# Defining gene regulatory networks controlling cell fate

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2022 · $329,063

## Abstract

PROJECT SUMMARY
Cell type-specific transcriptional networks are gene regulatory networks that dynamically reconfigure to drive
precise spatio-temporal expression patterns of genes. These networks are central to cell type specificity and are
often disrupted in many diseases. The structure of these networks is defined by a trans component that specifies
which regulatory proteins control a gene’s expression and a cis component that species the regulatory regions
that can regulate a gene’s expression both proximally and distally. Identifying these regulatory networks has
been a significant challenge for mammalian cell types because of the number of potential regulators of a gene
and the large number of assays needed to define these networks accurately. Advances in single cell omics
technologies, such as single cell RNA-seq (scRNA-seq) and single cell ATAC-seq (scATAC-seq), offer new
opportunities to define cell type-specific regulatory networks because of their ability to comprehensively profile
the transcriptome and accessibility for thousands of individual cells. However, computational methods for
integrating these data to define both cell lineage structure and cell-type specific regulatory networks are limited.
Most methods have used only one type of assay focusing either on the cis or trans components and have not
modeled temporal or hierarchical relatedness of multi-sample datasets. Finally, performance of computational
network inference methods has remained low when compared to experimentally detected networks. To address
these challenges, we will develop novel computational methods and powerful resources for mapping gene
regulatory network dynamics driving cell type specificity. Our aims are to (a) develop a computational toolkit to
integrate scRNA-seq and scATAC-seq datasets to infer both cell type lineage (Aim 1) and cell type-specific
transcriptional regulatory networks from scRNA-seq and ATAC-seq data (Aim 2), (b) identify the rewired network
components during a dynamic progress such as cellular reprogramming (Aim 2), and (c) develop an active
learning based approach to infer causal regulatory networks and apply this framework to refine the regulatory
networks for cellular reprogramming (Aim 3). We will apply our tools to public and newly collected datasets as
part of this project. Our analysis will reveal cis and trans regulatory network components associated with cell fate
specification during a dynamic process such as reprogramming or development. Our active learning approach
will use Perturb-Seq to perform regulator perturbations to both validate the predicted networks as well as to
establish improved gold standards for a system with high significance for translational and basic research. The
tools and datasets generated by this project will be publicly available and will serve as a powerful resource to
understand regulatory network dynamics in cell fate specification. Our tools should be broadly applicable to
define regulatory network...

## Key facts

- **NIH application ID:** 10530982
- **Project number:** 1R01GM144708-01A1
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Sushmita Roy
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $329,063
- **Award type:** 1
- **Project period:** 2022-08-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10530982

## Citation

> US National Institutes of Health, RePORTER application 10530982, Defining gene regulatory networks controlling cell fate (1R01GM144708-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10530982. Licensed CC0.

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