Dear Dr. sabzevari,

Thank you very much for submitting your manuscript "Strain design optimization using reinforcement Learning" 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.

The originality of your work was well received all around, but a major issue has been recognized relating to the practical application of your approach: you don't provide biological validation of the method and it seems that it could be very hard to implement in practice. In addition several reviewers commented on the lack of discussion of your results in the biological context. In order for the paper to be accepted in a future version it is critical that you address these points clearly. Of course we would like to see your reply (and corresponding changes) relating to the other points raised by the reviewers too. However the experimental feasibility of the method must be addressed, otherwise the manuscript fails to fit in the publication scope of this journal.

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,

Pedro Mendes, PhD

Associate Editor

PLOS Computational Biology

Daniel Beard

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 recognise that strain optimization is a combinatorial search problem, and that RL is a useful approach in such cases (provided the data – real or simulated - are available). Of course in the hands of DeepMnd RL has had considerable success e.g. in learning to play Go, and so on. They (wisely) choose a model-free approach, not least since there are no directly predictive relations between the observables (i) concentration and (ii) flux (both are variables).

I set out thinking that this would be very exciting, but TBH it promised rather more than it delivered. I think the gushing tone of the abstract needs to be toned down to recognise the limitations more clearly: (i) only three somewhat unexciting fluxes were measured, (ii) it required FORTY iterations, which is a huge number for an experimental program, and (iii) many of the explanations were close to non-existent. It probably reflects the mathematical strengths of the first author, but these cannot come – in PlosBiol at least – at the expense of explaining what the findings actually meant. In consequence, it failed to convince me that I might seek to adopt their strategy, which is what I would wish it to have done. (Or alternatively that it was a nice idea but not in fact worth adopting in this way, a point nicely made in Harford T: How to

Make the World Add Up: ten rules for thinking differently about numbers. London: Bridge Stree Press, 2020.)

Abstract: “We demonstrate the method’s capabilities in comprehensive experiments using the genome-scale kinetic model of E. coli, k-ecoli457, as a surrogate for in vivo cell behaviour”. These were simulations, not experiments. Call them so. Comprehensive is a claim, not a fact.

I am not qualified other than to take the maths on pp 3-4 on trust. The background of the authors suggests this is reasonable.

The multi-agent aspect is nice as it recognises that this is effectively how experiments are performed in a DBTL cycle.

The authors might care to contrast their approach with BOED as in

Foster A, Jankowiak M, Bingham E, Horsfall P, Teh YW, Rainforth T, Goodman N: Variational Bayesian Optimal Experimental Design. arXiv 2019:1903.05480v05483.

Vanlier J, Tiemann CA, Hilbers PAJ, van Riel NAW: A Bayesian approach to targeted experiment design. Bioinformatics 2012; 28:1136-1142.

Fig 2. Numbers in heat map unreadable. Also try to choose non red-green for those who are colour blind. https://colorbrewer2.org/#type=diverging&scheme=BrBG&n=3 gives suggestions.

“As we observe in this figure, the algoritmh [sic] tunes the enzyme levels in generally smooth fashion while the larger changes correspond to replacing the worst agent with another”. Maybe so, but I cannot see this and so it needs a much better explanation. Tell me what I am supposed to look at and what I am supposed to draw from it.

I don’t understand the units in Figs 2 and 3 – you can’t have more moles out than went in…

Yield improvement p8. 40 iteration is not that realistic for real DBTL programs. It would be good to show the time course of the median per iteration.

Fig 3. Why is succinate different from ethanol and acetate?

Table 2. Strain stability measure. Needs far more explanation – what am I supposed to infer? Also I doubt that the second DP has much meaning, or even the first…

Fig 4. Again you tell us what you did but not what we are supposed to make of the results.

“The enzyme levels can be quantified using targeted mass spectrometry based proteomics approaches found useful in optimizing production”. I doubt it. Transcriptomics typically provide a much better surrogate (Machado D, Herrgård M: Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol 2014; 10:e1003580).

Reviewer #2: This is a good paper, the proposed reinforcement learning (RL) method is novel to the field of strain engineering, well explained and sound. The benchmark with other methods is also fair and clear. The introduction of noise is fully relevant especially where dealing with strain engineering. Yet, I find the method a bit limited in scope. First, one needs a strain ODE kinetics models and very few are available. Secondly, the method is applied only to a predefined set of enzymes and that information is not always available. Finally, to experimentally implement the method one needs to regulate the expression levels of enzymes (via promoters, RBSs..) and this is not a straightforward task where model simulations always agree with experimental results.

I would advise the authors to add in the discussion the current limitations of their method and discus if and how a similar method could be developed and used with genome scale models (GEMs) and steady state dynamics as there are plenty of such models, methods and results in the literature. The authors could also comment and if and how the actions could be simplified to propose set of genes to be knocked out. Note that in that latter case and when using GEMs, results could be compared with MILP solvers like OptKnock.

Minor comments:

Page 2: In addition to references 6-8 which make use of machine learning but not RL there are few papers explicitly making use of RL in the context of bioproduction and it is worth mentioning these //doi.org/10.1016/j.jprocont.2018.07.013 , //doi.org/10.1021/acssynbio.9b00447, //doi.org/10.1016/j.compchemeng.2019.106649 , //doi.org/10.1371/journal.pcbi.1007783 .

Figure 1. It would be wise to define the symbols used in the caption. Some can be found in the text but not all. Would also be wise to indicate where in the flow chart the policy is learned and used.

Page 4. The ML engine to build the policy is MMR (SVM based), could the author motivate this choice as other methods could have been used?

Page 5. The authors make use of a mixed centralized and decentralized training and defined groups where RL is carried out. Although within a group, exploration seems to be favored, I do not see the RL exploitation vs. exploration strategy being used nor discussed. Could the authors comment on that?

Table 1. The target reaction needs to be defined.

Figure. 2. The process is iterated 40 times but the Figure shows only 13 rows, what are they? Would also be good to state that the numbers and colors correspond to enzyme level.

Figure 4. The Figure shows yield decrement when noise is introduced separately to action, yield and state, has any test been carried out where noise was introduced simultaneously on the 3 elements?

In addition there are few typos which could be cleared with a spell checker.

Reviewer #3: This is an excellent paper outlining an important new approach to strain design optimization. I have two main areas of feedback:

1) The specific settings/decisions for tuning the MARL parameters are not justified. How was it decided for example the number of iterations and trajectories? Would slightly different options yield improved performance? Would one set of parameters be universally best for any problem, or would users of this method need to re-evaluate this for each new context? This should be explained.

2) Substantial editing is needed to improve the clarity of the manuscript. For example: on line 239 "algorithm" is spelled wrong and the word "a" is missing. The sentence starting on line 254 is grammatically incorrect.

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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

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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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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

Dear Dr. sabzevari,

Thank you very much for submitting your manuscript "Strain design optimization using reinforcement Learning" 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.

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, PhD

Associate Editor

PLOS Computational Biology

Daniel Beard

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 #2: I have read the revised version and agree with the modifications the authors have introduced. The authors have answered well all the points I raised with the exception of one.

The comment was “I would advise the authors to add in the discussion the current limitations of their method and discus if and how a similar method could be developed and used with genome scale models (GEMs) and steady state dynamics as there are plenty of such models, methods and results in the literature. The authors could also comment and if and how the actions could be simplified to propose set of genes to be knocked out. Note that in that latter case and when using GEMs, results could be compared with MILP solvers like OptKnock.“

As a reply to this comment the authors are presenting the difficulties and solutions to modulate enzyme expression level (a point that was raised earlier in the initial review). Could the above comment be addressed?

Reviewer #3: Thank you for your updates, this addresses my concerns and comments.

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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

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 #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. sabzevari,

We are pleased to inform you that your manuscript 'Strain design optimization using reinforcement Learning' 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, PhD

Associate Editor

PLOS Computational Biology

Daniel Beard

Deputy Editor

PLOS Computational Biology

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