Wenpin Hou
Duke University, NC·Genetics
Every cell carries the same genes, yet cells take on very different identities. They do this by switching genes on and off through gene regulatory networks, the webs of regulator genes that control their targets.
- Fields
Genetics
Mathematics & Statistics
Computer Science
- Topics
gene regulatory networks
single-cell multiomics
single-cell RNA-seq
- Grants
- 1 known grant, through Aug 2028
What they work on
- Develop statistical methods that infer static and dynamic gene regulatory networks, including time-lagged regulator effects, from single-cell multiomics data that combine gene activity and epigenomic measurements across many samples.
- Use spatial trajectories from spatial transcriptomics and multiomics data, building on pseudotime analysis, to describe how gene regulation varies across tissue regions and over development.
- Design network control strategies that find the minimum set of driver genes needed to steer cells to a desired cell type or developmental trajectory, using multi-sample single-cell data.
- Benchmark the new methods across tissues and sample conditions with publicly available datasets and release them as open-source software.
More about the research
Knowing these networks could help explain how tissues develop. It could also show how to push diseased cells toward healthy states. New sequencing tools can now read, in the same single cell, both gene activity and the epigenome, the chemical and structural marks that show which DNA control regions are open. Still, methods to combine these data are lacking. Regulation may also be asynchronous: changes in the epigenome can come before changes in gene activity, and a regulator may act only in a short time window or in one region of a tissue.
Why it matters
Better maps of gene regulation could help researchers choose which genes to manipulate in order to move cells toward healthy states. Narrowing the candidates to a minimal set of driver genes may cut the number of costly validation experiments. It could also inform treatment strategies across many tissue and disease contexts.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Sushmita Roy
University of Wisconsin-Madison, WI·Computer Science
Develop machine-learning methods, including probabilistic graphical models and matrix factorization, that define cell types and their lineage relationships across species from scRNA-seq and scATAC-seq while modeling the species phylogeny.
Anat Kreimer
Rutgers University-New Brunswick, NJ·Genetics
Develop methods that integrate massively parallel reporter assay (MPRA) data, which test thousands of DNA sequences at once, with other genomic assays and deep learning to reconstruct cell-type-specific gene regulatory networks and their condition-specific regulators.
Beryl Jones
University of Kentucky, KY·Ecology & Evolution
Identify changes in gene regulatory networks (GRNs) linked to evolutionary gains and losses of social behavior across bee species that differ in sociality.
The school
Duke University: 61 labs on PI Pages, $1.6B a year on research, 417 research doctorates a year.
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