Modeling gene-regulatory networks to describe cell fate transitions and predict master regulators
Pierre-Etienne Cholley1,2, Julien Moehlin1, Alexia Rohmer1
11Equipe 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.
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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