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Alina Sîrbu
    • 1
  • Heather J. Ruskin
    • 1
  • Martin Crane
    • 1
  1. 1.Centre for Scientific Computing and Complex Systems Modelling, School of Computing, Dublin City UniversityDublin 9Ireland

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