PONE-D-25-06403THE IMPACT OF EXPERIMENTAL DESIGNS & SYSTEM SLOPPINESS ON THE PERSONALISATION PROCESS: A CARDIOVASCULAR PERSPECTIVEPLOS ONE

Dear Dr. Saxton,

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Additional Editor Comments:

Please note that the complete list of comments from one of the reviewers is included in the attachment.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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—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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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Saxton et al. explore the personalization process of a simplified cardiovascular model. They introduce the concept of sloppiness, which refers to the geometry of the loss function along the direction of each parameter. They also discuss experimental design, gradually increasing both the amount and type of data (from discrete to continuous) across experiments. The results show that experimental design affects parameter identifiability (and how parameters are ranked) and that continuous data increases sloppiness, leading to greater numerical difficulty in fitting the data.

The study is both interesting and timely. Several groups are currently developing cardiovascular digital twins and facing similar challenges to those highlighted in this manuscript. The topic is well-suited for PLOS ONE. Overall, the manuscript is well-written, with only a few minor typographical errors. However, the figures could be improved (see comments below).

Major Points:

- Section 2.1.3: Sloppiness certainly affects the choice of numerical strategy (in fact, in numerical analysis, it is related to the concept of “stiffness”). However, the key issue here is different: a sloppy system tends to have elongated or even flat valleys around the minimum, meaning that the minimum is not well-defined. As a result, even small noise in the data could lead to a large region of valid parameter values.

- Prior distribution of parameters: The choice of prior distributions is crucial for improving identifiability, especially in low-data regimes. Here, the prior is likely a uniform distribution (though this is not explicitly stated). While a uniform prior is valid in the absence of additional information, the authors should discuss how alternative priors could be incorporated into their workflow. For instance, certain parameter combinations may produce unrealistic physiological outputs (e.g., cardiac output or pressures outside expected ranges). Given population-based distributions of outputs, one could attempt to infer a suitable distribution of input parameters such that random sampling yields the observed output distribution.

- Experimental design strategy: My understanding is that each experiment, associated with different measurement sets, is independent of the others. However, suppose we start with the smallest measurement set and fit the parameters: could this information be used to optimize the selection of the next measurement set? Certain parameter combinations may exhibit low sloppiness, which could guide the experimental design.

- Section 3.2: Is the clinical data real or synthetically generated? What do you mean by “medically accurate”? Please clarify.

- Tables 5 and 6: These tables are not very accessible. Consider replacing them with a plot. For instance, a “rank plot” could effectively illustrate how parameter importance changes.

- Page 16: The sentence “Despite the number of discrete measurements outnumbering the number of continuous measurements” is misleading. A continuous measurement is, in principle, infinite-dimensional. While a function can often be approximated using a low-dimensional representation due to correlation, this representation typically has more than one dimension (unless the function is constant).

- Page 20: The paragraph beginning with “One problem of the sloppy analysis” is unclear. Could you refine and clarify this explanation?

Minor Points:

- Abstract: Why use “reduced-order” in the first sentence?

- Abstract: The term sloppiness is not defined. This concept may not be widely known outside statistical modeling communities. Consider providing a brief explanation.

- Author summary: Is this section a requirement for PLOS ONE?

- Terminology: The model is often referred to as a “4-chamber model.” While technically accurate, the term “4-chamber model” nowadays typically refers to a 3D electromechanical model of the heart. Consider using “lumped model” or a similar term to avoid confusion.

- Section 2.1.3: The loss function can be visualized in dimensions greater than two using various machine-learning techniques. A simple approach would be to slice the loss landscape with randomly chosen 2D planes.

- Page 12: “poreviosu” -> “previous”

- Figure sequence: Figure 5 is discussed before Figure 4 (page 14). Consider reordering the discussion or figures for better flow.

- Figure 6: You might add a delineation line between 3D and 4B and clarify that the addition of continuous data is being illustrated.

- Page 20: “personlisation” -> “personalisation”

Reviewer #2: The document “impact of experimental designs & system sloppiness on the personalisation process: a cardiovascular perspective” presents a thorough sensitivity analysis in a simplified model of the cardiovascular system leverages these results to explain the importance on the input parameters depending on the quantities of interest (QoIs) extracted from (and compared with) the physical system. Particularly, the authors are interested in the sloppiness of the system, this is the separation between the impact of parameters in the outputs, which of course change with the outputs. Particularly, the authors discover that, for this particular model, if the researcher wants to compare QoIs that are continuous clinical measures, there is an increase in the system sloppiness, compared to a discrete measurement approach.

While this work represents a good enough body of work for a publication, I consider there are items that could be improved to increase the technical quality and ease its understanding.

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Reviewer #1: No

Reviewer #2: Yes: Alfonso Santiago

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Attachments

Attachment

Submitted filename: Review.docx

THE IMPACT OF EXPERIMENTAL DESIGNS & SYSTEM SLOPPINESS ON THE PERSONALISATION PROCESS: A CARDIOVASCULAR PERSPECTIVE

PONE-D-25-06403R1

Dear Dr. Saxton,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Pan Li, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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—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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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: There are some latex typos I've spotted:

- line 490-491, the sentence is weird

- line 534, fig -> Figure

Reviewer #2: To this reviewer point of view, the author addressed all the concerns pointed out by the reviewing panel, either by modifying the text or by giving a detailed explanation of the reason the authors chose and making a brief comment on the text. From this reviewer’s perspective, the manuscript is approved for publication after some minor edition comments, see below.

Minor edition issues detected during this last review are:

* Some of the latex \ref seem broken (check e.g. tag 3/33 lines 102,107; page 4/33 lines 109, 114,136 page 5/33 line 175 and so on)

* On the new text page 3/33 lines 62-63 there’s a one sentence paragraph. While it’s not explicitly out of format, it’s stylistically weak. I recommend removing the end of paragraph, making it: “(…) or ‘model calibration’). Mathematically (…)”

* Page 11/33 line 349 Oxford comma missing “In tables 2,3!,! and 4,”. Not mandatory but preferred in formal writing.

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7. 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: Yes: Simone Pezzuto

Reviewer #2: Yes: Alfonso Santiago

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