# Decoding Genome Instability by Combining Accurate Mapping and Predictive Modeling

> **NIH NIH R01** · UNIVERSITY OF TEXAS MED BR GALVESTON · 2021 · $355,500

## Abstract

ABSTRACT
DNA double-strand breaks (DSBs) are the most lethal form of DNA damage and drive aging and cancer. A main
source of spontaneous DSBs is replication stress, i.e. aberrations in DNA replication leading to slowing or stalling
of replication forks. Replication stress can cause DSBs both directly and indirectly, and cells’ reaction to it is
often heterogeneous, which leads to DSB patterns that are difficult to interpret. To overcome this challenge, we
will use computer simulations to analyze DSB data and infer underlying mechanisms of DSB creation. We will
build on and expand techniques we developed in the previous funding period: (1) i-BLESS: the most sensitive
DSB detection method, allowing detection of 1 DSB in 100,000 cells; (2) quantitative DSB sequencing: the only
approach that allows precise genome-wide measurement of absolute DSB frequencies (DSBs/cell); and (3)
Repli-Sim: massive computer simulations of DNA replication that accurately reproduce both single-cell and
population-wide data. Specifically, we will use a combination of innovative computational methods and
experiments in the following Aims: 1) Elucidate the mechanisms of spatiotemporal regulation of DNA replication
and how its disturbance causes replication stress. 2) Clarify and quantify consequences of replication stress and
classify resulting DSBs 3) Characterize heterogeneity of cell population distribution of DSBs resulting from
replication stress and infer its underlying mechanisms. The large-scale of our study will allow us to put each
individual result into much broader context of all other results obtained, thus deepening its interpretation and
allowing for classification of obtained DSB landscapes and inferred pathways regulating replication. Taken
together, our results will lead to a system-level understanding of the mechanisms that cause and prevent
replication stress and DSBs. Our project will raise the study of genomic instability to a new level by quantifying
the mechanisms of replication stress and how they lead to DSBs. Our work will also provide methods and
computational tools to further study genome instability and pave the way to use this knowledge to guide
therapeutic decisions.

## Key facts

- **NIH application ID:** 10077293
- **Project number:** 5R01GM112131-07
- **Recipient organization:** UNIVERSITY OF TEXAS MED BR GALVESTON
- **Principal Investigator:** Maga Malgorzata Rowicka
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $355,500
- **Award type:** 5
- **Project period:** 2014-08-15 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10077293

## Citation

> US National Institutes of Health, RePORTER application 10077293, Decoding Genome Instability by Combining Accurate Mapping and Predictive Modeling (5R01GM112131-07). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10077293. Licensed CC0.

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