PCOMPBIOL-D-25-02183

Coevolutionary dynamics of viruses and their defective interfering particles

PLOS Computational Biology

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Tyler Cassidy

Academic Editor

PLOS Computational Biology

Zhaolei Zhang

Section Editor

PLOS Computational Biology

Additional Editor Comments:

Both reviewers appreciated this mathematically driven manuscript and raise important points to be addressed in revision. While mathematical models, such as the one presented in this work, can drive new biological knowledge and lead to novel insights into the underlying biological systems, I believe it is extremely important to justify the validity of the underlying mathematical model and clearly demonstrate that the conclusions being drawn reflect the biological reality, rather than arising from the mathematical model studied. Thus, this manuscript may require significant revisions to further establish the robustness of the model-derived conclusions.

In particular, it is not clear how the model parameters were chosen, nor how these could be inferred from the experimental system described in Figure 1. As many of the results in this manuscript are obtained via model simulation, it is extremely important to justify where these parameters are coming from, and why they are appropriate, though I appreciate that the authors have performed a 1 by 1 sensitivity analysis. This is particularly relevant to ensure that the conclusions drawn in this manuscript drive new biological understanding, rather than mathematical artifacts of the model framework chosen. Consequently, it will be important to fully address Reviewer 2's 2nd point. Similarly, it is important to further justify the modelling decisions made when developing the phenotypically structured model. While the current modelling choices may allow for an analytical investigation of the model (as reviewer 1 notes), as the model is not validated against experimental data, it is increasingly important to either strongly justify these modelling choices or show that the biological conclusions drawn from this model are robust to alternative model formulations.

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1) Please ensure that the CRediT author contributions listed for every co-author are completed accurately and in full.

At this stage, the following Authors/Authors require contributions: Shiv Muthupandiyan, and John Yin. Please ensure that the full contributions of each author are acknowledged in the "Add/Edit/Remove Authors" section of our submission form.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: See attachment

Reviewer #2: In this interesting paper, the authors consider a model of defective interfering particles (DIPs) that interfere with the spread of a viral infection. I had not been aware of these prior to reading this paper. They develop a model of how the viruses evolve in the presence of DIPs and examine and discuss the resultant dynamics. From a dynamical systems perspective the model has similarities to predator-prey models, as the authors point out.

This is a well written paper which explains the model and results clearly. The model development seems logical, with the different modelling components explained carefully and well within the Methods section. The results seem plausible and the paper has a clear and convincing narrative accompanied by very informative figures. Figures 3 and 4, and the accompanying videos in the SI, nicely illustrate the dynamics effects that can occur, including the chase scenario that they describe. Figure 5 shows the effect of varying parameters; the outcome of the low washout rate in particular leading to greater extinction probability was initially surprising to me until reading the explanation.

I have a few points where I would like the authors to clarify and potentially amend the paper but otherwise I think that this is a very good piece of work that warrants publication in PLOS Computational Biology.

1) Equations (12,13) are almost identical to equations (10,11). The only difference is that the parameter rV is replaced by 1, a scaling that could easily have been done at the start by suitably defining the time units. Thus it seems that there is some unnecessary work towards the end of the Methodology section, which hopefully the authors can streamline.

2) At the bottom of page 7 some specific numerical values are selected. Why these numbers in particular/ how robust are results if these are changed? We see later in the paper what happens when single parameter values are varied in turn, but what if a completely new set of parameters was used?

3) In Figure 6 and its associated caption, the terms "fold reduction" and "fold resistance" are used. These are mentioned nowhere else, however, and not explained. What do they mean and are these different things? What is their relevance?

4)In Figure 7 A, can the authors explain the meaning of the line crossings, and why they occur?

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes: Chiara Villa

Reviewer #2: No

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Attachments

Attachment

Submitted filename: PCOMPBIOL_Review_Report.pdf

Dear Dr. Yin,

We are pleased to inform you that your manuscript 'Coevolutionary dynamics of viruses and their defective interfering particles' has been provisionally accepted for publication in PLOS Computational Biology.

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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,

Tyler Cassidy

Academic Editor

PLOS Computational Biology

Zhaolei Zhang

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 #2: I liked the first version of this paper and the authors have addressed the small number of points from my previous review well. The only thing I saw in the new version for them to look at is that in the caption of Figure 1, the left panel is mentioned, but not the right one.

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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 #2: None

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Reviewer #2: No