Completed on 14 Jun 2016 by NIH/NHGRI preprint journal club, Steve Bond, Daniel Bar, Anthony Kirilusha, David McGaughey, Sofia Barreira and John Didion. Sourced from http://biorxiv.org/content/early/2016/04/22/049767.
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We reviewed this paper in our May preprint journal club.
This is a clever use of Avida to look at the dynamics of genome evolution.
We debated the choice of 15 essential instructions as the starting genome. On the one hand, it seems appropriate to assume modern genomes arose from smaller genomes, but on the other hand, you are starting from a genome that can only expand because all deletions are necessarily fatal (at least until the genome has acquired insertions). More interesting, perhaps, would be to start with a larger genome that is already capable of performing 3-4 logical operations (albeit inefficiently), and then observing the selective pressures imposed by population size. This way, deletions (deleterious or not) can run to fixation in the starting populations, and should be a more accurate representation of what is likely to occur in real world populations. It will also be interesting to see the effect of high deletion bias enforcement on these larger starting genomes (as per Fig. S6). We would anticipate that the genome size of small populations would drop quickly due to drift, likely losing traits; however, the largest populations would undergo an initial drop in genome size as trait efficiency improved, followed by the gradual increase reported in the manuscript.
Another point of discussion was the way trait count was used as a proxy for complexity. While it makes sense to use a multiplicative fitness function that limits merit to a single instance of any given trait (otherwise genomes would expand uncontrollably with repeats of simple operations), we believe you are doing yourselves a disservice by ignoring the emergence of redundant traits and by counting each trait equally once acquired. We would have preferred to see which traits emerged, and how many of each, plotted over time (we do appreciate that this is a difficult data visualization challenge, but we also believe the trends you are reporting will be much more compelling).
Generally, we recommend more informative plots. We observed considerable fluctuation in genome size and merit/fitness through time when we replicated several of the simulations, especially for small populations, so this variation should be better illustrated and discussed. E.g., line graph of mean genome size as a function of time, including 95% CI as a shaded area about the mean.
It also seems that some of the individual figures should be merged into multi-panel figures, and it is unnecessary to relegate so many figures to the supplement. For example, figures 2, 3, and S2 could be made into a single multi-panel figure.