Modeling gene-regulatory networks to describe cell fate transitions and predict master regulators
Journal article, 2018
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.
Author
Pierre-Etienne Cholley
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
University of Strasbourg
CSBI
Julien Moehlin
University of Strasbourg
Alexia Rohmer
University of Strasbourg
Vincent Zilliox
University of Strasbourg
Samuel Nicaise
University of Strasbourg
Hinrich Gronemeyer
University of Strasbourg
Marco Antonio Mendoza-Parra
Genomique Metabolique
University of Strasbourg
npj Systems Biology and Applications
20567189 (eISSN)
Vol. 4 1 29Subject Categories
Developmental Biology
Bioinformatics (Computational Biology)
Bioinformatics and Systems Biology
DOI
10.1038/s41540-018-0066-z