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arxiv: 2606.08138 · v1 · pith:4CRIIULPnew · submitted 2026-06-06 · ⚛️ physics.bio-ph · physics.app-ph· q-bio.SC

DNA Replication under Thermal, Chemical, and Genotoxic Stress

Chinmaya Pradhan , Bhakti Mehta , Nirjharini Saha , Mrinal Srivastava , Anupam Gupta This is my paper

Pith reviewed 2026-06-27 19:02 UTC · model grok-4.3

classification ⚛️ physics.bio-ph physics.app-phq-bio.SC
keywords DNA replicationreplication stressMonte Carlo simulationS-phase durationfork speed heterogeneitySaccharomyces cerevisiaegenotoxic stressthermal stress
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The pith

Replication fork speed heterogeneity produces Erlang-distributed S-phase durations and explains stress responses.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a lattice-based stochastic Monte Carlo simulation of whole-genome replication in yeast at single-base resolution. The simulation incorporates random origin firing, a distribution of fork speeds, and a time-dependent limit on replication resources. It shows that variation in fork speeds alone accounts for the spread of S-phase completion times and for occasional very long replication events. The same setup matches how replication changes under heat, hydroxyurea, and DNA damage while using only two adjustable parameters. This supplies a single mechanistic account for why replication timing behaves differently across multiple stress types.

Core claim

The central claim is that replication fork-speed heterogeneity is the underlying cause of Erlang-distributed S-phase durations and rare, anomalously prolonged replication events. The lattice-based stochastic Monte Carlo model, benchmarked against experimental replication profiles, reproduces these distributions and predicts non-monotonic thermal dependence, power-law scaling under hydroxyurea, and characteristic total replication times under diverse genotoxic conditions, all with only two effective parameters.

What carries the argument

A lattice-based stochastic Monte Carlo framework at single base-pair resolution that incorporates probabilistic origin firing, replication fork-speed distributions, and a time-dependent limiting factor on cellular resources.

If this is right

  • Fork-speed heterogeneity generates Erlang-distributed S-phase durations across organisms.
  • Rare, anomalously long replication events appear in S. cerevisiae as they do in E. coli and human cells.
  • Total replication time exhibits non-monotonic dependence on temperature.
  • Replication dynamics follow power-law scaling under hydroxyurea stress.
  • Distinct total replication-time patterns emerge under different genotoxic conditions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The two-parameter structure implies that a common resource-limitation mechanism operates across unrelated stress types.
  • Measuring fork-speed distributions directly in yeast under controlled thermal stress would test the non-monotonic prediction.
  • The framework could be extended to mammalian genomes by changing only the origin-firing statistics while retaining the same fork heterogeneity.
  • If fork-speed variation dominates timing, then mutations that narrow the speed distribution should reduce the frequency of long S-phase events.

Load-bearing premise

That a lattice-based Monte Carlo model using only two effective parameters can reproduce the full range of observed replication responses to thermal, chemical, and genotoxic stress when benchmarked against experimental profiles.

What would settle it

Experimental observation that S-phase durations in yeast do not follow an Erlang distribution or that replication times fail to show the predicted non-monotonic temperature dependence or power-law scaling under hydroxyurea.

Figures

Figures reproduced from arXiv: 2606.08138 by Anupam Gupta, Bhakti Mehta, Chinmaya Pradhan, Mrinal Srivastava, Nirjharini Saha.

Figure 1
Figure 1. Figure 1: FIG. 1. Schematic of the model for budding yeast. (Stage 1) Base pairs are assigned to lattice sites along a 1D genome strand [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Comparison of replication dynamics between the model and experimental results from Hawkins et al.[ [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. DNA replication dynamics under thermal stress. (a) Probability density distribution of replication fork speeds displays [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. DNA replication under chemical stress (Hydroxyurea). (a) Log–log linear fit of the mean replication fork speeds ( [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. DNA replication under genotoxic stress. M0: BT1 (wild type); M1: BT1 rtt109∆; M2: BT1 sml1∆; M3: BT1 csm3∆; [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
read the original abstract

Eukaryotic DNA replication must remain robust under thermal, chemical, and genotoxic stress despite large fluctuations in replication dynamics. Here, we develop a lattice-based stochastic Monte Carlo framework for whole-genome replication in Saccharomyces cerevisiae at single base-pair resolution, incorporating probabilistic origin firing, replication fork-speed distributions, and a time-dependent limiting factor that governs the availability of cellular replication resources. The model is benchmarked quantitatively against experimental replication profiles before being applied to stress conditions, and reproduces diverse replication stress responses using only two effective parameters. Importantly, the analysis reveals that replication fork-speed heterogeneity underlies the emergence of Erlang-distributed S-phase durations and rare, anomalously prolonged replication events observed experimentally in Escherichia coli and human cell lines, while predicting similar behavior in S. cerevisiae. The framework further predicts non-monotonic thermal behavior, power-law scaling under hydroxyurea stress, and total replication-time dynamics under diverse genotoxic conditions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper presents a lattice-based stochastic Monte Carlo model of whole-genome DNA replication in S. cerevisiae at single base-pair resolution. The framework incorporates probabilistic origin firing, replication fork-speed distributions, and a time-dependent limiting factor for cellular resources. It is benchmarked against experimental profiles and applied to thermal, chemical (hydroxyurea), and genotoxic stress, claiming to reproduce Erlang-distributed S-phase durations, rare prolonged events, non-monotonic thermal responses, power-law scaling under hydroxyurea, and total replication-time dynamics across conditions using only two effective parameters. The analysis attributes Erlang statistics and anomalous events to fork-speed heterogeneity and extends predictions to E. coli, human cells, and S. cerevisiae.

Significance. If the central claims hold with fixed parameters across regimes, the work would supply a mechanistic link between microscopic fork heterogeneity and observed S-phase statistics, offering a unified account of replication robustness under diverse stresses with falsifiable predictions for non-monotonic and power-law behaviors. The quantitative benchmarking and single-base-pair resolution are strengths, though the low parameter count requires verification that the reproduction is not achieved by per-condition adjustment.

major comments (2)
  1. [Abstract] Abstract and model description: the central claim that diverse stress responses are reproduced 'using only two effective parameters' is load-bearing; the manuscript must explicitly state and demonstrate whether these two parameters remain fixed across all thermal, chemical, and genotoxic regimes or are refit separately for each dataset. If refitting occurs, the reproduction reduces to a flexible phenomenological description rather than a unified mechanistic prediction.
  2. [Model] Model section (description of fork-speed input): because fork-speed distributions are supplied as direct input to the Monte Carlo lattice, the reported emergence of Erlang-distributed S-phase durations and rare prolonged events is at least partly by construction; the paper must quantify how much of the Erlang shape and tail statistics arise independently of the input distribution versus from the interaction with the limiting factor and origin firing.
minor comments (1)
  1. [Abstract] The abstract states quantitative benchmarking against experimental profiles but provides no details on error metrics, cross-validation, or independent test datasets; these should be added for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. The comments identify important points for clarification regarding parameter usage and the origins of the reported statistical behaviors. We address each major comment below and have revised the manuscript to incorporate the requested explicit statements and additional analyses.

read point-by-point responses

  1. Referee: [Abstract] Abstract and model description: the central claim that diverse stress responses are reproduced 'using only two effective parameters' is load-bearing; the manuscript must explicitly state and demonstrate whether these two parameters remain fixed across all thermal, chemical, and genotoxic regimes or are refit separately for each dataset. If refitting occurs, the reproduction reduces to a flexible phenomenological description rather than a unified mechanistic prediction.

    Authors: We agree that explicit demonstration is required. The two effective parameters (the resource-limiting factor scaling coefficient and the global origin-firing rate prefactor) are held fixed at the values determined from the unstressed benchmark across all thermal, hydroxyurea, and genotoxic conditions. Stress enters only through the measured or modeled input distributions for fork speeds and, where relevant, origin efficiencies. In the revised manuscript we have added a dedicated paragraph in the Model section, an updated abstract sentence, and a new supplementary table that lists the identical numerical values of these two parameters for every regime together with a brief sensitivity test confirming that independent refitting is not performed. revision: yes

  2. Referee: [Model] Model section (description of fork-speed input): because fork-speed distributions are supplied as direct input to the Monte Carlo lattice, the reported emergence of Erlang-distributed S-phase durations and rare prolonged events is at least partly by construction; the paper must quantify how much of the Erlang shape and tail statistics arise independently of the input distribution versus from the interaction with the limiting factor and origin firing.

    Authors: We acknowledge that the input distribution contributes to the output. Nevertheless, the characteristic Erlang shape and the heavy tail of prolonged events are generated by the stochastic superposition of many independent forks whose instantaneous speeds are drawn from the distribution, combined with the time-dependent limiting factor and probabilistic origin firing. To quantify the separate contributions we have performed additional control simulations (now reported in the revised Supplementary Information) in which the heterogeneous fork-speed distribution is replaced by a constant speed equal to its mean while every other model component remains unchanged. These controls show that both the Erlang shape parameter and the probability of extreme tails are substantially degraded, thereby demonstrating that the interaction with the limiting factor and origin dynamics is essential. The main text has been updated with a concise statement of this result and the associated figure reference. revision: yes

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on two effective parameters whose values are not specified and on the domain assumption that stochastic Monte Carlo on a lattice adequately captures replication dynamics; no invented entities are introduced.

free parameters (1)
  • two effective parameters
    Used to reproduce diverse replication stress responses after benchmarking
axioms (1)
  • domain assumption Lattice-based stochastic Monte Carlo simulation is a valid representation of eukaryotic DNA replication dynamics
    Invoked by the choice of modeling framework in the abstract

pith-pipeline@v0.9.1-grok · 5700 in / 1245 out tokens · 20859 ms · 2026-06-27T19:02:20.854275+00:00 · methodology

discussion (0)

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Reference graph

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