Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

Dear Dr. Lopez,

Thank you very much for submitting your manuscript "Predictive uncertainty in mechanistic models of cellular processes calibrated to experimental data" 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.

As you will see, one reviewer, in particular, had very strong concerns regarding a lack of diligence in the preparation of the manuscript. It would be important to address these concerns.

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,

Martin Meier-Schellersheim

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 authors make two significant contributions in my estimation. First, they extend the Bayesian inference methodological concepts of Ref. 12, wherein categorical observations of continuous variables are considered for use in model parameterization. In that work, the categorical observations are viewed as arising from a classifier function which has known threshold parameters. In this manuscript, the authors generalize, considering other types of functional relationships (GP/logistic) between observations and model/process variables and considering a wider range of combinations of observational data types. The authors argue that the function relating observations and model/process variables can be anything, such as a universal function approximator, and that the function may have different kinds of parameters besides thresholds, which can be learned jointly with model parameters. I think these are important ideas. The second contribution is that the authors provide another demonstration of how non-quantitative observations can be used to estimate model parameter values. There is some earlier work in this area but not much and the new demonstration differs from those provided earlier in several ways. The weakness of the demonstration is that it relies on synthetic data but this is offset by using an empirically-calibrated model to generate the synthetic datasets.

Minor:

In the second paragraph of the Introduction, when discussing earlier work, it might be an improvement to include a clearer objective summary of earlier approaches and only then to present an evaluation of their limitations. It seems that the authors are criticizing the methodology of Ref. 12 (closest to the authors’ own work) because in that work thresholds are taken to be known. This is an assumption that can easily be relaxed in principle, so it is not a severe limitation. It is certainly fair to say that taking a threshold to be known can introduce bias, but it should probably be mentioned that taking some parameters to be fixed is commonly done in inference problems to make the inference problem practically solvable. It is stated that other earlier work also has limitations but these limitations are somewhat unclear to me from just reading the text. The limitations of all earlier methods discussed should be clarified, perhaps through the discussion of concrete examples.

Consider defining the following terms in the text upon first use: nominal (binary categories?), ordinal (multiple categories?), semi-quantitative (e.g., relative measurements?), and quantitative. The discussion in the first paragraph of the Results section about the merits of the different data types could be made more interesting by saying more about the data. The discussion currently focuses on the value of the data, without saying much about the data.

The concepts of “measurand” and “measurement” should be more clearly defined when these terms are first brought up.

The comment “we introduce a concept from statistics, and social sciences: the measurement model” is a big overstatement. Measurement models, such as Eq (6) in Box 1, are commonly used by biological modelers. The overstatement is repeated in the Discussion: “Our work introduces the concept of measurement models to the mechanistic modeling paradigm.” I think what the authors are doing is using the well-known measurement model concept to connect non-quantitative observations to model/process variables, which is innovative. The authors should rephrase for precision and to avoid overstatement.

I would like to see more information about the MCMC sampling, such as representative parameter and likelihood trace plots and pairs plots.

I am concerned about a sentence in the Abstract: “We find two orders of magnitude more ordinal (e.g. immunoblot) data are necessary to achieve accuracy comparable to quantitative (e.g. fluorescence) data.” It should probably be mentioned that this finding is likely to be problem-specific. The authors really only consider one demonstration problem. Consideration of many problems would be needed to start making general conclusions.

Reviewer #2: This paper adds to the growing but small community of modelers who are focused on the role of qualitative data as well as more model directed experimentation, something one might term model driven rather than a data driven approach. A model driven approach is I feel exactly the right approach. The current paper adds to the literature by conducting a more thorough analysis of the role of qualitative data. I was very interested to see a discussion of the concept of measurement model. The theory of development models and the role of data is not discussed very much, if at all in systems biology, and if anything, this paper will introduce the community to this more nuanced and important concept.

Overall, this was an interesting paper, its not a definitive analysis (as the authors admit) but it would be a useful addition to the literature and will hopefully stimulate further similar studies.

Minor:

Line 105. I would add some brief definitions of nominal, ordinal etc. to the text. I understand that Figure 1 describes these terms, but the caption is lengthy. Maybe something along the lines:

“nominal (data is categorized), ordinal (data is categorized and ranked),”. For semi-quantitative and quantitative, I’m don’t quite understand the difference. I would attempt to clarify these two terms more, but I recommend being succinct. Is semi-quantitative unit less data, e.g ratios and quantitative data with units? I wasn’t sure from the text.

Line 144: “The model was calibrated to above fluorescence data”, missing word somewhere?

Line 249: “As described in Methods, we added a synthetic 249 dataset containing 61 ordinal time-course measurements” I didn’t immediately quite understand how time-course measurements can be an ordinal data type (this is briefly explained in the methods section). Could the authors point to the method section (line 631 in the manuscript I assume?) where this is described, for example: As described in the Methods x.y, we added a synthetic…”. The authors indicate in the methods section that: “These time-courses were converted to ordinal time-course datasets”, from reading the text I assume the time course data was just turned into a set of ranked data but without a quantitative time dimension?

I recommend that wherever the main text says “See Methods” or similar, I would be explicit about where in the methods section the reader to go to, this would greatly help the reader.

Line 269-270: “but this time we replaced the 270 free parameters in the measurement model fixed a priori parameterizations”, text doesn’t read correctly? Now sure what is being said here.

Conclusion: I would include in the conclusion a series of bullets points outlining the basic recommendations and results obtained from the work. I think many readers might find it difficult to extract the key conclusions as the text is fairly dense. I think there are perhaps three primary conclusions from the work, these should be corrected or reworded if I am inaccurate here: 1) A lot more ordinal data is required to constrain a model; 2) Prior assumptions about parameter values can bias a model; 3) Use the model to devise the most profitable data to collect.

Line 766: Typo in “We Consider”, Figure 1

Last but not least, the GitHub link for the osurce code appears to take one to a 402 page.

Reviewer #3: review is uploaded as an attachment

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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: No: GitHub link appears to be broken.

Reviewer #3: None

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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

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

Attachments

Attachment

Submitted filename: review_plos_cb.pdf

Dear Dr. Lopez,

Thank you very much for submitting your manuscript "Model certainty in cellular network-driven processes with missing data." 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 particular, in light of the review comments provided by reviewer #3, we would like to invite the resubmission of a significantly-revised version that takes into account that reviewer's comments.

Please note that many of the issues mentioned by reviewer #3 should have been addressed in the first revision you submitted to us and that, in general, it is part of the responsibilities of the authors to minimize errors that can be easily detected when proofreading the 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 back to reviewer #3 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,

Martin Meier-Schellersheim

Academic Editor

PLOS Computational Biology

Jason Haugh

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: This manuscript is interesting. Revisions made to address my concerns about the original version are deemed to be adequate.

Reviewer #2: The authors have address my main concerns. I have no further comments. Should be a useful contribution to literature.

Reviewer #3: Review is uploaded as an attachment.

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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

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: Yes: Herbert M Sauro

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

Attachments

Attachment

Submitted filename: review_revision1.pdf

Dear Dr. Lopez,

We are pleased to inform you that your manuscript 'Model certainty in cellular network-driven processes with missing data.' has been provisionally accepted for publication in PLOS Computational Biology.

Please make sure to correct the issue commented on by the reviewer (below).

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,

Martin Meier-Schellersheim

Academic Editor

PLOS Computational Biology

Jason Haugh

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 #3: line 446: Eq. 21 should be Eq. 22

**********

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: None

**********

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