Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression

Edward R. Morrissey, Miguel A. Juárez*, Katherine J. Denby, Nigel J. Burroughs

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


We propose a semiparametric Bayesian model, based on penalized splines, for the recovery of the time-invariant topology of a causal interaction network from longitudinal data. Our motivation is inference of gene regulatory networks from low-resolution microarray time series, where existence of nonlinear interactions is well known. Parenthood relations are mapped by augmenting the model with kinship indicators and providing these with either an overall or gene-wise hierarchical structure. Appropriate specification of the prior is crucial to control the flexibility of the splines, especially under circumstances of scarce data; thus, we provide an informative, proper prior. Substantive improvement in network inference over a linear model is demonstrated using synthetic data drawn from ordinary differential equation models and gene expression from an experimental data set of the Arabidopsis thaliana circadian rhythm.

Original languageEnglish
Pages (from-to)682-694
Number of pages13
Issue number4
Publication statusPublished - Oct 2011


  • Circadian clock
  • Gibbs variable selection
  • Markov process prior
  • Nonlinear gene regulatory networks
  • P-Splines regression
  • Time course gene expression data

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