title: Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes.
abstract: METHODS
  Dynamic Bayesian networks (DBNs) have been applied widely to reconstruct the structure of regulatory processes from time series data, and they have established themselves as a standard modelling tool in computational systems biology. The conventional approach is based on the assumption of a homogeneous Markov chain, and many recent research efforts have focused on relaxing this restriction. An approach that enjoys particular popularity is based on a combination of a DBN with a multiple changepoint process, and the application of a Bayesian inference scheme via reversible jump Markov chain Monte Carlo (RJMCMC). In the present article, we expand this approach in two ways. First, we show that a dynamic programming scheme allows the changepoints to be sampled from the correct conditional distribution, which results in improved convergence over RJMCMC. Second, we introduce a novel Bayesian clustering and information sharing scheme among nodes, which provides a mechanism for automatic model complexity tuning.
  
  
  RESULTS
  We evaluate the dynamic programming scheme on expression time series for Arabidopsis thaliana genes involved in circadian regulation. In a simulation study we demonstrate that the regularization scheme improves the network reconstruction accuracy over that obtained with recently proposed inhomogeneous DBNs. For gene expression profiles from a synthetically designed Saccharomyces cerevisiae strain under switching carbon metabolism we show that the combination of both: dynamic programming and regularization yields an inference procedure that outperforms two alternative established network reconstruction methods from the biology literature.
  
  
  METHODS
  A MATLAB implementation of the algorithm and a supplementary paper with algorithmic details and further results for the Arabidopsis data can be downloaded from: http://www.statistik.tu-dortmund.de/bio2010.html.
authors.affiliation: Department of Statistics, TU Dortmund University, Dortmund, Germany. grzegorczyk@statistik.tu-dortmund.de
authors.forename: Marco
authors.initials: M
authors.lastname: Grzegorczyk
authors.forename: Dirk
authors.initials: D
authors.lastname: Husmeier
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics; D001499:Bayes Theorem; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism