ArticleMolecular systems biology2020
Learning causal networks using inducible transcription factors and transcriptome-wide time series.
Article in Molecular systems biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed, 96 citations in OpenAlex.
- scYeast: a biological-knowledge-guided foundation model on yeast single-cell transcriptomics.Synthetic and systems biotechnology · 2027Article
- Time-resolved functional genomics using deep learning reveals global hierarchical control of autophagy.Nature cell biology · 2026Article
- UBIQUITOUS FUNCTIONAL SYNERGY PARTIALLY EXPLAINS WHY MOST TRANSCRIPTION FACTOR BINDING IS NON-FUNCTIONAL.bioRxiv : the preprint server for biology · 2026Article
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- Inferring fungal cis-regulatory networks from genome sequences via unsupervised and interpretable representation learning.Genetics · 2026Article
- Evolution of the Cell-Cycle Clock Models: Biochemical or Transcriptional?Advances in experimental medicine and biology · 2026Review
- GeneSNAKE: a Python package for simulation of gene regulatory networks and perturbation-induced expression data.Bioinformatics advances · 2026Article
- Kar4 acts as a Ste12 regulator inbioRxiv : the preprint server for biology · 2025Article
- Decoding yeast transcriptional regulation via a data-and mechanism-driven distributed large-scale network model.Synthetic and systems biotechnology · 2025Article
- A comparison of computational methods for expression forecasting.Genome biology · 2025Article
- Predicting dynamic expression patterns in budding yeast with a fungal DNA language model.Research square · 2025Article
- Mapping the transcriptional regulatory network of a fungal pathogen by exploiting transcription factor perturbation.bioRxiv : the preprint server for biology · 2025Article
- Predicting dynamic expression patterns in budding yeast with a fungal DNA language model.bioRxiv : the preprint server for biology · 2025Article
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- New reporters for monitoring cellular NMD.RNA (New York, N.Y.) · 2025Article
- Revisiting the model for coactivator recruitment: Med15 can select its target sites independent of promoter-bound transcription factors.Nucleic acids research · 2024Article
- Bayesian inference of sample-specific coexpression networks.Genome research · 2024Article
- Biologically informed NeuralODEs for genome-wide regulatory dynamics.Genome biology · 2024Article
- Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases.Metabolites · 2024Review
- Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference.Genome biology · 2024Article
Corrections and comments
- Commented on byA Bright IDEA.2020
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.