Data for: Tissue evolution: Mechanical interplay of adhesion, pressure, and heterogeneity

<p>Tissue evolution: mechanical interplay of adhesion, pressure, and&nbsp;heterogeneity</p> <p>Full Data for corresponding publication.</p> <p>&nbsp;</p> <p>All folders are named by the actual simulation values used, not by the rescaled values shown in the paper<br> All folders contain data files, trajectory files are converted and zipped, as well as the executable and the starting configuration file.<br> Start simulations with ./cell_dpd starting_configuration.sconf</p> <p>The simulation for Fig.1 can be found in folder /free_evolution/<br> folder structure: /free_evolution/LXxLYxLZ/GB_F1/DeltaG_alpha/ with LX, LY, LZ simulation box lengths in x,y,z direction, GB and F1 the simulation parameters of the host tissue, DeltaG difference in G of neighbouring species and alpha the relative change of f1 to G of neighbouring species.</p> <p>The simulation for Fig.2 and Fig.S3 can be found in folder /heterogeneity/sharp_treshold/<br> folder structure: /heterogeneity/sharp_treshold/pm/tradeoff/ with mutation probability pm and tradeoff parameter between changes in G and F1.</p> <p>The simulation for Fig.S1 can be found in folder /heterogeneity/division_rate/tradeoff/ with tradeoff parameter between changes in G and F1.</p> <p>The simulation for the division rate simulations of Fig.S2 can be found in folder /pair_competition/12x12x12/40_6.0/division_rate/</p> <p>The simulation for Figs. 4, 5 and S4 can be found in folder /pair_competition/12x12x12/40_6.0/sharp_treshold/<br> folder structure: /pair_competition/LXxLYxLZ/GW_F1W/sharp_treshold/GM/F1M/ with LX, LY, LZ simulation box lengths in x,y,z direction, GW and F1W the simulation parameters of host tissue and vice versa for mutant (M).</p> <p>The simulations for Fig.6 can be found in folder &nbsp;/mutationrate/<br> folder structure: /mutationrate/LXxLYxLZ/GW_F1W/pm/GM/F1M/ with LX, LY, LZ simulation box lengths in x,y,z direction, GW and F1W the simulation parameters of host tissue and vice versa for mutant (M) and mutation probability pm.</p> <p>The simulations for Fig.7 can be found in folder &nbsp;/survival/<br> folder structure: /survival/LXxLYxLZ/GW_F1W/GM/F1M/ with LX, LY, LZ simulation box lengths in x,y,z direction, GW and F1W the simulation parameters of host tissue and vice versa for mutant (M).</p> <p>The simulation for Fig.S5 can be found in folder /unstable/<br> folder structure: /unstable/LXxLYxLZ/GW_F1W/GM/F1M/phi0/sim_number with LX, LY, LZ simulation box lengths in x,y,z direction, GW and F1W the simulation parameters of host tissue and vice versa for mutant (M), initial number fraction phi0 and simulation number sim_number</p> <p>Folder /scripts/ contains all scripts to analyze the simulation results, described in the following:</p> <p>get_phi.py : Outputs file with cell number fractions from input file containing absolute cell numbers.<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Called by : python get_phi.py input output<br> &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;input: Name input file (in all simulations numcells.dat)<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; output: Name output file</p> <p>minimize_uid: Outputs minimized trajectory file as input.min<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Called by: ./minimize_uid input<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;input: name trajectory file (in all simulations traj.dat)</p> <p>convert2xyz_spec: Outputs trajectory in xyz format as input.xyz for pair competitions with n=2<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Called by : ./convert2xyz_spec input<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;input: name trajectory file (minimized file from minimize_uid)</p> <p>convert2xyz_new: Outputs trajectory in xyz format as input.xyz for heterogeneity simulations for n=21 species<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Called by : ./convert2xyz_new input<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; input: name trajectory file (minimized file from minimize_uid)</p> <p>block_average.py : Outputs file with block averaged value from input file.<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Called by : python block_average.py input output column n_or_t t_n_start t_n_end<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;input: Name input file<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;output: Output file name<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;column: column to be averaged<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;n_or_t: If 0, interprete following arguments as time, else as line numbers to start/end<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;t_n_start: time/line number to start average<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;t_n_end: time/line number to end average. If not given, t_n_start is interpreted, how much time/many line numbers to go back from the end</p> <p>cluster_analysis.py : Number of clusters of each species, &nbsp;cluster analysis by DB-SCAN algorithm with minimal points=1 and potential cut-off distance as size treshold<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Called by: python cluster_analysis.py input output<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; input: trajectory file in xyz format<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; output: name of output file</p> <p>neighbour_analysis.py : Analysis of the cell species average cell species of the cells in interaction range at each frame of the trajectory,<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;second column gives average of total neighbours per cell, following two columns for species 0 average number of identical&nbsp;<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;and different cell species, vice versa for species 1 in the next two last columns<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Called by: python neighbour_analysis.py input output<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;input: trajectory file in xyz format<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;output: name of output file</p> <p>get_average_cluster.py : averages number of clusters over given time/frame number<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Called by : python get_average_cluster.py input output n_or_t t_n_start t_n_end<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;input: Name input file<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;output: Output file name<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;n_or_t: If 0, interprete following arguments as time, else as line numbers to start/end<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;t_n_start: time/line number to start average<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;t_n_end: time/line number to end average. If not given, t_n_start is interpreted, how much time/many line numbers to go back from the end<br> get_average_neighbour.py : averages number of clusters over given time/frame number<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Called by : python get_average_neighbours.py input output n_or_t t_n_start t_n_end<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;input: Name input file<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;output: Output file name<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;n_or_t: If 0, interprete following arguments as time, else as line numbers to start/end<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;t_n_start: time/line number to start average<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;t_n_end: time/line number to end average. If not given, t_n_start is interpreted, how much time/many line numbers to go back from the end</p> <p><br> Folder /notebooks/ contains all jupyter notebooks to create the plots, starting from that directory as root directory</p> <p>Folder /src/ contains the source code for the individual simulation setups</p> <p>Folder /plots/ contains all plots created with the jupyter notebooks</p>