PCOMPBIOL-D-25-00798

Model-based inference of cell cycle dynamics captures alterations of the DNA replication programme

PLOS Computational Biology

Dear Dr. Alsina,

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Kind regards,

Jing Chen

Academic Editor

PLOS Computational Biology

Mark Alber

Section Editor

PLOS Computational Biology

Additional Editor Comments :

The reviewers have mixed opinions about the general value of the work. One reviewer think it is generally suitable for the journal, while another criticized about the lack of valuable biology insights. Every reviewer, as well as myself, agrees on several major issues. First, the claim they about the performance of the tool compared to others was not supported by data. Second, some of the model assumptions are questionable. Third, lack of clarity makes judgement of the value of the work difficult. If the authors can successfully address these issues, the work will be considered for publication.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note that one of the reviews is uploaded as an attachment.

Reviewer #1: This paper presents an interesting mathematical model of flow cytometry cell cycle profiles. It recapitulates previous findings pertaining to cell cycle perturbations of yeast mutants. While the approach can quantify cell cycle alterations including perturbations within S phase specifically, it is not clear that the approach is superior to other analyses of the same data. In that respect, the authors do not present a comparison and analysis of their data relative to other approaches for analyzing flow cytometry data.

The authors claim that they can infer perturbations of replication dynamics and also infer parameters of replication dynamics in general. I do not agree with these statements. While they can identify changes specifically in early vs late S phase, flow cytometry and their model cannot distinguish between replication origin loss/gain, changes in origin firing times, and changes in fork speed. It certainly cannot determine where in the genome any such changes (or the normal program for that matter) originate from. A delay in late S phase, for example (and as reported for RRM3 KO, similar to previous reports), could be due to loss of some origins, delays in a variety of replication origin subsets, or slowed replication forks, at specific locations or genome-wide.

Overall, while the mathematical models developed here may be of interest to researchers specializing in such approaches, the biological insights obtained with this work are limited.

Reviewer #2: In this manuscript the authors present a cell cycle based maximum likelihood based model to infer cell cycle distributions and replication dynamics using flow cytometry data solely stained with a DNA dye (i.e., DAPI). I think this approach is a very welcome addition to the cell cycle analysis toolkit. This is especially strong approach to analyze cell cycle distributions over a large set of perturbations, as demonstrated using the genetic knockout library in yeast. I think this manuscript is suitable for publication in PLoS Computational Biology, following addressing my concerns

Concerns

Although I appreciate the technical nature of this journal, I feel that the manuscript should be re-written with a larger audience in mind. Adoption of these methods is critical for their success. Therefore, I like to see two changes:

• The entire manuscript (especially the result section) should be extensively re-written to read less like a technical manual and take the time to explain design decisions for their application so that their target audience which I assume are both wet-lab biologists as well as computational biologist are able to digest this manuscript. The authors can describe the formulas and nitty-gritty details in the methods section of their manuscript.

• Code repositories should be made available with clear instructions on how to run the associated code (i.e., vignette). This might be even included in the manuscript as Supplementary documentation.

The assumption that even monogenic cell lines are progressing homogenous through the cell cycle is not necessarily true. For instance, using single cell sequencing revealed that cells at similar stages in S-phase can display different number of forks and replication speeds (van den Berg et al., Nature Methods, 2024). More recently, multigenerational time-lapse imaging has revealed that even sister cells are not progressing at similar rates through the cell cycle (Panagopoulos et al., Nature, 2025). I think the authors should at least cite these works and discuss their implications to their work. But I think a deeper exploration of variability parameters on their maximum likelihood estimation will increase the robustness of their method.

The authors state G1 are easily identifiable form S-phase cells based on the data from the Rainey et al., 2020 paper. However, I do not understand why they cannot use the G2 population from these data as this seems to be as easily identifiable in these data. It is very unclear why Fig. 8/ Suppl.Fig.3 the author opted to select a Cdc7i for a perturbation from Rainey et al., which I had to figure out myself by comparing resemblance between Suppl. Fig. 3 and Fig. 3a,c,j from Rainey et al., 2020. I personally do not see the problem in identifying the G2 population in these data. Therefore, I strongly feel the authors need to show that their model also predicts G2 proportions in a similar way they can predict fraction of G1 populations.

I am not exactly clear what the additional contribution of their microscopic model of replication dynamics is. In my mind, you still require the position of licensed origins, number of forks and position and fraction of replicated genome. Formally, the only thing this exercise adds is a speed and an origin firing rate? Do these speeds and orgin firing rates change in some of the yeast knockout strains? Are these in line with published literature? If the authors want to include this microscopic model in their revisions, they need to extend their analysis to other strains (i.e., knockouts) and show its applicability beyond a single condition.

The legends, especially of the Supplementary Figures, are absent or meager. The authors need to expand these to describe all features present in the Figures.

Reviewer #3: Attached

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

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Reviewer #1: None

Reviewer #2: No: the code is unavailable as far as I can tell

Reviewer #3: None

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Attachments

Attachment

Submitted filename: Review_PCOMPBIOL-D-25-00798 - (1).docx

Dear Dr Alsina,

We are pleased to inform you that your manuscript 'Model-based inference of cell cycle dynamics captures alterations of the DNA replication programme' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Jing Chen

Academic Editor

PLOS Computational Biology

Mark Alber

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors have addressed my concerns, thank you

Reviewer #2: The authors have adressed all my concerns and the alterations have made have drastically improved their work. I am in full favor of acceptence.

Reviewer #3: The authors respond to my comments successfully.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: Yes: Jeroen van den Berg

Reviewer #3: No