Dear Dr. Våge,

Thank you very much for submitting your manuscript "Individual-based model highlights the importance of trade-offs for virus-host population dynamics and long-term co-existence" for consideration at PLOS Computational Biology.

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Sincerely,

Amber M Smith

Associate Editor

PLOS Computational Biology

Ville Mustonen

Deputy 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: Pourhasanzade et al. present an interesting manuscript studying how bacteria-phage interactions mediate long-term coexistence of the bacteria and phage. The authors develop a discrete-time individual-based model that includes potential tradeoffs between bacterial growth rates and their ability to defend against infection by phage. They compare modeling results to those of simple infection experiments of E. Coli and a phage at different VHRs on plates.

The population dynamics of E Coli are shown in figure 3. For higher VHRs, the bacteria dynamics are interesting in that the bacteria initially increase then decrease (presumably due to the bacteria-phage interaction) before increasing yet again later on around hour 20. Under certain parameter regimes, the model successfully recapitulates the qualitative nature of these curves.

The individual-based simulations include bacterial growth, death, division, mutation, and virus-host interactions. Generally, I felt that the description of the model could be clarified; perhaps a simple schematic that illustrates the various steps of the model would help a reader understand each element of the model.

I was confused by a couple aspects of the model. I did not see the time step given (is it one hour?). A couple of issues: the parameter pi_h is the probability of mutation of host genotype—surely this must depend on the time step chosen. The parameters delta_h and delta_v are stated as mortality (or decay) rates with units per hour in table 1, but then in the text description (line 76 and line 89) these same parameters are treated as probabilities.

While the model is of course highly idealized, it is still quite complex and contains a large number of parameters (table 1). Many of these parameters are fixed at very specific values given by the study in reference 21. While some sensitivity analyses were performed for a few parameters, it is difficult to know whether model results are specific to this system, or whether results are more general. A broader exploration of parameter space may clarify this.

A major component of the model is the inclusion of bacterial evolution. When a cell divides, its genotype changes with some probability. These genotypes determine the compatibility of the virus and the bacteria through the function C_t. Yet it was difficult to see the connection to data, or to disentangle the effects of evolution from the innate structure of the model (the ecology) in determining population dynamics. Figure 3 shows E Coli population dynamics, but I did not see any sequencing or anything to show whether these dynamics are driven by evolution on the time scale of the experiment. It is difficult to know to what extent evolution is driving these dynamics in the experiments. Moreover, all results in figs 4-6 show population dynamics over time. It is difficult to know to what extent evolution is driving these dynamics in the model. Also, phage population dynamics were not measured, so these cannot be directly compared to the results of the model.

Minor issues:

I would say that Nh and Nv are state variables and not parameters as stated in Table 1.

Typo on line 289 “nutrientt” instead of “nutrient”

I thought the figures could be improved by adding labels to the legends so that the reader would know what is being varied without having to read the caption.

Many of the figures are means of many replicates. It might be nice to shade to show the amount of variation in replicates (standard deviation or similar).

Reviewer #2: See attachment.

Reviewer #3: This paper investigates the hypothesis proposed by others (who are cited in the introduction) that a trade-off between bacterial host competitiveness and host defense against viruses will result in a persistent population of both virus and host. A model is described that takes into account mutation rate, nutrient availability and virus infection efficiency and the results compared to those of an in vivo experiment performed in E. coli with a T-4 like phage. The inclusion of the in vivo experiment is a major strengths of this work in my opinion.

The model parameters are clearly described in tables. The code is not included but is stated to be available upon request. Figures consist of graphs of parameters over time either in the live experiment or the model runs.

Figures are generally clear, but there are ways they or their presentation could be improved.

• It is stated that the model is validated with laboratory data from a short-term infection experiment. However, the in vivo experimental result (Figure 3) and model result (Figure 4a) for host abundance differ considerably at the lowest VHRs and this does not seem to be addressed in the text.

• Methods state that multiple repetitions were done, but no indication of variation between runs is indicated on the graphs and no raw data are provided.

The authors state clearly that their model is “highly idealized, focusing on specific trait-based mechanisms.” However I found myself disagreeing with two assumptions that the investigation is based on, and this lessened my enthusiasm for the work.

Assumption 1: Evolution (genetic diversification) of bacteria requires the long-term co-existence of bacteria and phages.

While it is true that predator-prey relationships are a source of selective pressure that leads to genetic change over time, it is not the only source of selective pressure and therefore not necessary for evolution. Competition for nutrients, as included in the model, are just one of many other selective pressures. The fact that viruses exist and affect population dynamics of their hosts, to me, is not evidence that they are necessary for a healthy ecosystem.

Assumption 2: There is always a trade-off between the (bacterial) host’s growth (competitiveness) and its abilities to defend against the virus. i.e. an increase in virus-resistance host will always result in slower reproductive rate.

If taken to the mathematical extreme, doesn’t this assumption imply that a host with 100% virus resistance should be completely non-competitive with regard to growth? But absolute resistance to phage is known to exist in viable bacterial strains, which means in a single virus-host pair scenario, the virus would go extinct. Doesn’t this disprove the single host-virus pair model?

minor typo: virues is missing an "r" in the abstract

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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: No: I did not see that the code for the simulations and the raw data from the laboratory experiment are made available.

Reviewer #2: No: Code is available upon request, but could probably be made available more immediately via SI or online repository.

Reviewer #3: No: The data points behind the plotted means and variance measures were not provided and there was no indication of a publicly accessible repository of the information or code.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Figure Files:

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Attachments

Attachment

Submitted filename: PLOSCB.pdf

Dear Dr. Våge,

Thank you very much for submitting your manuscript "Individual-based model highlights the importance of trade-offs for virus-host population dynamics and long-term co-existence" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Amber M Smith

Associate Editor

PLOS Computational Biology

Ville Mustonen

Deputy Editor

PLOS Computational Biology

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

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

[LINK]

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: I thank the authors for their detailed responses. Most issues were adequately addressed. The authors now include supplementary pseudocode, a GitHub link to their code, and a more detailed model description, all of which make it far easier for a reader to follow their methodology. The figures are also greatly improved. One minor remains issue with the figures: Fig 3-5 are missing an explanation as to the meaning of the shading.

A key limitation that still exists is that phage population dynamics are not measured so that they cannot be directly compared to model results. In lines 284-287 of the discussion, this is touted as an advantage of the model. But I would argue that it is only an advantage if the viral population dynamics are independently verified with a rigorous comparison with data. Before such verification, the phage population dynamics seem speculative. In my view, such measurements are not needed for publication of this manuscript as the authors already state the challenges associated with obtaining such measurements. However, I do think that this limitation should be acknowledged.

Additional minor issue: Figure S2 does not appear to be referred to in the main text.

Reviewer #2: The authors seem to have addressed the majority of reviewer questions and concerns. While we might want a few more answers now, this paper seems to lay the groundwork for followup work.

I am satisfied with the revision and especially updates to the model description, tables/notation, and figures.

** I missed this the first time, but on line 145 a dot/bullet symbol is used in some chemistry notation. Not sure if this is standard.

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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

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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

Figure Files:

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. 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 us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your 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

References:

Review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Dear Dr. Våge,

We are pleased to inform you that your manuscript 'Individual-based model highlights the importance of trade-offs for virus-host population dynamics and long-term co-existence' 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.

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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,

Amber M Smith

Associate Editor

PLOS Computational Biology

Ville Mustonen

Deputy Editor

PLOS Computational Biology

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