Dear Dr C. de Oliveira,

Thank you very much for submitting your manuscript "A flexible generative algorithm for growing <in silico=""> placentas" 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.</in>

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).

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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,

Alison Marsden

Academic Editor

PLOS Computational Biology

Pedro Mendes

Section Editor

PLOS Computational Biology

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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:

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: This paper introduces a novel generative algorithm for creating in silico placentas. One of the advantages of the proposed algorithm is that it allows direct control over morphological parameters, a limitation of previous volume-filling algorithms.

There are some details relating to the validation of the algorithm that could be improved.

METHODS

M.1. What is the rationale for the tolerance choice in the generation of the chorionic and villous vessels?

M.2 The description of some of the rules for tree generation lacks a reference or rationale. For example,

Line 738: To account for empty space to be occupied by venous vessels, we assume that if DLij > 7/2 (di + dj ), the generated daughter branch is accepted; otherwise, the algorithm proceeds to step 4.

Or

Line 729: The ROI is defined as a sphere of radius (3/5)ld,

Can the authors explain what this is doing exactly:

Line 744: The surface meshes are then converted to convex meshes, which represent collision geometries, and tested for collision using built-in functions from the Robotics System Toolbox.

M.3. The point candidates for branching seem to be bounded to the initial mesh defined for the placental surface. How is this mesh generated? Does the point density (i.e., number of point candidates) affect the model's output? If so, how does it compare to the variabilities shown in Figure 11?

M. 4. Why is the placentone defined as a cuboid? An ellipsoidal or circular cylinder seems a more appropriate choice to describe this structure.

RESULTS

R.1. Some parameter ranges from the literature are missing in Table 4.

R.2 About half of the parameters presented in Table 4 to evaluate the morphology of the generated trees are from in-silico studies (mostly one study; the other in-silico study mentioned is a quantification of micro-CT images). In the study from Clark et al. 2015, the authors use for validation, the placental vasculatures are generated using a volume-filling algorithm. According to the authors, the limitations of the volume-filling algorithm are precisely what motivates the generative algorithm presented in this paper. Hence, it seems contradictory to use results from the implementation of Clark et al. 2015 as validation for their tree generation algorithm. It is this reviewer's opinion that the authors should limit validation to in vivo or ex vivo data.

R.4. What is the inner polygon of Figure 12 (and 14) representing?

R.5. The results highlight the crucial role of cf1 for the branching angle outputs. However, this metric is not reported in the literature, and it is provided by the authors to the model. How are the ranges reported in Table 1 defined? This is also not included in the discussion.

R.6. Can the authors provide additional details and interpretation of the results obtained regarding the "variability in output metrics from multiple algorithm runs"? What is exactly plotted in figures a) and b)? Is this divergence acceptable?

R.7. Not all the output variables included in Figures 7, 8, 9, and 10 seem to be included in Table 4. Also, the name used is different in some cases, which adds confusion.

Other comments:

Figure 7and 9, add label in the color scale

Image resolution of Figure 7, 8, 9,10, 11 and 13 is not optimal.

Line 322: missing word (Section")

Lines 349: missing words (more details in Sections "and")

Line 631: missing word (Section")

Reviewer #2: The manuscript presents a flexible generative algorithm for creating synthetic feto-placental vasculature (arterial trees so far) with biophysical models and user-defined control. Compared with previous generating algorithms, a substantial advance of the algorithm presented herein is to allow direct control of the vasculature morphological parameters (e.g. vessel dimensions, branching angles) for designated feto-placental network characteristics reflecting healthy or pathological feto-placental structures, with a notable feature of stochastic variability for a given set of input parameters. The algorithm itself is introduced in detail with clear physiological underpinning and demonstrated through clinically-relevant examples, together with comprehensive quantification and sensitivity analysis. Overall, this is a well-motivated in silico framework and offers a new avenue for investigating the structure-function relationships in the placenta. With further developments to incorporate venous trees and complete capillary pathways, the framework can pave the way for more efficient organ-scale placenta modeling and inform diagnosis of placental disorders in the future. However, certain aspects of the manuscript need to be improved to enhance its clarity and impact, which are detailed below.

1. The stochastic nature of the algorithm is a desired feature to reflect biological variability but may also impose challenges on reproducibility. Does the algorithm have a mechanism (e.g. random seed generator) to reproduce a designated vasculature generated with a given set of user-defined parameters?

2. The authors should also discuss the implications of the modelled stochasticity in a biological context as the realistic vascularisation process of the feto-placental network is unlikely to be purely random but rather orchestrated through mechanical/chemical signals during pregnancy. Also, it would be nice if the authors could clarify how the level of stochasticity-induced structural variation compares to the absolute sample differences associated with pathologies versus physiology.

3. Does the algorithm support both dichotomous branching and monopodial branching (as shown in the Fig 2b schematic)? And if yes, does it have a preference for either branching mode in different scenarios, say non-central v.s. centralised umbilical artery insertion? I suspect that unlike the predicted outcome in Fig 12c (only marginal difference in the mean vascular density but not other topological metrics), the structural differences for non-central and centralised insertion in real placentas may be more severe? Or are the topological metrics adopted here robust enought to tell the structural difference?

4. The whole feto-placental vasculature or placentone villous tree is generated using morphological parameters known a priori in literature, but there seems no validation or evaluation of the generated in silico replica against the biological counterpart where the morphological parameters were drawn? Is there a way to achieve such validation at this stage without functional inspection, which I understand will need blood flow simulations in the generated network (therefore out of this paper’s scope) to compare with clinical imaging data (e.g. flow rate, oxygenation measurements)? It would also be nice to discuss how clinical phenotypes have been reproduced, e.g. hypo/hyper-branching patterns.

5. More details and analyses about the healthy/pathological placentone fetal trees in Fig 15 should be given to showcase the potential workflow from medical imaging data to in silico placenta.

Some figures and text also need to be polished:

Fig 1, the zoomed-in are (black box) in (a) does not quite correspond to the lobule detailed in (b). Also in (b), may use normal arrows to replace the dot-line pointers for indicating the flow directions.

Fig 3, the use of numbered sections in the diagram is an issue as the sections in the main text are not numbered.

Fig 5, consider modifing the diagram flow to better reflect the decision tree from step 3 to step 6.

Fig 6, the feto-placental vasculature is not fit to size and somehow mispositioned in the coordinate system.

Figs 8, 10-11, the line plots are blurred when printed out. Consider a darker line colour. Also in the captions of Figs 8 & 10, “Clusters... associated with higher mu* and lower sigma values, are circled in black,” is “lower sigma values” the case?

Figs 12c, 14, explain the base coordinates of the radar plots, which are sometimes non-zero.

Double check all figure captions and make sure the concerned symbols are explained.

Currently, the main text is quite dense and sometime difficult to navigate due to a certain level of redundancy. The text may benefit from streamlining the contents in the Model, Results and Method sections.

Table 3, double check “settings from 6-13.” It is unclear to me how the parameters in table 3 lead to a round (rather than oval) chorionic plate in Fig 12a, b.

Page 18 line 454, provide references for “consistent with previous reports.”

Some cross-references are not working, e.g. missing section titles on page 13 lines 316 & 322, page 14, line 350, page 30 line 631.

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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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Reviewer #1: No

Reviewer #2: Yes: Qi Zhou

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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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 C. de Oliveira,

Thank you very much for submitting your manuscript "A flexible generative algorithm for growing in silico placentas" 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.

The manuscript is almost ready, but we need you to address this issue: "The authors should include quantitative data supporting the heuristic determination of parameters and parameter ranges in the supplementary". Of course you should try to address other issues in the attached reviews as much as possible.

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,

Pedro Mendes

Section 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:

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 provided additional details on the methods, which facilitate understanding their approach.

The authors should include quantitative data supporting the heuristic determination of parameters and parameter ranges in the supplementary (e.g., tolerances, number of seeds, cf1, and cf2 …). These parameters have a critical role in successfully generating a physiological placental vasculature; supporting information on how they are determined will be essential for reproducibility.

On the topic of validation, this reviewer finds that validation with an in silico-generated vascular model may be insufficient. This is especially true in this case since the motivation for this new algorithm, according to the authors, is to address the limitations of previous in silico placenta vasculature generation algorithms.

References to the model's ability to represent psychological vascular trees should be limited to references that provide histological or image quantification of ex vivo data.  For example, the sentence:" Both chorionic vessels and villous tree structures are strongly asymmetric in branching, as given by Strahler branching ratios beyond 2.3, within the range of those presented in the literature [20]" is presented as a fact, while the citation refers to a modeling study. In such cases, the authors should only claim that their model produces results similar to those of the cited research, making it clear to the reader that both are computational results.

Although computational cost is mentioned several times in the manuscript, no quantitative data is provided in this respect. Please include a short comment referring to the hardware employed and the compute time required to generate the full vascular tree. If available, compare against previously published algorithms for vascular generation.

Reviewer #2: The authors have satisfactorily addressed my comments and the manuscript is significantly improved in this revision. I would recommend publication with an optional suggestion for the authors to consider: adopt consistent axial scales between the gridlines for each parameter in Figs. 12 and 14 to facilitate date interpretation.

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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

**********

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 C. de Oliveira,

We are pleased to inform you that your manuscript 'A flexible generative algorithm for growing in silico placentas' 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,

Pedro Mendes

Section Editor

PLOS Computational Biology

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