def run(output_file):
scipy.random.seed()
gene_expression = start_transcribing(INI, "outputdir", tss, tts, prot,
genome_size)
fitness = get_fitness(gene_expression, expected_profile)
with open(output_file, 'w') as f:
f.write(str(fitness) + '\n')
def evol_main_loop(tss, tts, prot, genome_size, fitness, p_inversion,
temperature, indel_size, expected_profile):
# making backups for the current genome
tss_backup = tss.copy()
tts_backup = tts.copy()
prot_backup = prot.copy()
genome_size_backup = genome_size
fitness_backup = fitness
if np.random.rand(
) < p_inversion: # decide if there is an inversion or indel
inversion(tss, tts, prot, genome_size)
mutation_type = 1
else:
genome_size = indel(tss, tts, prot, genome_size, indel_size)
mutation_type = 2
#~ print(tss)
#~ print(tts)
#~ print(prot)
#~ display_genome(tss,tts,prot,genome_size)
gene_expression = sim.start_transcribing(INI_file, output_dir, tss, tts,
prot, genome_size)
fitness = get_fitness(gene_expression, expected_profile)
# if the new fitness is lower than the previous one
# and the random choice doesn't select the new genome
# we go back to the backups
if fitness < fitness_backup and np.random.rand() > np.exp(
(fitness - fitness_backup) / temperature):
tss = tss_backup
tts = tts_backup
prot = prot_backup
genome_size = genome_size_backup
fitness = fitness_backup
mutation_type = 0
return (tss, tts, prot, genome_size, fitness, mutation_type)
def evol_master(arg):
scipy.random.seed()
fitness_file, p_inversion = arg
# read the evolution parameters from the config file
config = sim.read_config_file(INI_file)
environment_file = config.get('EVOLUTION', 'environment')
temperature = config.getfloat('EVOLUTION', 'temperature')
temperature_rate = config.getfloat('EVOLUTION', 'temperature_rate')
nbiter = config.getint('EVOLUTION', 'nbiter')
#p_inversion = config.getfloat('EVOLUTION', 'p_inversion')
indel_size = config.getint('EVOLUTION', 'indel_size')
#fitness_file = config.get('EVOLUTION', 'output_file')
pth = INI_file[:-10]
expected_profile = pd.read_table(pth + environment_file,
sep='\t',
header=None)[1]
tss, tts, prot, genome_size = sim.load_genome(INI_file)
gene_expression = sim.start_transcribing(INI_file, output_dir, tss, tts,
prot, genome_size)
fitness = get_fitness(gene_expression, expected_profile)
with open(fitness_file, 'w') as f:
f.write("fitness\tgenome size\tmutation type\n")
for it in range(nbiter):
temperature *= temperature_rate
tss, tts, prot, genome_size, fitness, mutation_type = evol_main_loop(
tss, tts, prot, genome_size, fitness, p_inversion, temperature,
indel_size, expected_profile)
print("iteration : " + str(it) + "\tfitness : " + str(fitness))
# mutation_type is 0 if no changes occured 1 for inversion and 2 for indel
with open(fitness_file, 'a') as f:
f.write(
str(fitness) + "\t" + str(genome_size) + "\t" +
str(mutation_type) + "\n")
import sys import simulation as sim INI_file = sys.argv[1] output_dir = sys.argv[2] sim.start_transcribing(INI_file, output_dir)
import sys import simulation as sim INI_file = sys.argv[1] output_dir = sys.argv[2] (tss, tts, prot, genome_size) = sim.load_genome(INI_file) sim.start_transcribing(INI_file, output_dir, tss, tts, prot, genome_size)
def simulation(nb_simulations):
#definition des listes de stockage de variations de fitness
ancien_genome_optimal=[]
variation_fitness=[]
variation_nb_transcrits=[]
liste_changement_position={}
for simulation in range (nb_simulations):
[position_gene,pos_barriere,taille_genome,rapport_mutation,nb_transcrits,pas_espace,taille_indel]=sim.start_transcribing(INI_file)
fitness=calcul_fitness(fitness_optimal,nb_transcrits)
print("on en est a la simulation ",simulation)
#on calcule dans tous les cas un fitness de base
if (simulation==0):
liste_debut=[copy.deepcopy(position_gene) ,pos_barriere.copy(),taille_genome]
variation_nb_transcrits.append(nb_transcrits)
liste_changement_position["genome_originelle"]=position_gene
ancien_genome_optimal=[position_gene,pos_barriere,taille_genome]
elif application_monte_carlo(variation_fitness[-1],fitness):
#la mutation apporte une amelioration de la fitness ou avec une certaine probabilite est conservee meme si deletere
#on met alors a jour nos variables globales de variation de fitness
cle="generation"+str(simulation)
liste_changement_position[cle]=position_gene
variation_nb_transcrits.append(nb_transcrits)
ancien_genome_optimal=[position_gene,pos_barriere,taille_genome]
else:
#on est dans le cas ou il n'y a pas eu d'ameliorations de la fitness, dans ce cas, on revient a l'ancien genome le plus optimal
fonction_modification_fichiers(INI_file,ancien_genome_optimal)
variation_fitness.append(fitness)
#On cree notre nouveau genome, qui sera eventuellement conserve
#print("voila ce qu'on envoie a la mutation ",position_gene,pos_barriere,taille_genome,rapport_mut,pas_espace)
new_genome=v2.create_new_genome(position_gene,pos_barriere,int(taille_genome+1),rapport_mutation,int(pas_espace),int(taille_indel))
#print( "nouveau genome apres mutation est ",new_genome, "\n\n")
fonction_modification_fichiers(INI_file,new_genome)
return variation_fitness
#############main function#################
INI_file=sys.argv[1]
#fixation par avance de certains parametres
#on prend un seuil de 5%
#recuperation fitness optimal
fitness_optimal=recuperer_fitness_cible(INI_file)
[position_gene,pos_barriere,taille_genome,rapport_mutation,nb_transcrits,pas_espace,taille_indel]=sim.start_transcribing(INI_file)
liste_debut=[copy.deepcopy(position_gene) ,pos_barriere.copy(),taille_genome]
nb_simulations=int(sys.argv[2])
test_parametres = {"['SIMULATION','RNAPS_NB']":[3,7],"['MUTATION','rapport_mutation_insert_invert']":[75,90],"['SIMULATION','GYRASE_CONC']":[0.2,0.9],"['MUTATION', 'taille_indel']":[1,5]}
'''
test_parametres = {"['GLOBAL','DELTA_X']":[20], "['SIMULATION','SIM_TIME']":[1000],
"['MUTATION','rapport_mutation_insert_invert']":[0.2],"['SIMULATION','GYRASE_CONC']":[0.1],
"['MUTATION', 'taille_indel']":[1]}
valeurs_initiales = {"['SIMULATION','RNAPS_NB']":4, "['MUTATION','rapport_mutation_insert_invert']":60,"['SIMULATION','GYRASE_CONC']":0.3,