Dear Dr. Ruess,

Thank you very much for submitting your manuscript "Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level" 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.

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).

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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,

Christopher Rao

Associate Editor

PLOS Computational Biology

Jason Haugh

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: The paper by Davidovic et al proposes a method for performing inference of parameters of stochastic reaction networks from time series data. The paper contains a theoretical description of the algorithm as well as extensive validation on simulated data and on one new experimental data set. Overall, the paper is well written and very clear, although cosmetic improvements to the notation could be made. My major reservation though is that I do not understand why the methodology should be considered new. As far as I know, the idea of performing Kalman filtering with an approximation of the transition probabilities for stochastic reaction networks has been initially proposed in an overlooked paper by Ruttor and Opper (Phys. Rev. Lett. 103, 2009) over ten years ago. Several others have followed suit. It is true that several papers use the LNA or similar in an open loop approach, but it is well known that this is simply wrong (see e.g. the popular review by Schnoerr et al in J.Phys.A 2017, sec 6.2-6.4, in particular the incipit of 6.4.1 which very clearly explains how the problem should be formulated in terms of approximating the transition probability in a forward-backward algorithm, which is what the authors of this paper do). Having said that, many of the empirical results of this paper are interesting, e.g. the study of how sampling time affect accuracy, and also and perhaps primarily the real data study. I think the authors should: either be much clearer about the novelty of their method (in case my diagnosis above is wrong), or completely restructure the paper, removing claims of algorithmic novelty and focussing on the analysis and the data part.

Reviewer #2: The manuscript presents an interesting extension of the moment based methods for inferences of parameters governing molecular processes inside single cells. It offers to bypass disadvantages related to a fixed form of transition probabilities by coupling inference to a Kalman filter that is updated over time. The applicability and advantages of the approach are well documented using synthetic and experimental data (opto-genetic gene activation system in E. coli). The text is well written and easy to follow. My critique should include to major points:

From my understanding, the different responses of single-cells in section 3.2.3 are assumed to result from noise that is modelled by LNA and Kallman Filter. This is however not necessarily (most likely in my view) the case. Most likely the differences result from differences in copy number of molecular species involved in the system (often referred to as extrinsic noise). If my understanding is correct then treating the data like this is misleading. Several approaches can be easily found that model extrinsic variability.

I am not sure how novel the ideas to couple Kallman Filter into LNA/Moment based inference is. This should be explained in more detail in the introduction.

Reviewer #3: Please see the attached.

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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 #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: 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.

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Attachments

Attachment

Submitted filename: report.pdf

Dear Dr. Ruess,

Thank you very much for submitting your manuscript "Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level" 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.

All three reviewers recommended publication. However, reviewer 3 offered two minor suggestions for improving the manuscript.  We ask that you consider these recommendation before we accept that final version of the manuscript. 

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,

Christopher Rao

Associate Editor

PLOS Computational Biology

Jason Haugh

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: The authors have done a good job in repositioning the paper to highlight the more novel and interesting aspects. I'm happy for it to proceed as is

Reviewer #2: All concerns were addressed to my satisfaction.

Reviewer #3: The authors have carefully revised the manuscript to address my concerns regarding the previous version. Most importantly, the revision has emphasized more clearly the contribution of the paper, which is to show how new experimental platforms that produce multiple single-cell trajectories measured at high frequencies may enable more precise and efficient identification of model parameters, and how methods based on LNA and moment closure, which are notoriously inaccurate for other types of data, may become serious contenders for the new data types. In my opinion, this is an interesting insight that deserves publication.

While the current version is acceptable, I have minor suggestions that in my opinion could help improve the clarity of writing.

Minor comments

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1. It appears to me the current title of the manuscript still seems to put the reader’s focus more on the “parameter inference” part rather than the new experimental data type and empirical results that the authors aim to emphasize. For example, how about “New perturbation experiments parallelized at the single cell level enables efficient parameter inference for stochastic biochemical models”?

2. On line 601, the authors claimed that "it has not been recognised that iterative likelihood evaluations may lead to vastly improved precision if the chemical master equation needs to be approximated". I have reservation about this claim, since it seems in much of literature on parameter inference of stochastic models that iterative approaches (such as particle filtering, particle marginal MCMC...) are the methods of choice when dealing with time-series data. The main question is what simulation/approximation of the CME (SSA, FSP, LNA...) is effective and efficient on the data at hand. Admittedly, when the data were sparse with long time intervals between measurements, many methods became either very slow or imprecise.

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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 #2: Yes

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 #2: 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

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. Ruess,

We are pleased to inform you that your manuscript 'Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level' 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.

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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,

Christopher Rao

Associate Editor

PLOS Computational Biology

Jason Haugh

Deputy Editor

PLOS Computational Biology

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