Dear Dr. Cvijovic,

Thank you very much for submitting your manuscript "A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism" 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. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

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[1] A letter containing a detailed list of your responses to the 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).

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Thank you again for your submission. 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,

Attila Csikász-Nagy

Associate Editor

PLOS Computational Biology

Jason Papin

Editor-in-Chief

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: In this work, the authors present a hybrid yeast model combining signaling, regulation and metabolism. The model offers advantages over purely metabolic models by allowing to analyse the effects of nutrient sensing. The final model is a combination of a signaling and regulatory network for carbon and nitrogen sensing, manually curated by the authors, with a core metabolic model of yeast metabolism obtained by reducing the network of the yeast gecko model. Overall the paper presents a methodological advancement in the combination of boolean-based signaling model with a protein-constrained metabolic model. While the scientific aspect of the manuscript seems solid, and of general interest, for me the main issue is the writing and structure of the manuscript. I found the text not very well written and difficult to follow. I believe the manuscript would benefit from substantial rewriting. Please see further details bellow.

Major issues:

* In the introduction the reader is only pointed to a diagram that doesn’t explain much of the simulation method. The details of simulation are explained at the end, but a brief description (pointing to the methods section for a more detailed description) should be given before explaining the main results.

* Each subsection of the results immediately begins with an enumeration of results. A bit more context into the motivation for each part, and an explanation of why these particular simulations were selected with respect to that motivation, would better help the reader to interpret the results.

* I don’t understand why are there only 3 figures in the main text and all the others go into supplementary material. Some of these figures are essential for understanding the method and the main findings in the paper.

* The supplementary file itself is very confusing. There are full paragraphs of text mixed with figures and tables. This file needs to be better organized into supplementary figures, supplementary tables and supplementary text. Also, a lot of the supplementary text contains interpretation of results and discussion. There is no explanation given to the reader at any point on why some of the text is moved into supplementary and what one should expect to find there.

Minor issues:

* Please explain how the thresholds are decided for converting the glucose and nitrogen concentrations into binary values.

* Please specify the growth medium used during simulation.

* In lines 231-239 there are multiple p-values presented. Please mention also the statistical test used, sample sizes, effect size, and the respective test statistic.

* Fig 3A looks very suspicious, some values reach almost 10^-10, which is probably below the solver precision. Log-changes are a very strange way to compare experimental and simulated fluxes. Why not use the mean squared error (MSE) instead ?

* Fig 3B: why showing only these 3 proteins when there are more proteomics data ?

* Fig 3D: the net flux for PGK and GPM seems higher than futile flux, how is this possible?

Reviewer #2: This paper aims do provide a novel hybrid modelling framework to integrate (nutrient-induced) signalling, regulation and metabolism, which is applied to yeast and used to perform some simulations.

The overall idea for the framework is well designed and the goal is worthy of research, being a major aim of systems biology. However, I believe that the work has several limitations, mainly regarding the results presented, which do not seem to justify the authors' claims.

Firstly, regarding the model, it should be clear from the beginning this not a genome-scale model, even in the metabolic part and that the signalling and regulatory layers are very limited. For instance, this is not clear in the abstract of the work.

Also, the reported simulations are very limited and provide results that are not convincing. The errors provided for the proteomics datasets are all in the high orders of magnitude (2 to 4); while improvements are visible the obtained errors are still around 100 fold. Also, the reported use of iso-enzymes is only somewhat visible in fermentation.

Finally, the Crabtree effect reported was already reported for GECKO models alone, so it is not clear where the improvement is. The relationship to ageing is also not convincing and its significance not well explained.

So, although I value the work on the framework and the thorough work on the signalling part of the model, the overall result does not seem to justify the publication.

There are also some language problems; the overall paper should be read, as there are a few typos (e.g. investigat - 28; singaling - 39, trough - 548) and in many cases sentences are confusing missing commas to make them more readable (as an example consider the sentence in rows 89-90). The language in S1 text is even less carefully written (consider one example of a sentence: "As can be seen by the summery statistics, prediction of individual proteins are difficult. ").

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: None

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Dear Dr. Cvijovic,

We are pleased to inform you that your manuscript 'A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism' 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,

Attila Csikász-Nagy

Associate Editor

PLOS Computational Biology

Jason Papin

Editor-in-Chief

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: The authors successfully addressed by main comments in the revised version.

I still believe the language issues are not completely solved (it has improved) and a re-read of the manuscript could help.

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

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 #2: No