The Aevol User Manual
Contents
1. 8 2 MAX INDES SIZE Sia a Di id sil 24 Chromosomal Rearrangements Parameters o 25 9 1 DUPLICATION RATE DELETION RATE TRANSLOCATION RATE INVERSION RATE 25 To be continued 0 a a a a a 25 Introduction 1 What is AEVOL AEVOL is a digital genetics model populations of digital organisms are subjected to a process of selection and variation which creates a Darwinian dynamics By modifying the characteristics of selection e g population size type of environment environmental variations or variation e g mutation rates chromosomal rearrangement rates types of rearrangements horizontal transfer one can study experimentally the impact of these parameters on the structure of the evolved organisms In particular since AEVOL integrates a precise and realistic model of the genome it allows for the study of structural variations of the genome e g number of genes synteny proportion of coding sequences The simulation platform comes along with a set of tools to help analyse phylogenies and to measure many characteristics of the organisms and populations along evolution 2 License This program is free software you can redistribute it and or modify it under the terms of the GNU General Public License as published by the Free Software Foundation either version 3 of the License or at your option any later version This program is distributed in the hope that it will be useful but WITHO2. i e has a better fitness than an organism with no genes The population is then filled with clones of the generated organism 5 3 INITIAL GENOME LENGTH Meaning Size of the initial randomly generated genome s Default Value 5 000 6 Artificial Chemistry Parameters 6 1 MAX TRIANGLE WIDTH Meaning Maximum degree of protein pleiotropy This value must be strictly greater than 0 which would mean that a protein cannot do anything and lower than 1 which means that a protein can contribute to every possible metabolic process Default Value 0 033333333 7 SELECTION PARAMETERS 23 7 Selection Parameters 7 1 SELECTION SCHEME Meaning Selection scheme to use fitness_proportionate linear_ranking or exponential_ranking In the fitness_proportionate scheme the probability of reproduction of each organ ism is proportional to its fitness The probability of reproduction is proportional to exp k x g where k determines the intensity of selection it can be set using the SELEC TION PRESSURE keyword and g is the metabolic error see the model description The other two selection schemes are based on the rank of the organisms in the popula tion which allows one to maintain a constant selective pressure throughout the entire evolutionary process Organisms are thus first sorted by increasing fitness the worst in dividual in the population having rank 1 Then their probability of reproduction can be computed
3. RANK i INDEX If neither index nor rank are specified the program computes the detailed statistics for the best individual of generation GENER 18 II TUTORIAL USING AEVOL 4 5 aevol misc lineage The lineage tool allows for the reconstruction of the lineage of a given individual It requires the phylogenetic tree to be recorded during the evolutionnary run see the TREE_MODE parameter Using this phylogenetic tree it will produce a binary file contain ing the whole evolutionary history of any given individual i e for each of its ancestors which organism in the previous generation it is an offspring of and the list of mutations that occured during replication This file will be named e g lineage b000000 e050000 i999 r1000 ae which means we retraced the evolutionary history of the organism with rank 1 000 that had the index 999 at generation 50 000 and that its history was retraced all the way down to generation 0 This file is not readable in a text editor it is meant to be used by other programs like ancstats fixed_mutations or gene_families see below Usage aevol_misc_lineage i index r rank b generi e gener2 If neither index nor rank are specified the program creates the EPS files of the best individual of generation gener2 4 6 aevol misc _ancstats The ancstats tool issues the statistics for the line of descent of a given individual providing its lineage file see section 4 5 It will produce a set
4. all triangles resulting from the translation of a coding sequence are superimposed Usage aevol_misc_create_eps i INDEX r RANK g GENER There must have been a backup of the population at this generation For example if the program is called with the option g 4000 there must be a file called pop_004000 ae in the populations directory The program will then create a subdirectory called analysis generation004000 and write the EPS files therein If neither index nor rank are specified the program creates the EPS files of the best individual 4 3 aevol misc mutagenesis This mutagenesis tool creates and evaluates single mutants of an individual saved in a backup by default the best of its generation Use option g to specify the generation number contanining the individual of interest There must have been a backup of the population at this generation For example if the program is called with the option 4 POST TREATMENT TOOLS 17 g 4000 there must be a file called pop_004000 ae in the populations directory Use either the r or the i option to select another individual than the best one with i you have to provide the ID of the individual and with r the rank 1 for the individual with the lowest fitness N for the fittest one The type of mutations to perform must be specified with the m option Choose 0 to create mutants with a point mutation 1 for a small insertion 2 for a small deletion 3 for a duplication 4 for a la
5. children nodes are added to it representing the two gene copies When a gene sequence is modified the mutation is recorded in its corresponding node in one of the gene trees When a gene is lost the corresponding node in one of the gene trees is labelled as lost When a new gene appears from scratch i e not by gene duplication it becomes 20 II TUTORIAL USING AEVOL the root of a new gene tree Environmental variations are also replayed exactly as they occured during the main run When all mutations have been replayed several output files are written in a directory called gene_trees Two general text files are produced The file called gene_tree_statistics txt contains general data on each gene family like its creation date its extinction date or how many nodes it contained The file called nodeattr_tabular txt contains informa tion about each node of each gene tree like when it was duplicated or lost or how many mutations occurred on its branch In addition for each gene tree two text files are gener ated a file called genetree x x topology tre contains the topology of the gene tree in the Newick format and a file called genetree x x nodeattr txt that contains the list of events that happened to each node in the tree file before it was either duplicated or lost Usage ae_misc_gene_families c n t tolerance f lineage_file With the option c or fullcheck enabled the program will check that the rebuilt
6. or overspecialized genomes A sample parameter file is provided in examples workflow wild_type Once your parameter file is ready simply run the following commands it is recommended you do that in a dedicated directory called wild_type for example cd wild_type aevol_create f your_param_file aevol_run n number_of_generations 3 2 Experimental setup This is where the setup of the campaign of experiments is done As it would be done in a wet lab experiment different populations will be allowed to evolve in different condi tions to compare the different outcomes In this example we will start from an evolved population called the wild type created as above We will use this wild type to start 10 evolutionary lines that will have to adapt to a new environment Five of them will evolve under the same rates of chromosomal rearrangements as the wild type whereas the other five will be mutators evolving under higher rates of chromosomal rearrangements Both groups will evolve during 10 000 generations First the wild type population should have been created with aevol_create and aevol_run n 5000 for example Then the aevol_propagate tool allows for an exact copy of the whole data structure required by AEVOL with a reset of the current generation number to 0 Followed by a call to aevol_modify it allows us to set up our example in the 2 following steps Propagate the experiment The aevol_propagate tool allows for the creat
7. AL USING AEVOL 4 1 aevol misc view generation The view_generation tool is probably the easiest and most straightforward tool provided with AEVOL It allows one to visualize a generation using the exact same graphical outputs used in aevol_run However since it relies on graphics it is only available when AEVOL is compiled with X enabled which is the default Usage aevol_misc_view_generation g generation_number There must have been a backup of the population at this generation For example if the program is called with the option g 4000 there must be a file called pop_004000 ae in the populations directory 4 2 aevol misc create eps The create_eps tool takes a generation number as an input and produces several EPS files describing an individual of this population the best one by default at this generation best_genome_with_CDS eps where the chromosome is represented by a circle and coding sequences on the leading resp lagging strand are drawn as arcs outside resp inside the circle best_genome_with_mRNAs eps where the chromosome is represented by a circle and transcribed sequences on the leading resp lagging strand are drawn as arcs outside resp inside the circle Gray arcs correspond to non coding RNAs and black arcs correspond to coding RNAs best_phenotype eps where the phenotype resulting from the interaction of all genes is superimposed to the environmental target best_triangles eps where
8. S 5S 97558 535241 1499 916189 677 43743 7265 11942 29734 43155 Modify parameters to meet the experiment requirements For each of the propagated experiments create a plain text file e g newparam in con taining the parameters to be modified Parameters that do not appear in this file will re main unchanged The syntax is the same as for the parameter file used for aevol_create For example for the lines 1 to 5 we will create a text file called newparam groupA in will consist in the following lines New environment ENV_GAUSSIAN 0 5 ENV_GAUSSIAN 0 5 ENV_GAUSSIAN 0 5 ENV_VARIATION none ooo For the lines 6 to 10 we also want to modify the rearrangement rates hence the file newparam groupB in will consist in the following lines New environment ENV_GAUSSIAN 0 5 ENV_GAUSSIAN 0 5 ENV_GAUSSIAN 0 5 ENV_VARIATION none New rearrangement rates DUPLICATION_RATE DELETION_RATE TRANSLOCATION_RATE INVERSION_RATE 0 2 0 4 0 8 Then we will run the following commands 4 POST TREATMENT TOOLS 15 cd line01 aevol_modify gener 0 file newparam groupA in cd cd line02 aevol_modify gener 0 file newparam groupA in cd cd line03 aevol_modify gener 0 file newparam groupA in cd cd line04 aevol_modify gener 0 file newparam groupA in cd cd line05 aevol_modify gener 0 file newparam groupA in cd cd line06 aevol_modify gener 0 file newparam gr
9. The AEVOL User Manual for version 4 4 or newer Contents 1 What is AEVOL 2 Lic ns ta ee dete ed adds ced 3 The AEVOL Community I Installation 1 Linux users 1 1 Pre built packages 1 2 Installation from Source 2 Mac mser it ig Godse bad ed 2 1 Pre built packages 2 2 Installation from Source II Tutorial Using AEVOL 1 2 3 Appendix 5 Introduction Basic examples The workflow example 3l Wild Type generation 3 2 Experimental setup 3 3 Run the simulations 3 4 Analyse the outcome Post treatment Tools 4 1 aevol_misc_view_ generation 4 2 aevol misc create eps 4 3 aevol misc mutagenesis 4 4 aevol misc robustness 4 5 aevol_misc_lineage 4 6 aevol_misc_ancstats 4 7 aevol misc fixed mutations 4 8 aevol_misc_gene families AEVOL Parameters param in Initialization Parameters 5 1 INIT_POP_SIZE 5 2 INIT_ METHOD 5 3 INITIAL _ GENOME LENGTH Artificial Chemistry Parameters 6 1 MAX _ TRIANGLE WIDTH 11 11 12 12 12 13 15 15 15 16 16 16 17 18 18 19 19 10 TABLE OF CONTENTS S lection Parameters mio wale e GOO e 23 7 1 SELECTION SCHEME 3 44 4 8 42484 bo a 23 7 2 SELECTION PRESSURE a dee inea A e Gd we a a tS 24 Local Mutations Parameters ooa aa a a eee eee 24 8 1 POINT_MUTATION_RATE SMALL INSERTION RATE SMALL DELETION RATE ino ae a 24
10. These parameters set the spontaneous per replication per base rate of point mutations small insertions and small deletions indels respectively Default Value 1 x 10 8 2 MAX INDEL SIZE Meaning Sets the maximum size of indels small insertions and small deletions whose actual size will be uniformaly drawn in 1 MAX INDEL_SIZE 9 CHROMOSOMAL REARRANGEMENTS PARAMETERS 25 Default Value 9 Chromosomal Rearrangements Parameters There are two distinct ways to perform chromosomal rearrangements either taking se quence homology into account which is time consuming or not the breakpoints are then chosen at random Only the simple case where sequence homology is ignored will be covered here please see for homology driven rearrangements 9 1 DUPLICATION RATE DELETION RATE TRANSLOCATION RATE INVERSION RATE Meaning These parameters are used when sequence homology is ignored They set the sponta neous per replication per base rate of each kind of chromosomal rearrangements The breakpoints defining the sequence that will be either duplicated deleted translocated or inverted are drawn at random uniform law on the genome size Default Value 1 x 10 10 To be continued
11. UT ANY WARRANTY without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE See the GNU General Public License for more details You should have received a copy of the GNU General Public License along with this program If not see lt http www gnu org licenses gt 6 INTRODUCTION 3 The AEVOL Community AEVOL s primary ressource is its website http www aevol fr where you shall find information about the project and its contributors To subscribe to the low trafic users mailing lists please visit http lists gforge liris cnrs fr mailman listinfo aevol users You may also want to report bugs and ask for new features to be implemented To do so simply write to aevol bugs lists gforge liris cnrs fr or aevol feat request lists gforge liris cnrs fr Chapter I Installation AEVOL can run on Linux and on MacOS X 1 Linux users 1 1 Pre built packages AEVOL is available as a deb package but it is still in the testing repositories You should be able to apt get install aevol soon AEVOL should soon be available as an rpm package 1 2 Installation from Source Required Dependencies e Build Tools apt get install build essential or yum install gcc c e Compression library AEVOL compresses most of the data it uses apt get install zlibig dev or yum install zlib devel 8 I INSTALLATION Optional Dependencies e X libraries AEVOL uses the X11 library for the graphical o
12. build the executables tar zxf aevol VERSION tar gz 10 I INSTALLATION cd aevol VERSION configure make If you have administration privileges you can finally make the AEVOL programs available to all users on the computer by typing sudo make install If you don t have administration privileges you may still install AEVOL locally by doing the following configure prefix install path make make install where install path is a directory where you have write permission Don t forget to add install path to your PATH environment variable 11 Chapter II Tutorial Using AEVOL 1 Introduction AEVOL is made up of 4 main tools aevol_create aevol_run aevol_propagate and aevol_modify man pages provided in appendix 1 and a set of post treatment tools prefixed by aevol_misc_ Everything in AEVOL relies on an ad hoc file organization where all the data for an exper iment is stored organisms in the populations directory the task they are selected for in environment the experimental setup in exp_setup and so on It is not recommended to manually modify these files since this may cause some inconsistency leading to undefined behaviour Besides most of these files are compressed Once created an experiment can either be run propagated or modified Running an experiment simply means simulate evolution for a given number of gener ations Propagating an experiment means creating a fresh copy of it s
13. depending on their rank r and according to whether the linear or exponential scheme is used For the linear_ranking scheme the probability of reproduction of an individual is given by Dreproa y X n7 nt 77 x 4 where w and represent the probability of reproduction of the best and worst individual respectively For the population size to remain constant the sum over N of this expression must be equal to 1 and so y must be equal to 2 y As for 7 it must be chosen in the interval 1 2 so that the probability increases with the rank and remains in 0 1 To date variable population size is not supported with the linear_ranking scheme thus only 7 is required and can be specified using the SELECTION PRESSURE parameter For the exponential_ranking scheme the probability of reproduction is given by Preprod lt x cN where c 0 1 determines the intensity of selection it can be set using the SELECTION _ PRESSURE keyword The closer it is to 1 the weaker the selection Default Value exponential_ ranking 24 APPENDIX AEVOL PARAMETERS PARAM IN 7 2 SELECTION PRESSURE Meaning Intensity of selection This value is interpreted differently according to the selection scheme being used see the SELECTION SCHEME parameter Default Value 0 998 fit for the exponential_ranking scheme 8 Local Mutations Parameters 8 1 POINT MUTATION RATE SMALL INSERTION RATE SMALL DELETION RATE Meaning
14. etting the current gen eration number to 0 Modifying an experiment actually means modifying some of its parameters The aevol_modify tool virtually allows for the modification of any parameter of the experi ment including manipulations of the whole population or of individual organisms e g I want the population to be filled with clones of the organism having the longest genome or I want a random subset of organisms to be switched to super mutators To date only the most common experiment modifications have been implemented but feel free to ask for more aevol feat request lists gforge liris cnrs fr AEVOL comes along with a set of simple but representative examples Following these 12 II TUTORIAL USING AEVOL examples is probably the best way to get going with AEVOL and have a quick overview of the possibilities it offers In any case keep in mind that you can always get help by typing man aevol_cmd only available for the 4 main commands or aevol_cmd h available for all the commands Most examples are showcases for different features of the model such as spatially struc tured populations plasmids and horizontal transfer They can all be run with the same very simple commands Simply follow the instructions from section 2 The workflow example proposes a typical experiments on a previously generated wild type workflow It will lead you through the whole experimental process including a sample of possible post
15. genome sequence and the replayed environment are correct every lt BACKUP_STEP gt gener ations by comparing them to the data stored in the backups in the populations and environment directories The default behaviour is faster as it only performs these checks at the final generation only The option n or nocheck diasbales genome sequence checking completely Although it makes the program faster it is not recommended The option t tolerance is useful when gene_families is run on computer different from the one that performed the main evolutionary run In this case differences in compilators can lead to small variations in the computation of floating point numbers The tolerance specified with this option is used to decide whether the replayed environment is sufficienlty close to the one recorded during the main run in the environment directory 21 Appendix AEVOL Parameters param in 5 Initialization Parameters 5 1 INIT POP SIZE Meaning Initial Population Size constant in many setups Default Value 1 000 5 2 INIT METHOD Meaning Initialisation bootstrapping method It is strongly recommended to use the default method which is explained hereafter Default Value ONE GOOD GENE CLONE 22 APPENDIX AEVOL PARAMETERS PARAM IN A random sequence of size INITIAL GENOME LENGTH is generated and evaluated with regard to the defined task This process is repeated until the generated genome perform any subset of the task
16. get it is to install XCode freely downloadable from the App Store to start XCode and to install the Com mand Line Tools package from the menu XCode Preferences Downloads tab Components Alternatively you can also install the Command Line Tools package for Xcode without installing Xcode itself by downloading it from Apple s developer site free registration required and search for Command Line Tools e Compression library AEVOL compresses most of the data it uses using the zliblg library This library is already included as part of Mac OS X so there is no need to install it Optional Dependencies e X libraries For the graphical outputs Mac users should also have X11 installed X11 is not included with Mac OS X but X11 server and client libraries for OS X are available from the XQuartz project http xquartz macosforge org You will need to log out and log in after the installation to have X11 properly setup Note however that AEVOL can be compiled without graphical outputs and hence no need for X libraries by typing configure without x instead of configure see below This option is useful if you want to run AEVOL on a computer cluster for example Installation Instructions Download the latest release of AEVOL at http aevol fr download and save it to a directory of your choice Open a terminal and use the cd command to navigate to this directory Then follow the steps below to extract the files and
17. ion of fresh copies of an experiment as it was at a given time The i option sets the input directory and the o option the output directory You must provide a distinct output directory for each of the experiments you wish to run If the output directory does not exist it will be created If as we do here you use aevol_propagate repeatedly to initialize several simulations you should specify a different seed for each simulation otherwise all simulations will yield exactly the same results You can use the option S to do so In this case the random drawings will be different for all random processes enabled in your simulations mutations stochastic gene expression selection migration environmental variation environmental noise Alterna tively to change the random drawings for specific random processes only do not use S but the options m s t e n see aevol_propagate h for more information on those options 14 cd aevol_propagate aevol_propagate aevol_propagate aevol_propagate aevol_propagate aevol_propagate aevol_propagate aevol_propagate aevol_propagate aevol_propagate 8 8 8 8 8 8 8 8 8 8 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 wild_type wild_type wild_type wild_type wild_type wild_type wild_type wild_type wild_type wild_type II TUTORIAL USING AEVOL line01 line02 S line03 S line04 S line05 S line06 S line07 S line08 S line09 S line10
18. n on computer different from the one that performed the main evolutionary run In this case differences in compilators can lead to small variations in the computation of floating point numbers The tolerance specified with this option is used to decide whether the replayed environment is sufficienlty close to the one recorded during the main run in the environment directory 4 8 aevol misc gene families The gene_families tool issues the detailed history of each gene family on the lineage of a given individual providing its lineage file see section 4 5 A gene family is defined here as a set of coding sequences that arised by duplications of a single original gene The original gene called the root of the family can either be one of the genes in the initial ancestor or a new gene created from scratch for example by a local mutation that transformed a non coding RNA into a coding RNA The history of gene duplications gene losses and gene mutations in each gene family is represented by a binary tree The program starts by loading the initial genome at the beginning of the lineage and by tagging each gene in this initial genome Each of these initial genes is marked as the root of a gene family Then each mutation recorded in the lineage file is replayed and the fate of all tagged genes is followed and recorded in their respective families When a gene is duplicated the corresponding node in one of the gene trees becomes an internal node and two
19. of files similar to those created in the stats directory during the simulation but regarding the successive ancestors on the provided lineage instead of the best organism of each generation These files are placed in the stats ancstats directory The program works by loading the initial genome at the beginning of the lineage and then by replaying each mutation recorded in the lineage file Environmental variations are also replayed exactly as they occured during the main run Usage ae_misc_ancstats c n t tolerance f lineage_file With the option c or fullcheck enabled the program will check that the rebuilt genome sequence and the replayed environment are correct every lt BACKUP_STEP gt gener ations by comparing them to the data stored in the backups in the populations and environment directories The default behaviour is faster as it only performs these checks at the final generation only The option n or nocheck diasbales genome sequence checking completely Although it makes the program faster 1t is not recommended The option t tolerance is useful when ancstats in run on computer different from the one that performed the main evolutionary run In this case differences in compilators can lead to small variations in the computation of floating point numbers The tolerance specified with this option is used to decide whether the replayed environment is sufficienlty close to the one recorded during the main run in the environmen
20. oupB in cd cd line07 aevol_modify gener 0 file newparam groupB in cd cd line08 aevol_modify gener 0 file newparam groupB in cd cd line09 aevol_modify gener 0 file newparam groupB in cd cd line10 aevol_modify gener 0 file newparam groupB in cd 3 3 Run the simulations Each of the propagated experiments can be run thus aevol_run n lt number_of_generations gt Of course all the runs being completely independent you can submit these tasks to a cluster of your choice to save time 3 4 Analyse the outcome In addition to the set a statistics files that are recorded in the stats directory AEVOL includes a set of post treatment tools to further analyse the outcome of your experiments please refer to section 4 4 Post treatment Tools In addition to the set a statistics files that are recorded in the stats directory AEVOL includes a set of post treatment tools to further analyse the outcome of your experiments Please note that these tools have only been tested on simple experimental setups and can fail with exotic ones For example the tools listed below are fully functional un der a single chromosome setup but are still under development for most complicated settings with both a chromosome and exchangeable plasmids However in most cases the problems can easily be remedied Please do not hesitate to send us your request aevol feat requestQlists gforge liris cnrs fr 16 II TUTORI
21. rge deletion 5 for a translocation or 6 for an inversion For the point mutations all single mutants will be created and evaluated For the other mutation types an exhaustive mutagenesis would take too much time hence only a sample of mutants 1000 by default will be generated Use option n to specify another sample size The output file will be placed in a subdirectory called analysis generationGENER Usage aevol_misc_mutagenesis g GENER i INDEX r RANK m MUTATIONTYPE n NBMUTANTS 4 4 aevol misc robustness The robustness tool computes the replication statistics of all the individuals of a given generation like the proportion of neutral beneficial deleterious offsprings This is done by simulating NBCHILDREN replications for each individual 1000 replications by default with its mutation rearrangement and transfer rates Depending on those rates and genome size there can be several mutations per replication Those global statistics are written in analysis generationGENER robustness allindivs gGENER out with one line per individual in the specified generation The program also outputs detailed statistics for one of the individuals the best one by default The detailed statistics for this individual are written in analysis generationGENER robustness singleindiv details gGENER iINDEX rRANK out with one line per simulated child of this particular individual Usage aevol_misc_robustness g GENER n NBCHILDREN r
22. t directory 4 POST TREATMENT TOOLS 19 4 7 aevol misc fixed mutations The fixed_mutations tool issues the detailed list of mutations that occurred in the lineage of a given individual providing its lineage file see section 4 5 This text file is placed in the stats directory The program works by loading the initial genome at the beginning of the lineage and then by replaying each mutation recorded in the lineage file Environmental variations are also replayed exactly as they occured during the main run The output file indicates for each mutation at which generation it occurred which type of event it was point mutation small insertion inversion where it occurred on the chromosome and how many genes actually how many coding RNAs where affected More details are given in the first lines of the file itself Usage ae_misc_fixed_mutations c n t tolerance f lineage_file With the option c or fullcheck enabled the program will check that the rebuilt genome sequence and the replayed environment are correct every lt BACKUP_STEP gt gener ations by comparing them to the data stored in the backups in the populations and environment directories The default behaviour is faster as it only performs these checks at the final generation The option n or nocheck disables genome sequence checking altogether Although it makes the program faster it is not recommended The option t tolerance is useful when fixed_mutations is ru
23. treatments you can use to analyse the outcome of your different simulations 2 Basic examples To run all but the workflow examples simply follow the following steps 1 Install AEVOL preferentially with graphics enabled see chapter I 2 cd into the directory of the example e g examples basic 3 run aevol_create 4 run aevol_run 5 Have a look at the graphical outputs Ctrl Q to quit Optional Explore the different statistics created in the stats subdirectory 3 The workflow example The workflow example provides an example of one of the many different workflows that can be used for experiments with AEVOL The main idea underlying this workflow is to parallel wet lab experiments which are conducted on evolved organisms To use already evolved organisms for AEVOL experiments one can either use an evolved genome provided by the community or evolve one s own This example describes the latter more complete case 3 1 Wild Type generation Generating a Wild Type in AEVOL is very easy all you need is a parameter file describing the conditions in which it the Wild Type should be created population size mutation rates task to perform However have in mind that founding effects can influence the 3 THE workflow EXAMPLE 13 course of evolution especially in the case of overconstrained evolution It is recommended to use mild mutation and rearrangement rates and to let the environment vary over time to avoid overconstrained
24. utputs apt get install libxii dev or yum install 1ibX11 devel Note however that AEVOL can be compiled without graphical outputs and hence no need for X libraries by typing configure without x instead of configure see installation instructions below for more information This option is useful if you want to run AEVOL on a computer cluster for example Installation Instructions Download the latest release of AEVOL at http aevol fr download and save it toa directory of your choice Open a terminal and use the cd command to navigate to this directory Then follow the steps below to extract the files and build the executables tar zxf aevol VERSION tar gz cd aevol VERSION configure make If you have administration privileges you can finally make the AEVOL programs available to all users on the computer by typing sudo make install If you don t have administration privileges you may still install AEVOL locally by doing the following configure prefix install path make make install where install path is a directory where you have write permission Don t forget to add install path to your PATH environment variable 2 MAC USERS 9 2 Mac users 2 1 Pre built packages This option is not available yet for mac users 2 2 Installation from Source Required Dependencies e C command line compiler Mac users should have a command line C compiler like g or clang installed One easy way to
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