Dear Prof. Phillips,

Thank you very much for submitting your manuscript "Dissecting endogeneous genetic circuits from first principles" 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.

Please address these concerns in particular:

(1) demonstrate the novelty of the results vs. previously published work from your lab and others

(2) underscore the limitations of the thermodynamic equilibrium approach

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

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

[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).

Important additional instructions are given below your reviewer comments.

Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

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,

Alexandre V. Morozov, Ph.D.

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: Pan et al. have attempted to develop a theory for massively parallel reporter assays (MPRAs) that tries to delineate the relationship between DNA sequence and phenotype. In particular, the authors used the so-called “thermodynamic model” of gene regulation to develop to develop a pipeline to characterize how the different biological and experimental parameters control measured MPRA data. These parameters include the transcription factor (TF) binding site copy number, limited TF resource, etc. These biophysical parameters dictate the degree of mutual dependence between mutations in the regulatory region and expression levels. The authors systematically characterized the effects of various parameters on MPRA data. Moreover, authors showed how to optimise MPRA experimental designs.

The paper is very clearly written. The approach of “developing a theory of the experiment” is particularly important in biology. The paper should definitely be accepted. I have the following comments that the authors can try to address.

Main points

1) A figure describing the model and a brief introduction to the statistical ensemble / distinct microstates involved would be nice for people unfamiliar with the background literature. This can go to the methods or the supplementary section.

2) I wonder if the assumption of equilibrium is universally applicable to analyse MPRA data. Is it possible to extend the model to consider the scenario where the detailed balance condition is broken for TF binding to regulatory DNA.

Minor Points:

1) What is Ns? In line 79 it is referred to as the number of non-binding sites, in 82 it is referred to as the total number of base pairs in the genome.

2) The reference in line 76 (1) should be (17).

3) In line 322, it is mentioned that when R1>R2, the signal at the R1 binding site is higher but in the figure 8C 3) it is lower. I think the authors mean to say R2<r1.

4) In Figure 8C, subfigures 5) and 4) have the same captions.</r1.

Reviewer #2: See attached PDF with the review.

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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: None

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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.

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Attachments

Attachment

Submitted filename: review_wenshan_pan.pdf

Dear Prof. Phillips,

Thank you very much for submitting your manuscript "Deciphering regulatory architectures from synthetic single-cell expression patterns" 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.

Please address all the reviewer comments carefully, including their suggestions on improving the paper's flow and presentation and on making the computational pipeline more accessible. This will be the last round of major revisions for this manuscript.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

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

[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).

Important additional instructions are given below your reviewer comments.

Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

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,

Alexandre V. Morozov, Ph.D.

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 adequately addressed all the comments.

Reviewer #3: In this paper the authors provide thermodynamic models for 6 simple promoter architectures that are commonly found in E.coli: one repressor, one activator, two repressors act together, two repressors that have an xor relationship and an activator, a repressor and double activation. The binding energy is derived from the sequence motif and it is assumed that only binding of the TF at the original site can induce or repress transcription. Binding sites are assumed not to overlap, but new binding sites may appear in the genome and thus compete with the original site for limited TF-proteins. The model appears to require a number of detailed input parameters such as the number of RNA-polymerase and Transcription factor molecules in a cell (maybe a table with required input parameters would be nice to have).

Based on this the authors can then construct a synthetic RNA-seq data-set that predicts expression for all variants of the simulated promoter. This data is then used to calculate the mutual information between the sequences and the expression level, which can in a real life MPRA be used to predict the binding sites. The authors exemplify the LacZYA promoter. Here it would be nice to compare the simulated data to what the real data from their earlier papers show.

Until here this sounds like a general enough approach that might also work in eukaryotes, even though some of the assumptions, especially the lack of epistatic interactions and the strong position dependence, are unlikely to be correct.

Next, the authors go through experimental design aspects that I feel are rather specific to microbial systems: Specifically finding the optimal mutation rate to identify the TF footprints. Mutation rate and sequencing depth go hand in hand, and the authors also provide advice on that. However, what I am missing here is a discussion about how much this depends on biological properties: i.e. the size and information content of the TF motif as well as the expression level of the associated TF. Only in a later chapter it is mentioned that those factors are a cause for differences in the detection power, biasing the detection towards highly expressed TFs with strong binding sites.

Importantly, since the authors frame the paper as a computational pipeline for experimental design of MPRAs, I would appreciate that the simulation results are translated into the more traditional terms of power analysis such as the AUC.

As is the simulation results provoke interesting thoughts, but I do not see how a research can translate them into actionable advice on MPRA design and interpretation.

The insights with respect to promoter architecture and TF-concentration as well as competition for binding sites also remain not more than interesting thought experiments. Moreover, they are highly dependent on the initial assumptions, such as the importance of the exact location. Also here, I do not see concrete advice on how these insights will be helpful to real life examples with unknown TF architectures. Can the simulations help to distinguish such scenarios?

Adding the non-equilibrium model at the end is nice, but again I don’t quite see the practical implications. If I understand correctly, only the simplest architecture is implemented and for some conditions the simpler and more flexible equilibrium model will suffice. Again a comparison of simulations to real life data would be helpful, to guide the user towards what model is appropriate.

Furthermore, the paper is too long, it should be possible to shorten. Each chapter centres on a specific scenario and then repeats a similar train of logic. It should be possible to shorten and only describe the implications.

Most importantly, the computational pipeline that I found on GitHub is a collection of jupyter notebooks that is rather sparsely annotated.

If this is to be of use to other people more work is needed here. At an absolute minimum the functions provided in tregseq require a detailed description including I/O.

In summary, given the limitations of the model both the introductory remarks and the discussion are vastly overstated. This paper could be of merit if a few results are clearly formulated and not buried between lengthy derivations and the overstated introduction and discussion. For most parts, this paper reads more like a review than the description of a computational pipeline as is claimed in the discussion. The authors need to decide on a focus and rewrite accordingly.

Minor comments

I find the title misleading. Even though a single cell is simulated, it is not intended for single cell MPRA analysis. Also the title should make it clear that the focus is on microbial systems not to mislead people like me who work on mammalian single cell genomics.

Many of the appendices appear to be basic repetitions of what is already in the main paper, this makes it extremely difficult to fin the relevant information. Again I could not find a real pipeline description.

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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: None

Reviewer #3: 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 #3: 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

Dear Prof. Phillips,

We are pleased to inform you that your manuscript 'Deciphering regulatory architectures of bacterial promoters from synthetic expression patterns' 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,

Alexandre V. Morozov, Ph.D.

Academic Editor

PLOS Computational Biology

Pedro Mendes

Section Editor

PLOS Computational Biology

Feilim Mac Gabhann

Editor-in-Chief

PLOS Computational Biology

Jason Papin

Editor-in-Chief

PLOS Computational Biology

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

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #3: Honestly, I found that the long responses to my first review rather confused me more than they clarified. The authors also repeatedly implied that the issue was my limited knowledge of the field. This might well be, I am not expert in thermodynamic modelling. I know about MPRAs from an application side and for somebody like me I don't see any application for the presented models. I don't have the time and the energy to go again through this extremely long and complex paper to argue point by point. Therefore I refrain from giving another recommendation.

**********

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 #3: 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 #3: No