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From Valley Forge to the Lab: Parallels between Washington's Maneuvers and Drug Development2 weeks ago in The Curious Wavefunction
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Political pollsters are pretending they know what's happening. They don't.2 weeks ago in Genomics, Medicine, and Pseudoscience
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Course Corrections5 months ago in Angry by Choice
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The Site is Dead, Long Live the Site2 years ago in Catalogue of Organisms
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Posted: July 22, 2018 at 03:03PM6 years ago in Field Notes
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post doc job opportunity on ribosome biochemistry!9 years ago in Protein Evolution and Other Musings
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Blogging Microbes- Communicating Microbiology to Netizens10 years ago in Memoirs of a Defective Brain
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The Lure of the Obscure? Guest Post by Frank Stahl12 years ago in Sex, Genes & Evolution
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Lab Rat Moving House13 years ago in Life of a Lab Rat
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in The Biology Files
Not your typical science blog, but an 'open science' research blog. Watch me fumbling my way towards understanding how and why bacteria take up DNA, and getting distracted by other cool questions.
Independent of mutation rate
Here's a better way of looking at how the mutation rate affects (doesn't affect) the outcome of the simulations. It was suggested by a mathematically sophisticated family member.
Previously I plotted the score as a function of the number of cycles run, for each of the three mutation rates, with 3 or 4 replicate runs plotted on the same graph. I've done several things differently in the graph on the left (which PowerPoint has inexplicably warped). The first two differences are trivial - I've plotted the means of the replicate runs, so there are only three lines, and I've put the x-axis on a log scale so the points are spread out evenly.
The third difference is the important one. I've changed the x-axis so instead of being the cycle number it's the total number of mutations each genome has been exposed to, expressed per 100 bp. The µ=0.01 scale didn't change, but the scale for the µ=0.001 runs decreased by 10-fold and the scale for the µ=0.0001 runs decreased 100-fold.
Now we see that the runs with three different mutation rates have all given very similar (superimposable) results. I'll present this figure in the paper, as it will nicely justify our decision to do the bulk of the oher runs with µ=0.001.
This presentation also shows that, even after 20,000 cycles, the runs with µ=0.0001 are only just reaching equilibrium - I'll try to run them for longer, even if this means running them for a week or two on our lab computer.
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