PCOMPBIOL-D-25-00318

Dynamic Modeling of Cell Cycle Arrest Through Integrated Single-Cell and Mathematical Modelling Approaches

PLOS Computational Biology

Dear Dr. Fröhlich,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript within 60 days Jun 24 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at ploscompbiol@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcompbiol/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

* A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to formatting updates and technical items listed in the 'Journal Requirements' section below.

* A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

* An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter

We look forward to receiving your revised manuscript.

Kind regards,

Ovidiu Radulescu, PhD

Academic Editor

PLOS Computational Biology

Pedro Mendes

Section Editor

PLOS Computational Biology

Journal Requirements:

1) We ask that a manuscript source file is provided at Revision. Please upload your manuscript file as a .doc, .docx, .rtf or .tex. If you are providing a .tex file, please upload it under the item type u2018LaTeX Source Fileu2019 and leave your .pdf version as the item type u2018Manuscriptu2019.

2) Please upload all main figures as separate Figure files in .tif or .eps format. For more information about how to convert and format your figure files please see our guidelines: 

https://journals.plos.org/ploscompbiol/s/figures

3) We have noticed that you have uploaded Supporting Information files, but you have not included a list of legends. Please add a full list of legends for your Supporting Information files after the references list.

4) Please amend your detailed Financial Disclosure statement. This is published with the article. It must therefore be completed in full sentences and contain the exact wording you wish to be published.

1) State what role the funders took in the study. If the funders had no role in your study, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

5) Please ensure that the funders and grant numbers match between the Financial Disclosure field and the Funding Information tab in your submission form. Note that the funders must be provided in the same order in both places as well. Currently " Ludwig Cancer Center at Harvard" is missing from the Funding Information tab.

Reviewers' comments:

Reviewer's Responses to Questions

Reviewer #1: The paper by Cortés-Ríos et al presents an integrated framework combining single‐cell multiplexed immunofluorescence data with mathematical modelling to decipher cell cycle dynamics under both oscillatory and arrest conditions. The authors extend the cc‐CMD pseudo‐time ordering method to integrate data from multiple experimental conditions, including treatments with palbociclib and nocodazole. By incorporating regulatory interactions such as p21/p27 inhibition of CDK‐cyclins and the impact of cyclin B on p21 degradation, the model successfully recapitulates the cell cycle progression of untreated MCF 10A cells and accurately predicts arrest states corresponding to early G1. Model calibration using both untreated and drug‐treated datasets reduced parameter uncertainty and yielded biologically plausible simulations. The predictive capacity of the model was further validated under growth factor starvation, demonstrating its potential for extrapolating cell cycle arrest conditions not included in the training set. The paper presently a sensible approach though I am not 100% sure of some of the assumptions made and the extensibility of the approach to other datasets.

1. The method is inherently based on the assumption of ergodicity, i.e. that the cell population is representative across all phases in proportion to the time spent in each phase—which may not hold true for unsynchronized cells or those grown under non‐ideal conditions. If this assumption fails, the inferred cell cycle times and phase transitions could be significantly skewed. Can the authors test the effect on their method’s output by using simulated data instead of experimental data, e.g. using the stochastic simulation algorithm, that is not in quasi-steady state conditions and that hence does not obey the ergodic hypothesis.

2. Another potential issue is the reliance on a single MCF-10A cell line and a restricted marker panel, which raises concerns about the generalizability of the findings to other cell types or biological contexts. Would be useful to apply to data beyond this particular cell line to remove these concerns.

3. The method appears to completely ignore the inherent large cell-to-cell variability in single cell data (as they use the moving median for the protein levels). While noise can be seen as a nuisance, actually there are many studies (especially in the scRNA sequencing literature) which point out that it possesses biological information that is better used rather than discarded. The authors should discuss how they would include this variability in the protein levels and the anticipated effect on their method and on the main results reported in this paper. In this context, the literature on the use of stochastic models of the cell-cycle (especially those used in conjuction with single-cell data) would probably be useful to consult and to reference appropriately.

Reviewer #2: The title of the manuscript is misleading: it should highlight that the main contribution of this work is methodological, and not on modeling of cell cycle arrest. As far as I understand, the authors show that using pseudotime techniques on multiple experimental data via appropriate training provides accurate and relevant results, when applied to the modeling of cell cycle arrest. This is certainly an important result, that would deserve publication, provided that its relevance is better motivated.

My detailed comments:

Lines 101-102, the authors claim to "develop novel model training techniques". Reading through the manuscript, it is unclear what is new, since the cc-CMD method is not new and already proved to be useful. Moreover, it is not clear what the extension of this method consists in. Since this is, in my opinion, the core of the current work, it should be clearer and discussed in more depth (advantages, limitations, etc.).

Lines 160-162, the authors claim their results "confirm" the relevance of CMD-based pseudo-time techniques, yet, as mentioned above, this was already known. Can the authors clarify what is confirmed by their results?

Lines 231-232, the authors mention that "the simulations for cyclin B and cyclin A were slightly outside the median absolute deviation range for a few datapoints", could they justify this result or at least hypothesize why this is observed? It is probably the most interesting unexpected result of their work, I believe it deserves more attention.

Lines 271-272, the authors wrote "our results demonstrate that training on data from multiple experimental conditions make it possible to reduce parameter uncertainty", I must say that it seems obvious: if you train on more data you reduce the uncertainty. What exactly is noticeable in the results? Is it that it actually works? Or how better it works, comapred to previous methods? Please clarify.

In the discussion, the authors argue (lines 304-306) that they "decided that a thorough exploration of the relationship between the particular pseudo-time embedding and the resulting mathematical model is beyond the scope of this study", so they do not investigate further the impact of using the pseudo-time technique in the resulting training of the models. I do believe, on the contrary, that this is in the scope of the manuscript. One cannot claim that a method is better than previous ones without investigating its limitations.

Finally, it is overall unclear what are the contributions of the authors: it seems it is a training accounting for multiple (here 2) conditions as well as a modificaton of the cell cycle model. Yet this is unclear, and I suggest it is clarified before a final decision is taken.

**********

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

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

Figure resubmission:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. If there are other versions of figure files still present in your submission file inventory at resubmission, please replace them with the PACE-processed versions.

Reproducibility:

To enhance the reproducibility of your results, we recommend that authors of applicable studies deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Dear Mr Fröhlich,

We are pleased to inform you that your manuscript 'Dynamic Modelling of Cell Cycle Arrest Through Integrated Single-Cell and Mathematical Modelling Approaches' 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.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

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

Best regards,

Ovidiu Radulescu, PhD

Academic Editor

PLOS Computational Biology

Pedro Mendes

Section Editor

PLOS Computational Biology

***********************************************************

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 done substantial changes to the manuscript in response to all reviewer comments. I am happy to suggest the paper's acceptance in its present form.

Reviewer #2: The authors have correctly revised their manuscript, the revised version is much clearer. I am satisfied with the authors answers to my comments. However, I believe the paper would have benefited from a more detailed discussion of the difficulties and challenges posed by the initial question and answered by the authors' work, and how their contribution would be relevant for a large community.

**********

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: None

Reviewer #2: Yes

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: No

Reviewer #2: No