🔬 applied physics

DNA Replication under Thermal, Chemical, and Genotoxic Stress

This paper presents a stochastic Monte Carlo framework for whole-genome DNA replication in *Saccharomyces cerevisiae* that quantitatively reproduces diverse stress responses and reveals how fork-speed heterogeneity drives S-phase duration distributions and anomalous replication events.

Original authors: Chinmaya Pradhan, Bhakti Mehta, Nirjharini Saha, Mrinal Srivastava, Anupam Gupta

Published 2026-06-09

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Chinmaya Pradhan, Bhakti Mehta, Nirjharini Saha, Mrinal Srivastava, Anupam Gupta

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine a cell as a massive construction site where the goal is to copy a 3-billion-page instruction manual (the DNA) perfectly before the cell divides. In the yeast cell (Saccharomyces cerevisiae), this copying job happens during a specific shift called the "S-phase."

For a long time, scientists thought this process was like a factory assembly line: very predictable, with machines starting at exact times and moving at a steady speed. However, this new paper argues that the reality is more like a chaotic, bustling city during rush hour, governed by chance and limited resources.

Here is how the authors built a computer model to understand this chaos, using simple analogies:

1. The Construction Site and the "Start Buttons"

Think of the DNA as a long, straight road. Scattered along this road are thousands of "Start Buttons" (origins of replication).

The Old View: Everyone thought these buttons were pressed in a strict, pre-planned order.
The New View (This Paper): The buttons are pressed randomly. Some get pressed early, some late, and some not at all. It's a game of chance.

2. The "Fuel Tank" (Limiting Factors)

You can't start every construction crew at the exact same time because the city only has a limited supply of fuel (cellular resources).

The Analogy: Imagine a limited number of "Fuel Trucks" (limiting factors) available to the site.
How it works: To start a new crew (a replication fork), you need two Fuel Trucks.
- At the start of the shift, there are very few trucks, so only a few crews can start.
- As the shift goes on, the supply of trucks increases, allowing more crews to launch.
- When two crews meet in the middle of a road section and finish their job, they merge, and their two Fuel Trucks are released back into the pool to start new crews elsewhere.
The Result: This creates a natural rhythm. You don't start everyone at once; you start them as resources become available, and you stop starting new ones when the road is almost done.

3. The "Speed Bumps" (Fork Speed Heterogeneity)

Once a crew starts, they don't all drive at the same speed.

The Analogy: Some construction crews are on a smooth highway (fast DNA regions), while others are stuck in traffic or navigating potholes (slow DNA regions, like areas with tight packaging or active transcription).
The Discovery: The paper found that if you assume all crews drive at the same average speed, the whole project finishes in a predictable time. But because some crews are slow and some are fast, the total time to finish the project varies wildly.
The "Long Tail": Occasionally, you get a "perfect storm" where a few crews get stuck in terrible traffic. This causes the entire project to take much longer than usual. The paper shows that these rare, long delays follow a specific mathematical pattern (called an Erlang distribution), which explains why some cells take much longer to divide than others.

4. Stressing the System

The authors tested their model under three types of "bad weather" to see how the construction site copes:

Thermal Stress (Temperature Changes):
- Cold: The crews drive slower. Because they move slowly, they stay on the road longer, "hogging" the Fuel Trucks. This prevents new crews from starting, so the site runs with fewer active crews but for a longer time.
- Hot: The crews drive very fast. They finish their sections quickly, release the Fuel Trucks, and start new crews. However, if it gets too hot, the crews finish so fast that they run into each other before new crews can even start, actually reducing the total number of crews needed.
- The Surprise: The number of active crews doesn't just go up or down with temperature; it goes up, peaks, and then goes down (a non-monotonic curve).
Chemical Stress (Hydroxyurea):
- This drug is like putting a "slow-down" sign on the road. It reduces the fuel supply (dNTPs), making the crews drive much slower.
- The Finding: Simply slowing the crews down wasn't enough to match real-world data. The model showed that the drug also effectively reduced the number of Fuel Trucks available. The cell has to do two things to survive: slow down the drivers and stop starting new crews to avoid running out of fuel.
Genotoxic Stress (Damaged DNA/Mutations):
- The authors looked at yeast with broken "safety gear" (mutant proteins).
- Some mutants had crews that moved too fast (unsafe), while others had crews that moved too slowly or got stuck easily.
- The model showed that when the safety gear is broken, the crews either speed up dangerously or get stuck, leading to longer completion times and more instability.

The Big Picture

The paper concludes that you don't need a complex, pre-written master plan to explain how cells replicate DNA. Instead, a simple system with just two main rules is enough:

Random Start Times: Start crews randomly based on a probability.
Limited Resources: You can only start new crews if you have enough "Fuel Trucks," and you get more trucks as the job progresses.

When you add the reality that some crews drive faster than others, this simple system perfectly explains why DNA replication is robust (it works even when things go wrong) and why it sometimes takes a surprisingly long time to finish. It turns a chaotic, random process into a reliable biological machine.

Technical Summary: DNA Replication under Thermal, Chemical, and Genotoxic Stress

Problem Statement
Faithful genome duplication is essential for genomic integrity, yet eukaryotic DNA replication must remain robust despite fluctuations caused by thermal, chemical, and genotoxic stress. While Saccharomyces cerevisiae (budding yeast) is a model system for studying replication, conventional views often treat its replication program as predominantly deterministic, with fixed origin firing times and constant fork speeds. However, experimental evidence indicates substantial stochasticity in origin firing efficiency and significant heterogeneity in replication fork speeds. Existing stochastic models often operate at coarse-grained resolutions (e.g., ~1 kbp) or assume constant mean fork speeds, failing to capture fine-scale dynamics, rare prolonged replication events, or the specific mechanisms by which cells adapt to diverse stress conditions. There is a need for a quantitative framework that integrates stochastic origin firing, fork speed distributions, and resource limitations at single-base-pair resolution to understand how robust replication emerges under stress.

Methodology
The authors developed a lattice-based stochastic Monte Carlo framework for whole-genome replication in S. cerevisiae at single base-pair (1 bp) resolution. The model represents the genome as a one-dimensional strand where base pairs transition from an unreplicated state (0) to a replicated state (1). The framework is governed by two primary, experimentally motivated parameters:

Stochastic Origin Firing: The model utilizes experimentally determined data for 459 potential origins, characterized by competence ( $P$ ), median firing time ( $t_{1/2}$ ), and firing time width ( $t_w$ ). Firing is modeled as a time-dependent Gaussian probability distribution. To prevent large inter-origin gaps, the genome is partitioned, and origins are sampled stochastically with a constant rate.
Limiting Factor ( $L(t)$ ): A time-dependent limiting factor represents a finite pool of cellular resources required to initiate origin firing. An origin fires only if two limiting factors are available. These factors are recruited to form active replication forks and are released upon fork convergence (merger), allowing them to be recycled for subsequent origin firing. The temporal dynamics of $L(t)$ follow a sigmoid function constrained by experimental active fork statistics.
Fork Speed Distribution: Unlike models assuming a constant speed, this framework assigns fork speeds drawn from a skewed Gaussian distribution ( $f(v)$ ), parameterized by location ( $\xi$ ), width ( $\omega$ ), and skewness ( $\alpha$ ). These parameters are derived from experimental data or linear extrapolations based on temperature and stress conditions.

The model is benchmarked against experimental replication profiles (active fork counts, replicated fraction, inter-origin distances, and spatiotemporal replication profiles) before being applied to thermal, chemical (hydroxyurea), and genotoxic stress conditions.

Key Contributions

Single-Base-Pair Resolution: The framework achieves the highest physically meaningful resolution (1 bp), explicitly tracking replication timing and fork propagation, enabling the analysis of fine-scale dynamics and localized mutation patterns.
Minimal Parameterization: The model reproduces diverse stress responses using only two effective parameters: the time-dependent limiting factor and the fork speed distribution, without invoking condition-specific regulatory mechanisms.
Integration of Heterogeneity: It explicitly incorporates fork speed heterogeneity and stochastic origin firing, moving beyond deterministic or constant-speed assumptions.

Results

Benchmarking: The model quantitatively reproduces experimental data for active fork numbers, genome replication fractions, and spatiotemporal replication profiles (STRP) of chromosome 14 at 23°C.
Erlang-Distributed S-Phase Durations: The inclusion of fork speed heterogeneity generates a distribution of total replication times ( $T_{rep}$ ) that follows an Erlang distribution with a pronounced right-skewed tail. This explains the emergence of rare, anomalously prolonged replication events observed experimentally in E. coli and human cell lines, predicting similar behavior in S. cerevisiae. Simulations with constant fork speeds fail to reproduce these long-duration events.
Thermal Stress: The model predicts a non-monotonic dependence of the total number of replication forks on temperature. Fork numbers peak around 23°C; at lower temperatures, slower fork speeds lead to delayed merging and sequestration of limiting factors, suppressing new origin firing. At higher temperatures, rapid fork speeds cause many origins to be passively replicated before firing. The model accurately recapitulates experimental $T_{rep}$ values across temperatures using linear extrapolation of fork speed distribution parameters.
Chemical Stress (Hydroxyurea): Under hydroxyurea (HU) stress, the model demonstrates that reduced fork speed alone is insufficient to match experimental replication profiles. Accurate reproduction of data requires a simultaneous reduction in the availability of the limiting factor (to ~30% of its original value). The model predicts a power-law scaling relationship between fork speed and HU concentration.
Genotoxic Stress: The framework successfully models replication dynamics in various S. cerevisiae mutant strains (affecting histone acetylation, dNTP pools, and fork protection complexes). It captures how specific mutations alter fork speed distributions and, in some cases (e.g., rtt109 $\Delta$ ), modulate the limiting factor function, leading to changes in total replication time.

Significance
The paper claims that robust genome replication across diverse conditions emerges from a minimal framework driven by the competition for limiting replication resources and the heterogeneity of fork speeds. The study provides a physical interpretation of how global replication dynamics, including the sharp acceleration of S-phase progression and the distribution of S-phase durations, arise from these fundamental mechanisms. By operating at single-base-pair resolution, the framework offers a versatile platform for investigating replication dynamics in local genomic regions associated with point mutations and diseases. The authors conclude that this work establishes a quantitative foundation for understanding how genome replication adapts to stress, unifying the control of replication under thermal, chemical, and genotoxic conditions through a shared set of effective parameters.