Sur ce site

le jeudi 09 mars à 11:00

Jacques van Helden (TAGC, Marseille)

Salle de formation du PRABI

par Vincent Daubin - 9 mars 2017

Understanding regulatory networks in the era of massively parallel sequencing : did we lose our genetic switches and feedback loops ?

The turn from the 20th to the 21st Century was marked by a drastic
change in the scale at which biologists study regulatory networks. In
the 1990, a PhD student could spend years analysing the regulation of
one particular gene by one or a few transcription factors. Microarray
technologies enabled monitoring the expression of all the genes of an
organism in a single experiment (transcriptome arrays), and to lead
genome-wide location analysis to report supposedly exhaustive lists of
transcription factor binding sites. Next Generation Sequencing amplified
the movement, and many labs are now combining ChIP-seq and RNA-seq
experiments to get a wide view on transcription factor binding
locations, histone modifications, and transcriptional responses to a
multitude of conditions, cell types, developmental stages, etc. In the
first part of the talk, I will present some of the bioinformatics
approaches and tools that we developed to analyse regulatory motifs from
various types of high-throughput data (e.g. co-expression clusters,
ChIP-seq peaks, replication origins).

At the light of the evolution of the domain, I would also like to
address a more general question about the insights gained from
high-throughput approaches on fundamental mechanisms of regulation.
Indeed, it implicitly became standard to consider that a typical
high-throughput experiments should return thousands of significant
features (differentially expressed genes, TF binding sites, active
enhancers). This however does not fit with our classical models, were
transcription factors would turn on or off specific sets of target genes
(“regulatory switches”), thereby forming regulatory networks whose
behaviour was understandably determined by feedback loops. How can we
conciliate the undeniable robustness of regulatory networks with the
apparent noisiness of binding and transcription profiles ?