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Statistical inference of the time-varying structure of gene-regulation networks
Authors:Sophie Lèbre  Jennifer Becq  Frédéric Devaux  Michael PH Stumpf  Gaëlle Lelandais
Institution:1.Center for Bioinformatics, Imperial College London,London,UK;2.Laboratoire des Sciences de l'Image de l'Informatique et de la télédétection (LSIIT), UMR UdS-CNRS 7005, Université de Strasbourg,Strasbourg,France;3.Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB), INSERM U 665,Paris,France;4.Université Paris Diderot - Paris 7, UMR-S665,Paris,France;5.INTS,Paris,France;6.Laboratoire de Génomique des Microorganismes, CNRS FRE 3214, Université Pierre et Marie Curie, Institut des Cordeliers,Paris,France;7.Institute of Mathematical Sciences, Imperial College London,London,UK
Abstract:

Background  

Biological networks are highly dynamic in response to environmental and physiological cues. This variability is in contrast to conventional analyses of biological networks, which have overwhelmingly employed static graph models which stay constant over time to describe biological systems and their underlying molecular interactions.
Keywords:
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