DOI: 10.5061/dryad.5mkkwh7cd
title: Experimental evidence that network topology can accelerate the spread of beneficial mutations

description: Whether and how the spatial arrangement of a population influences
 adaptive evolution has puzzled evolutionary biologists. Theoretical models
 make conflicting predictions about the probability a beneficial mutation
 will become fixed in a population for certain topologies like stars, in
 which “leaf” populations are connected through a central “hub.” To date,
 these predictions have not been evaluated under realistic experimental
 conditions. Here, we test the prediction that topology can change the
 dynamics of fixation both in vitro and in silico by tracking the frequency
 of a beneficial mutant under positive selection as it spreads through
 networks of different topologies. Our results provide empirical support
 that metapopulation topology can increase the likelihood that a beneficial
 mutation spreads, broadens the conditions under which this phenomenon is
 thought to occur, and points the way towards using network topology to
 amplify the effects of weakly favored mutations under directed evolution
 in industrial applications. 
description: This data was collected from experimentally evolving laboratory
 populations of <em>P. aeruginosa</em> strain 14 (PA14) under a
 sub-inhibitory concentration of Ciprofloxacin and a novel agent-based
 SANCTUM model. The raw counts and proportions of the mutant bacteria
 PA14-<em>gyrA</em> wild-type non-mutant
 PA14-<em>LacZ</em> are both available. The proportions have
 been used to perform all the statistical analyses. 
description: # Experimental evidence that network topology can accelerate the spread of
 beneficial mutations ## Description of the data and file structure This
 dataset was collected from a selection experiment where 4-deme
 metapopulations of *Pseudomonas aeruginosa* were grown in rich and complex
 laboratory media supplemented with a subinhibitory concentration of the
 antibiotic Ciprofloxacin. In one of the demes (Patch 3), a beneficial
 mutant of *Pseudomonas aeruginosa* that has an advantage for growing in
 the presence of Ciprofloxacin (PA14-gyrA) was inoculated at a low
 frequency (1:1000 ratio) along with the wild-type bacteria (PA14-LacZ).
 All the other patches were inoculated with the wild-type bacteria
 (PA14-LacZ). In the X-Gal supplemented solid growth media, PA14-LacZ
 colonies appear blue but PA14-gyrA colonies appear white. The increase in
 the frequency of the beneficial mutant (PA14-gyrA) in the whole
 metapopulation was measured by counting blue and white colonies and taking
 the proportion of white colonies in the total number of colonies counted.
 All raw data (counts for blue and white colonies) can be found in the
 subfolders inside the "**Raw counts**" folder. Unless otherwise
 specified here, all datasheets contain variables describing the name of
 the network treatments (STAR/AMP or Well-mixed), the time/day of the
 evolution experiment, the identity of the biological replicates, blue
 colonies counted, white colonies counted, and the proportion of white
 colonies in that replicate metapopulation. The datasheets for the
 individual subpopulations (in the "Asym Subpopulations" and
 "Undirected subpopulations" subfolders) contain the variable
 proportion that is counted for individual subpopulations. The data file in
 the subfolder "denovo evolution of LacZ at 20ng CIP" depicts the
 number of *de novo* Ciprofloxacin-resistant mutants recovered after
 evolving *Pseudomonas aeruginosa* for 10 days at high and low population
 sizes under the sub-inhibitory concentration of Ciprofloxacin used in the
 experiment. This is the datasheet that has been used to produce
 supplementary figure 8. The data file inside the "Competitions at
 sub-inhibitory Cip" subfolder shows the relative fitness of PA14-gyrA
 mutant at different subinhibitory concentrations of Ciprofloxacin and has
 been used to make supplementary figure 7. The proportion data derived from
 these raw count datasheets has been extracted into the datasheets inside
 the "**Proportions**" folder for downstream analyses (making
 figures and statistical analyses) with R. This includes data from the
 simulation that have the same data structure as the experiments. The
 folders are structured according to the order of appearance of figures in
 the article. Please consult the "Instructions for reproducing
 figures" file provided with the dataset to reproduce the results of
 the article. The keys associating the file names with the experimental
 treatments are also inserted in each individual folder. The folder
 "**RKvalues**" contains the R and K values derived from the
 *nls* fitting to the proportion datasets. These have been used for
 statistical analyses. Code for the SANCTUM model can be found inside the
 "**Simulation data and code**" folder. The SANCTUM model is
 written in Python and can be uploaded to Google Colab and run in a web
 browser without installing dependencies. The instructions for running the
 simulation are included in the Python file itself. The data output from
 the SANCTUM model is divided into three folders. First, the
 "Undirected" folder. Datafiles with the phrase
 "histlong" in their name show the minimum generation required
 for the beneficial mutants to reach 50% of the frequency for each network
 treatment. Datafiles with "simmig" in the name show the
 proportion of beneficial mutants at each generation for each network
 treatment. Second, the "Undirected subpops" folder: datafiles
 starting with the phrase "sim" show the proportion of beneficial
 mutants in each subpopulation for each network treatment. Files inside the
 "Asymmetric" folder contain the proportion of the beneficial
 mutant for each asymmetric STAR/AMP and well-mixed network at different
 time points during the simulation. The folder "**R scripts**"
 contains all the R codes used to make the figures and perform the
 statistical analyses More details on each of these files and how to use
 them are described in the "Instructions for reproducing figures"
 text file. These files should run in R versions released after 4.0.2.
 Finally, the folder "**Stats results**" contains the data file
 with the results of all statistical analyses.