# Reconstructing regulatory networks from time series single cell data

> **NIH NIH R01** · CARNEGIE-MELLON UNIVERSITY · 2020 · $81,396

## Abstract

Reconstructing regulatory networks from time series single cell data
New technological advances are enabling researchers to profile the gene expression of single
cells. These experiments, termed single cell RNA-Seq, open the door to several important
applications. These include the ability to elucidate the networks and pathways controlling
cellular differentiation and understanding the sequence of regulatory events that lead to, and
control, cell fate decisions. Such models and networks provide critical information for
investigators attempting to derive specific types of differentiated human cells which in turn
opens the door to several applications ranging from disease modeling to the ability to use
regenerative cells for potential reconstitution of damaged cells or tissues.
However, the analysis of single cell RNA-Seq data, and specifically time series single cell data
which is required for such developmental studies, raises several new challenges. Determining
which cells should be combined to construct developmental models is challenging. Cells at each
time point usually come from a mixture of cell types, each of which may be a progenitor of one,
or several, specific lineages. To reconstruct the networks controlling cell differentiation we first
need to determine a `time series' by linking single cells within and between time points and then
use these assignments to reconstruct the networks and pathways that drive cell fate decisions.
A specific example of a differentiation process we intend to study is abnormal lung development
which often arises due to genetic perturbations and can lead to congenital or neonatal lung
diseases. Our preliminary results indicate that single cell RNA-Seq data has great potential to
illuminate the complex gene regulatory networks that control normal development of several
different types of cells in the lung and to aid in identifying regulatory mechanism that may go
awry during abnormal development leading to disease.
Given these initial findings, in this project we will develop and test computational methods,
based on probabilistic graphical models, for the analysis and modeling of time series single cell
RNA-Seq data. Our methods would allow the determination of the different types of cells at each
time point, relationship between cells across time points and the reconstruction of regulatory
networks that control the differentiation process. The reconstructed networks would also allow
us to identify key genes and factors controlling the differentiation process and would lead to
testable hypotheses about the proteins regulating key events. We will apply the methods we
develop to study and model normal and diseased lung development by performing new single
experiments on human induced pluripotent stem (iPS) cells.

## Key facts

- **NIH application ID:** 9965031
- **Project number:** 3R01GM122096-03S1
- **Recipient organization:** CARNEGIE-MELLON UNIVERSITY
- **Principal Investigator:** Ziv Bar-Joseph
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $81,396
- **Award type:** 3
- **Project period:** 2017-08-01 → 2021-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9965031

## Citation

> US National Institutes of Health, RePORTER application 9965031, Reconstructing regulatory networks from time series single cell data (3R01GM122096-03S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9965031. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*