Learning causal networks using inducible transcription factors and transcriptome‐wide time series.

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

We present IDEA (the Induction Dynamics gene Expression Atlas), a dataset constructed by independently inducing hundreds of transcription factors (TFs) 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 TFs 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. Synopsis: A transcriptional induction system is used to conditionally express hundreds of transcription factors in yeast. The resulting time‐course transcriptomics data are used to train parametric models and predict regulatory connections between genes. Chechik & Koller model is used to obtain kinetic parameters for > 100,000 regulatory connections between transcription factors and their target genes.Regulator‐target gene connections are predicted with a dynamical systems model.Transcription factor induction experiments reveal new regulatory connections as well as transcriptional feedback loops and cascades at a genome‐wide scale.The presented data and modeling results can be explored interactively at https://idea.research.calicolabs.com.

TRANSCRIPTION factors; TIME series analysis; REGULATOR genes; GENE expression; DYNAMICAL systems; CIS-regulatory elements (Genetics); FUNGAL gene expression

Molecular Systems Biology, 2020, Vol 16, Issue 3, p1

1744-4292

Academic Journal

10.15252/msb.20199174