# Computational Models of the Human Cell Cycle to Reveal Disease Mechanism and Inform Treatment

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2022 · $341,558

## Abstract

PROJECT SUMMARY / ABSTRACT
The overall goal of this project is to develop computational models that predict how the human cell cycle
responds to clinically-relevant perturbations such as radiotherapy, targeted therapy, oncogenic mutation, and
directed differentiation. These models will fill a significant void in our understanding of the mechanisms
underlying the initiation, progression, and treatment of diseases that involve abnormal cell proliferation. Our
approach is to use quantitative single-cell imaging to measure the molecular states of proliferating cells and to
integrate these data into predictive modeling frameworks. We have assembled a cross-institutional team
comprising a computational biologist, two cell biologists, and a physician scientist with specialization in
radiation oncology. The team has a strong and productive history of collaboration with six joint publications to
date. Aim 1 investigates the mechanism by which retinal epithelial cells respond to radiation-induced DNA
damage during S phase to execute G2 arrest. Time-lapse imaging and deterministic modeling will predict: how
the response to DNA damage is delayed until the S/G2 transition; how a small-molecule inhibitor of DNA
repair—currently involved in clinical trial—intensifies the arrest response; and how loss of the tumor
suppressor p53 renders cells refractory to combination therapy. Aim 2 asks how pancreatic epithelial cells with
mutations in KRAS escape permanent cell cycle arrest. We will use high-content imaging to profile multiple
signaling activities in single cells expressing oncogenic KRAS. These data will be used to construct a manifold
representation of cell cycle progression that spans a two-week time course of oncogenic KRAS-mediated
transformation. Computational analysis of the manifold’s geometry will identify molecular branching points in
G1 that govern the proliferation/arrest decision in pancreatic cells, and we will validate these predictions
through small molecules and genetic manipulation. Aim 3 tests the hypothesis that human embryonic stem
cells inherit cell-cycle-specific gene products (specifically, G1 regulators) from the previous G2 phase to
promote pluripotency in daughter cells. We will combine mitosis-specific chromatin profiling with convolutional
neural network-based image analysis to identify the mechanisms by which stem cells sustain rapid proliferation
and pluripotency over multiple cell-cycle generations. Each aim yields both basic and applied knowledge,
providing fundamental insights into cell cycle progression under perturbation and generating specific,
molecular predictions to inform new treatment schemes. With an eye toward the future, predictive models of
the human cell cycle will enable patient-specific treatments for diseases that are driven by abnormal cell
proliferation.

## Key facts

- **NIH application ID:** 10458019
- **Project number:** 5R01GM138834-03
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Jeremy Purvis
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $341,558
- **Award type:** 5
- **Project period:** 2020-09-11 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10458019

## Citation

> US National Institutes of Health, RePORTER application 10458019, Computational Models of the Human Cell Cycle to Reveal Disease Mechanism and Inform Treatment (5R01GM138834-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10458019. Licensed CC0.

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