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NPJ Syst Biol Appl. 2018 Aug 2;4:29. doi: 10.1038/s41540-018-0066-z. eCollection 2018.

Modeling gene-regulatory networks to describe cell fate transitions and predict master regulators.

Author information

1
1Equipe Labellisée Ligue Contre le Cancer, Department of Functional Genomics and Cancer, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Centre National de la Recherche Scientifique UMR 7104, Institut National de la Santé et de la Recherche Médicale U964, University of Strasbourg, Illkirch, France.
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2Present Address: Computational Systems Biology Infrastructure, Chalmers University of Technology, Kemivägen 10, 41296 Gothenburg, Sweden.
3
3Present Address: UMR 8030 Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, University of Evry-val-d'Essonne, University Paris-Saclay, 91057 Évry, France.

Abstract

Complex organisms originate from and are maintained by the information encoded in the genome. A major challenge of systems biology is to develop algorithms that describe the dynamic regulation of genome functions from large omics datasets. Here, we describe TETRAMER, which reconstructs gene-regulatory networks from temporal transcriptome data during cell fate transitions to predict "master" regulators by simulating cascades of temporal transcription-regulatory events.

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