Open AccessLearning causal networks using inducible transcription factors and transcriptome‐wide time series
Author(s) -
Hackett Sean R,
Baltz Edward A,
Coram Marc,
Wranik Bernd J,
Kim Griffin,
Baker Adam,
Fan Minjie,
Hendrickson David G,
Berndl Marc,
McIsaac R Scott
Publication year - 2020
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.15252/msb.20199174
Subject(s) - biology , transcriptome , computational biology , transcription factor , transcription (linguistics) , genetics , bioinformatics , gene expression , gene , linguistics , philosophy
Abstract We present IDEA (the Induction Dynamics gene Expression Atlas), a dataset constructed by independently inducing hundreds of transcription factors ( TF s) and measuring timecourses of the resulting gene expression responses in budding yeast. Each experiment captures a regulatory cascade connecting a single induced regulator to the genes it causally regulates. We discuss the regulatory cascade of a single TF , Aft1, in detail; however, IDEA contains > 200 TF induction experiments with 20 million individual observations and 100,000 signal‐containing dynamic responses. As an application of IDEA , we integrate all timecourses into a whole‐cell transcriptional model, which is used to predict and validate multiple new and underappreciated transcriptional regulators. We also find that the magnitudes of coefficients in this model are predictive of genetic interaction profile similarities. In addition to being a resource for exploring regulatory connectivity between TF s and their target genes, our modeling approach shows that combining rapid perturbations of individual genes with genome‐scale time‐series measurements is an effective strategy for elucidating gene regulatory networks.