PCOMPBIOL-D-25-00226

From imaging to computational domains for physics-driven molecular biology simulations: Hindered diffusion in platelet masses

PLOS Computational Biology

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Shugang Zhang

Academic Editor

PLOS Computational Biology

Marc Birtwistle

Section Editor

PLOS Computational Biology

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Reviewers' comments:

Reviewer's Responses to Questions

Reviewer #1: This is a well written paper that describes a computational pipeline to quantify diffusion of particles in 2D images that are segmented into cellular matter and surrounding pores. The value of this approach is its technique to quantify estimates of diffusivity in in-vivo imaged data. The authors acknowledge all limitations that came to my mind as I was reading the manuscript. I think this manuscript is worthy of publication after a few minor revisions.

1. The abstract misleads that "large stacks" of 2D images are being used to computationally model a 3D thrombus. in fact, this is why I agreed to review this manuscript. Please better communicate your actual approach.

2. The motivation and background section is superb.

3. Please clearly indicate in the text (earlier than the limitations section) that your difusion analyses are only for 2D planes.

4. 1000 particles diffusing for 0.003 sec seems really coarse and fast. Are the 1000 particle cross the entire domain or for each subdomain? Please clarify.

5. Please give an estimate (on average) of how many pixels wide are the typical pore widths in each of the three image types.

6. Please provide typical CPU times and resources required to perform the CNN training and well as run the heat maps.

7. In Fig. 9, can you add and/or highlight which data points are associated with the ground truth segmented images? The reason is: the reader may question how necessary is it to include the additional CNN segmented images if the 5-15 manually segmented images essentially provide the same linear fit?

8. The Discussion section is weak. The first 3 paragraphs read like an extended abstract, rehashing things already stated in more detail in the Results section. The latter paragraphs have good discussion content. Please decide how to resolve this. A short Discussion section is fine.

9. Please beef up the "so what?" aspect in your discussion. How does this work generalize to something of future relevance for thrombosis modeling? Perhaps discuss future work and/or forecasting how this work can be synergistically coupled to other modeling approaches.

Reviewer #2: I believe that, this is the original work of the authors. The quality of the manuscript is good and fulfill the requirements of the journal. This is an interesting observation and there is very little in the literature on this topi. The contents are presented in a clear and narrative manner, which is easy to understand for readers with various background.

Weaknesses:

- Not much has been said about the importance of this study, and this is the weakness of the article.

- There is a lack of a general discussion to summarize the results. There is no proper discussion based on the results obtained in the article.

Reviewer #3: The manuscript describes a pipelined process to reconstruct from SBF-SEM images the initial conditions (computational domain) for numerical simulations. A lattice kinetic MC method is used to simulate molecule diffusion on these reconstructed comput. domains. Although the result of hindered diffusion of molecules through cell packed domains is previously known, using deep learning to help image analysis is quite interesting.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: None

Reviewer #3: No:  The raw image, the lattice kinetic MC code are not available.

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Reviewer #1: No

Reviewer #2: Yes:  Reza Razaghi

Reviewer #3: No

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Attachments

Attachment

Submitted filename: Resubmission_letter_PLOS_05.29.2025.docx

Dear Dr. Sinno,

We are pleased to inform you that your manuscript 'From imaging to computational domains for physics-driven molecular biology simulations: Hindered diffusion in platelet masses' has been provisionally accepted for publication in PLOS Computational Biology.

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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,

Shugang Zhang

Academic Editor

PLOS Computational Biology

Marc Birtwistle

Section 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 #2: The authors have taken reviewers comments seriously and the manuscript is much improved as a result.

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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 #2: Yes

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Reviewer #2: No