# Computational methods to predict gene regulatory network dynamics and cell state transitions

> **NIH NIH R35** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2022 · $412,500

## Abstract

Project Summary
The goal of this research program is to provide tools for the discovery of transcriptional networks that control
cell fate decisions. Cell fate decisions driven by cell state transitions underlie essential cell processes from
development to cellular reprogramming. There is an opportunity to make use of publicly available genomic data
to develop predictive computational models of cell state transition dynamics. The methods proposed will offer
means to gain insight into cell fate decision-making and how it is transcriptionally regulated, given specific cell
fate decision points and suitable data. Examples of such decision points include control of epidermal
regeneration, or the maintenance of balance among myeloid cell fates during hematopoiesis. In order to bridge
the gap between genomics and cell dynamics, statistical and computational modeling challenges must be
overcome. Two key challenges form the basis of this research program: 1) developing statistical methods to
infer regulatory networks while accounting for the levels of variability between single cells, and 2) developing
computational models to couple gene regulatory dynamics within cells and cell-cell communication between
cells. To address the first challenge, we will develop machine learning models to predict gene expression
dynamics from time-series data. These models will be able to classify genes by their temporal patterns, and
the results will inform gene network inference. We will then develop methods for network inference that
integrate muti-modal data (single-cell RNA and ATAC sequencing) as well as cell-cell signaling information to
learn networks that control specific cell state transitions. To address the second challenge, we will develop
differential equation-based multiscale models of the gene regulatory network dynamics coupled with the cell-
external signaling dynamics. This will allow us to capture both molecular and cellular dynamics in high
resolution, and thus identify which parameters exert key control over the system. We will use Bayesian
methods for parameter inference to fit models to data and perform model selection, adapting methods where
needed for multiscale model inference. Models will be rigorously evaluated through their application to specific
systems, including cell differentiation (e.g. myeloid fate decisions during hematopoiesis) and development (e.g.
nephron progenitor cell fate decisions). In each of these organ systems, models predictions will be tested
experimentally via collaborations. Following iterative testing, open-source, validated methods will be made
widely available for the study of the dynamic processes of cell fate decision-making.

## Key facts

- **NIH application ID:** 10490309
- **Project number:** 5R35GM143019-02
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Adam L MacLean
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $412,500
- **Award type:** 5
- **Project period:** 2021-09-18 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10490309

## Citation

> US National Institutes of Health, RePORTER application 10490309, Computational methods to predict gene regulatory network dynamics and cell state transitions (5R35GM143019-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10490309. Licensed CC0.

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