ArticleJournal of computational biology : a journal of computational molecular cell biology2015
cDREM: inferring dynamic combinatorial gene regulation.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Modeling combinatorial regulation from single-cell multi-omics provides regulatory units underpinning cell type landscape using cRegulon.Genome biology · 2025Article
- Recovering time-varying networks from single-cell data.Bioinformatics (Oxford, England) · 2025Article
- Adrenergic signaling controls early transcriptional programs during CD8+ T cell responses to viral infection.PloS one · 2022Article
- Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis.Frontiers in genetics · 2021Article
- Hybrid analysis of gene dynamics predicts context-specific expression and offers regulatory insights.Bioinformatics (Oxford, England) · 2019Article
- MicroRNAs Induce a Permissive Chromatin Environment that Enables Neuronal Subtype-Specific Reprogramming of Adult Human Fibroblasts.Cell stem cell · 2017Article
- A transcription factor hierarchy defines an environmental stress response network.Science (New York, N.Y.) · 2016Article
- From milliseconds to lifetimes: tracking the dynamic behavior of transcription factors in gene networks.Trends in genetics : TIG · 2015Review
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Genes are often combinatorially regulated by multiple transcription factors (TFs). Such combinatorial regulation plays an important role in development and facilitates the ability of cells to respond to different stresses. While a number of approaches have utilized sequence and ChIP-based datasets to study combinational regulation, these have often ignored the combinational logic and the dynamics associated with such regulation. Here we present cDREM, a new method for reconstructing dynamic models of combinatorial regulation. cDREM integrates time series gene expression data with (static) protein interaction data. The method is based on a hidden Markov model and utilizes the sparse group Lasso to identify small subsets of combinatorially active TFs, their time of activation, and the logical function they implement. We tested cDREM on yeast and human data sets. Using yeast we show that the predicted combinatorial sets agree with other high throughput genomic datasets and improve upon prior methods developed to infer combinatorial regulation. Applying cDREM to study human response to flu, we were able to identify several combinatorial TF sets, some of which were known to regulate immune response while others represent novel combinations of important TFs.
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Registered trials
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.