PONE-D-24-14689Phenotype Selection due to Mutational RobustnessPLOS ONE

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: Yes

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5. Review Comments to the Author

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Reviewer #1: The author presents a study of gene regulatory networks (GRNs), where the presence of three kinds of dynamical systems behavior are compared between an unbiased sampling of the genotype space (using a Monte Carlo approach) and two kinds of evolutionary simulations. The main finding is that evolution strongly selects against one-way behavior, whilst mono and toggle-switch behavior appear to be favored. The author argues that one-way behavior is less robust than the other two behaviors, in the sense that one-way tends to require more essential edges.

I applaud the massive amount of simulations done to establish a set of ~50k lineages to analyze, and the innovative application of McMC sampling to generate unbiased probability distributions of the three kinds of dynamical systems behavior. The results are not unanticipated, as it is known that (long-term) evolution in a constant environment favors the evolution of robustness.

Overall, the study has been well done and is a nice contribution to the (in silico) evolutionary biology community. In my opinion, it is worthy of publication in PLoS ONE after revisions. Please find below my comments.

Comments:

- citations are put after the sentence; they should be within, i.e. before the end-of-sentence mark.

- please consider having the manuscript checked by a native English person. Especially the use of articles (the) is problematic (too abundant) and hinders a smooth reading experience.

- in the Introduction, it is argued that selection for robustness is almost independent of fitness (line 17). What does the author mean? That there is a trade-off between being fitter and being more robust? I do not remember the precise works that study this relationship, but surely there is such a trade-off and I would expect it to depend on selection pressure.

- in the Introduction, the author states that second order selection enhances evolvability (line 18-19). Since evolvability is such a loosely used term, it would be good if the author defines what exactly they mean in this context, e.g. by choosing one of the three definitions from Pigliucci. It seems, the first definition, evolvability as heritability is the one.

- in the Introduction, the author argues that fitness traditionally is expressed as the number of offspring. I agree, this is surely the case in population genetics. In many simulation studies, however, fitness is a value computed as the author does. So, it is not that special and I think it would be good if the text reflected this more clearly.

- line 106 could be put in Eq. 3 for completeness: f = max(x_out(1) - x_out(0), 0)

- in the Methods, lines 107-119 and lines 120-127 read more like results than Methods. The author should consider moving the text.

- in line 136 it is not clear what happens to the sign of an edge (activation, inhibition) as it is rewired.

- in the Methods, the explanations of ES0 and EST are confusing. The zero in ES0 has nothing to do with the temperature beta of EST, whilst I expected these two to be related. And naming EST explicitly as "stochastic" (line 146) made me think the other one is not... which is not the case, of course, since mutations always introduce some stochasticity. Alternatively, one may argue that "sure mutations are stochastic", but the network is computed in a deterministic manner, so fitness is deterministic and taking the top half of the population is not stochastic either. That is fine with me, but it would be good to make that more explicit, f.i. by stating that here we talk about the selection step only. Some rephrasing is necessary here. Also, at some point I thought EST was a continuation of ES0, but that appears not to be the case either (at least not in operational terms, conceptually yes?). So, do I understand it correctly that ES0 has a classical selection scheme of replacing the bottom-half of the population? And that EST is more like a noisy propertional-fitness selection scheme? I think it would be better either to explain how the two selection schemes are unified (since right now their naming suggests there is a single framework behind) or to choose two really different names, like ES_half and ES_noise, to acknowledge the independent approaches.

- in lines 188 and on, essential edges are defined in a way that evolution never experiences, which may impact the results reported. I mean: removing an edge is normally accompanied by the addition of a new one somewhere else in the network (the definition of a mutation). So to measure mutational robustness by subjecting networks to modifications that are not mutations and that the GRNs never evolved with is inconsistent. It is likely to be rather harmless, but I think this needs to be checked or at least reported as a caveat.

- in the Results, the author does not elaborate on any patterns of network structure that evolution may have come up with. How many edges are activating / inhibiting in evolved and sampled networks? Is the node in- and outdegree of evolved networks very different from the sampled ones? Are there many forcing structures (similar to the AND and OR functions in Boolean networks)? Understanding such structural patterns may give deeper insight into what evolution prefers as a solution to the task of reacting to an input signal.

- Discussion: this study focuses on the "classic study case" of evolution in a constant environment, where a single task needs to be done. There are many other settings that have been left unexplored (e.g. changing environments, multiple tasks, co-evolution, allowing for changes in nr of genes & nr of edges), which makes me rather hesitant at the claim of universality in the Discussion. I know "universality" has a specific meaning in physics, yet I fear a biology or comp sci colleague could easily misunderstand it. I would suggest the author to either not claim it, or better contextualize the claim. Perhaps there are specific experimental results that could be mentioned (E. coli, yeast)? Note that I do not suggest to do more simulations under different kinds of evolutionary scenarios!

- I don't understand why the reported results are third-order. I thought of them as the mechanism (or implementation) that evolution selected. Mutational robustness must manifest in some way. Iow, for me they are second-order.

- it would be good to discuss limitations of the study: besides the above mentioned constant environment, a fixed nr of genes and a fixed total nr of edges is bound to have had an impact on the results presented here.

- the final statement that GAs explore in a biased way the optimization landscape is common knowledge. I would encourage the author to come up with a stronger concluding sentence at the end of their manuscript.

Detailed comments:

- line 2: "developed" is not the term to use, since in biology development is rather different from evolution.

- in figure 1 colors are not explained, especially those of the edges.

- it would be helpful to provide titles in the panels in figures 4 and 6 with "mono", "toggle" and "one-way" and similarly, in figure 5 with "McMC" and "EST". Otherwise, one keeps going back and forth between caption and figure.

- in many panels, errorbars are difficult to see because they overlap with the symbol. Also, too many symbols together make for very fat lines, could the author space them better? Smaller symbols? Less symbols?

- N_e is a famous technical term/symbol in evolutionary biology meaning "effective population size". I advice the author to choose a different one for "nr of essential edges" to avoid confusion. Already N_ee could work, not?

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Reviewer #1: No

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Phenotype selection due to mutational robustness

PONE-D-24-14689R1

Dear Dr. Kikuchi,

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.

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Additional Editor Comments (optional):

Please note that the reviewer has suggested two minor edits, which can be made before publication. 

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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

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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

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

Reviewer #1: 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

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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

**********

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: I enjoyed reading the revised manuscript and would like to congratulate the author.

Two minor comments:

- An end-of-sentence dot is missing in line 350 (non-colored manuscript version)

- Reference nr 7 first author is "Isalan" not "Isaran"

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If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #1: Yes: Anton Crombach

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