{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T08:07:51Z","timestamp":1773130071244,"version":"3.50.1"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Method: 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.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>Availability and implementation: 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<\/jats:p>\n               <jats:p>Contact: \u00a0grzegorczyk@statistik.tu-dortmund.de; dirk@bioss.ac.uk<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btq711","type":"journal-article","created":{"date-parts":[[2010,12,22]],"date-time":"2010-12-22T02:55:34Z","timestamp":1292986534000},"page":"693-699","source":"Crossref","is-referenced-by-count":71,"title":["Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes"],"prefix":"10.1093","volume":"27","author":[{"given":"Marco","family":"Grzegorczyk","sequence":"first","affiliation":[{"name":"1 Department of Statistics, TU Dortmund University, Dortmund, Germany and 2Biomathematics and Statistics Scotland (BioSS), Edinburgh, UK"}]},{"given":"Dirk","family":"Husmeier","sequence":"additional","affiliation":[{"name":"1 Department of Statistics, TU Dortmund University, Dortmund, Germany and 2Biomathematics and Statistics Scotland (BioSS), Edinburgh, UK"}]}],"member":"286","published-online":{"date-parts":[[2010,12,21]]},"reference":[{"key":"2023012511564487500_B1","first-page":"418","article-title":"Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements","volume":"2000","author":"Butte","year":"2000","journal-title":"Pac. 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