def setUp(self):
self.trna_space = Test_Space(get_uniform_mutation_matrix(3, .0001))
self.aars_space = Test_Space(get_uniform_mutation_matrix(3, .0001))
self.code = Code([1, 2], [1, 2], self.trna_space, self.aars_space)
self.mutator = Code_Mutator(self.code, self.aars_space.mutation_matrix,
self.trna_space.mutation_matrix)
class Code_Mutator_Test(unittest.TestCase):
def setUp(self):
self.trna_space = Test_Space(get_uniform_mutation_matrix(3, .0001))
self.aars_space = Test_Space(get_uniform_mutation_matrix(3, .0001))
self.code = Code([1, 2], [1, 2], self.trna_space, self.aars_space)
self.mutator = Code_Mutator(self.code, self.aars_space.mutation_matrix,
self.trna_space.mutation_matrix)
def test_mutant_codes(self):
""" Tests if all possible code variants are created. """
possible_codes = self.mutator.get_possible_codes()
# Each code contains 4 items with 3 variants for each item
# variants can be reused
# so 3^4 = 81
self.assertEqual(81, len(possible_codes))
def test_mutant_probability(self):
""" Does basic checks on the mutation probability from one code to another. """
# No mutations
to_code = Code([1, 2], [1, 2], self.trna_space, self.aars_space)
self.assertAlmostEqual(.9998**4,
self.mutator.mutation_probability(to_code))
# 1 mutation
to_code = Code([1, 1], [1, 2], self.trna_space, self.aars_space)
self.assertAlmostEqual(.9998**3 * .0001,
self.mutator.mutation_probability(to_code))
# 4 mutations
to_code = Code([0, 0], [0, 0], self.trna_space, self.aars_space)
self.assertAlmostEqual(.0001**4,
self.mutator.mutation_probability(to_code))
def test_custom_mutant_probability(self):
""" Checks if the mutation probability uses the correct indices. """
aars_mutation = self.aars_space.mutation_matrix.copy()
aars_mutation[0, :] = [.75, .15, .10]
from_code = Code([1, 2], [0, 1], self.trna_space, self.aars_space)
custom_mutator = Code_Mutator(from_code, aars_mutation,
self.trna_space.mutation_matrix)
to_code = Code([1, 2], [1, 1], self.trna_space, self.aars_space)
self.assertAlmostEqual(.9998**3 * .15,
custom_mutator.mutation_probability(to_code))
class Code_Mutator_Test(unittest.TestCase):
def setUp(self):
self.trna_space = Test_Space(get_uniform_mutation_matrix(3, .0001))
self.aars_space = Test_Space(get_uniform_mutation_matrix(3, .0001))
self.code = Code([1,2], [1,2], self.trna_space, self.aars_space)
self.mutator = Code_Mutator(self.code, self.aars_space.mutation_matrix,
self.trna_space.mutation_matrix)
def test_mutant_codes(self):
""" Tests if all possible code variants are created. """
possible_codes = self.mutator.get_possible_codes()
# Each code contains 4 items with 3 variants for each item
# variants can be reused
# so 3^4 = 81
self.assertEqual(81, len(possible_codes))
def test_mutant_probability(self):
""" Does basic checks on the mutation probability from one code to another. """
# No mutations
to_code = Code([1,2], [1,2], self.trna_space, self.aars_space)
self.assertAlmostEqual(.9998 ** 4, self.mutator.mutation_probability(to_code))
# 1 mutation
to_code = Code([1,1], [1,2], self.trna_space, self.aars_space)
self.assertAlmostEqual(.9998 ** 3 * .0001, self.mutator.mutation_probability(to_code))
# 4 mutations
to_code = Code([0,0], [0,0], self.trna_space, self.aars_space)
self.assertAlmostEqual(.0001 ** 4, self.mutator.mutation_probability(to_code))
def test_custom_mutant_probability(self):
""" Checks if the mutation probability uses the correct indices. """
aars_mutation = self.aars_space.mutation_matrix.copy()
aars_mutation[0,:] = [.75, .15, .10]
from_code = Code([1,2], [0,1], self.trna_space, self.aars_space)
custom_mutator = Code_Mutator(from_code, aars_mutation,
self.trna_space.mutation_matrix)
to_code = Code([1,2], [1,1], self.trna_space, self.aars_space)
self.assertAlmostEqual(.9998 ** 3 * .15, custom_mutator.mutation_probability(to_code))
def test_custom_mutant_probability(self):
""" Checks if the mutation probability uses the correct indices. """
aars_mutation = self.aars_space.mutation_matrix.copy()
aars_mutation[0,:] = [.75, .15, .10]
from_code = Code([1,2], [0,1], self.trna_space, self.aars_space)
custom_mutator = Code_Mutator(from_code, aars_mutation,
self.trna_space.mutation_matrix)
to_code = Code([1,2], [1,1], self.trna_space, self.aars_space)
self.assertAlmostEqual(.9998 ** 3 * .15, custom_mutator.mutation_probability(to_code))
def test_custom_mutant_probability(self):
""" Checks if the mutation probability uses the correct indices. """
aars_mutation = self.aars_space.mutation_matrix.copy()
aars_mutation[0, :] = [.75, .15, .10]
from_code = Code([1, 2], [0, 1], self.trna_space, self.aars_space)
custom_mutator = Code_Mutator(from_code, aars_mutation,
self.trna_space.mutation_matrix)
to_code = Code([1, 2], [1, 1], self.trna_space, self.aars_space)
self.assertAlmostEqual(.9998**3 * .15,
custom_mutator.mutation_probability(to_code))
def setUp(self):
self.trna_space = Test_Space(get_uniform_mutation_matrix(3, .0001))
self.aars_space = Test_Space(get_uniform_mutation_matrix(3, .0001))
self.code = Code([1,2], [1,2], self.trna_space, self.aars_space)
self.mutator = Code_Mutator(self.code, self.aars_space.mutation_matrix,
self.trna_space.mutation_matrix)
def get_transition_probabilities(self):
""" Returns a dictionary of the probability of going from the current code
to a mutant code in one time step. """
# Keep things cached in case the code doesn't evolve over one step
if self._code_mutator is None:
self._code_mutator = Code_Mutator(self._current_code, self._mu)
mutant_prob = self._code_mutator.get_one_gene_mutation_probabilities(
)
self._transition_probabilities = {
to_code: self._transition_probability(to_code,
mutant_prob[to_code])
for to_code in mutant_prob
}
# Handles the corner case in _transition_probability()
self._transition_probabilities[self._current_code] = 1.0 - sum(
self._transition_probabilities.values())
return self._transition_probabilities
def run(self):
trnas = 8
aarss = 8
phi = 0.9
mu = .01
msg_mu = .1
pop = 100
trna_names = ["tra_" + str(i) for i in xrange(trnas)]
codon_names = ["codon_" + str(i) for i in xrange(trnas)]
aars_names = ["aars_" + str(i) for i in xrange(aarss)]
aa_names = ["aa_" + str(i) for i in xrange(aarss)]
site_names = ["site_" + str(i) for i in xrange(aarss)]
trna_space = Bit_TRNA_Space(trna_names, codon_names)
aars_space = Id_AARS_Space(aars_names, aa_names)
trna = range(trnas)
trna[1] = 0
trna[2] = 0
code = Code(trna, range(aarss), trna_space, aars_space)
site_types = Ring_Site_Types(phi, zip(aa_names, np.arange(0, 1, .125)),
zip(site_names, np.arange(0, 1, .125)),
[1] * len(site_names))
self.evolver = Evolver(
code, site_types, mu,
get_ring_mutation_matrix(len(codon_names), msg_mu), pop, self.rng)
mut = Code_Mutator(code, mu)
trna[0] = 1
code2 = Code(trna, range(aarss), trna_space, aars_space)
#print mut.mutation_probability(code2)
#print [trna_space.mutation_probability(0, i, mu) for i in xrange(8)]
# mut = Code_Mutator(code, mu)
# code_dict = mut.get_one_gene_mutation_probabilities()
#for code in code_dict:
# print code, code_dict[code]
for i in xrange(10000):
self.evolver.step_time()
def get_transition_probabilities(self):
""" Returns a dictionary of the probability of going from the current code
to a mutant code in one time step. """
# Keep things cached in case the code doesn't evolve over one step
if self._code_mutator is None:
self._code_mutator = Code_Mutator(self._current_code, self._mu)
mutant_prob = self._code_mutator.get_one_gene_mutation_probabilities()
self._transition_probabilities = {to_code:self._transition_probability(to_code, mutant_prob[to_code])
for to_code in mutant_prob}
# Handles the corner case in _transition_probability()
self._transition_probabilities[self._current_code] = 1.0 - sum(self._transition_probabilities.values())
return self._transition_probabilities
class Evolver(object):
def __init__(self, initial_code, site_types, mu, message_mutation_matrix,
pop_size, rng):
if pop_size < 1:
raise ValueError("The population size must be greater than 0.")
if not 0 <= mu <= 1:
raise RuntimeError("Mu must be in the range [0,1].")
if initial_code._trna_space.codons != message_mutation_matrix.shape[0] or \
initial_code._trna_space.codons != message_mutation_matrix.shape[1]:
raise ValueError(
"The message mutation matrix must be square with {} "
"codons per side. It is currently {}".format(
initial_code._trna_space.codons,
message_mutation_matrix.shape))
self._current_code = initial_code
self._fitness_matrix = site_types.fitness_matrix
self._message_mutation_matrix = message_mutation_matrix
self._site_weights = site_types.weights
self._pop_size = pop_size
self._rng = rng
self._mu = mu
# All these values depend on the current code
# and are created/destroyed as needed
self._last_messages = None
self._current_code_fitness = None
self._code_mutator = None
self._frozen = False
def _code_fitness(self, code, codon_usage=None):
""" Gets the overall fitness of a code at the given codon usage,
if no codon usage is given the codon usage of the
currently fixed code at equilibrium is used. """
fitness_contributions = None
if codon_usage is None:
if self._last_messages is None:
if code == self._current_code:
msgs = Messages(self._current_code.effective_code_matrix,
self._fitness_matrix,
self._message_mutation_matrix)
msgs.calculate_at_equilibrium()
self._last_messages = msgs
fitness_contributions = msgs.fitness_contributions
else:
self._code_fitness(self._current_code)
if fitness_contributions is None:
msgs = Messages(code.effective_code_matrix, self._fitness_matrix,
self._message_mutation_matrix)
msgs.calculate_at_codon_usage(self._last_messages.codon_usage)
fitness_contributions = msgs.fitness_contributions
overall_fitness = self._overall_fitness(fitness_contributions)
if codon_usage is None and code == self._current_code:
self._current_code_fitness = overall_fitness
return overall_fitness
def _overall_fitness(self, fitness_contributions):
""" Eq5 SellaArdell06, uses the fitness contribution of each site
and the accompanying weights of each site to get an overall
fitness of the code. """
return np.prod([
fitness_contributions[site]**self._site_weights[site]
for site in xrange(len(self._site_weights))
])
def _transition_probability(self, to, mutation_probability):
""" Returns the probability a code mutates from the current code
to the other code. This is just Eq3 Sella09. """
if self._current_code == to: # This case must be handled when the transition matrix is already built
# It is really 1 - summation(transition_probability(from_, to),
# for all potential values of end)
return 0
if mutation_probability == 0.0:
return 0
fix_probability = self._fixation_probability(to)
if fix_probability == 0.0:
return 0
trans_probability = self._pop_size * fix_probability * \
mutation_probability
#trans_probability = np.log(self._pop_size) + np.log(fix_probability) + \
# np.log(mutation_probability)
#trans_probability = np.exp(trans_probability)
assert 0 <= trans_probability <= 1, "Transition probability was not in the range [0,1]."
return trans_probability
def _fixation_probability(self, to):
""" Returns the proability of the next fixed code being the code passed
Eq1 Sella09 - Fixation probability of a Moran birth-death process. """
if self._last_messages is None:
# sets self._last_messages
# and self._current_code_fitness
self._code_fitness(self._current_code)
to_fitness = self._code_fitness(to, self._last_messages.codon_usage)
if to_fitness == 0.0:
# This is the limit of the function below
# as the "to_fitness" becomes 0
return 0
assert self._current_code_fitness > 0, \
"The fitness of the current code should always be greater than 0."
if np.isclose(self._current_code_fitness, to_fitness):
# This is the limit of the function below
# as the fitnesses become equal
return 1.0 / self._pop_size
fitness_ratio = self._current_code_fitness / to_fitness
# Check if calculation will overflow
if (self._pop_size * np.log10(fitness_ratio)
) > 308: # largest number floats can represent ~10^308
return 0 # Same limit as above for to_fitness
# Calculates the Moran fixation probability
num = 1 - fitness_ratio
dem = 1 - (fitness_ratio)**self._pop_size
fix_prob = num / dem
assert 0 <= fix_prob <= 1, "Fixation probability must be in the range [0,1]."
return num / dem
def get_transition_probabilities(self):
""" Returns a dictionary of the probability of going from the current code
to a mutant code in one time step. """
# Keep things cached in case the code doesn't evolve over one step
if self._code_mutator is None:
self._code_mutator = Code_Mutator(self._current_code, self._mu)
mutant_prob = self._code_mutator.get_one_gene_mutation_probabilities(
)
self._transition_probabilities = {
to_code: self._transition_probability(to_code,
mutant_prob[to_code])
for to_code in mutant_prob
}
# Handles the corner case in _transition_probability()
self._transition_probabilities[self._current_code] = 1.0 - sum(
self._transition_probabilities.values())
return self._transition_probabilities
def step_time(self):
""" This function steps one generation allowing the current code to
potentially mutate into another code. """
#if self.frozen:
# return
transition_probabilities = self.get_transition_probabilities()
range_bottom = 0
random_num = self._rng.random()
code_changed = False
possible_transitions = 0
for to_code in transition_probabilities:
range_top = transition_probabilities[to_code] + range_bottom
# Avoid infinite loop
if transition_probabilities[to_code] > 0:
possible_transitions += 1
if range_bottom <= random_num <= range_top:
code_changed = True
if to_code != self._current_code:
self._current_code = to_code
# Invalidate all variables that depend on the current code
self._last_messages = None
self._current_code_fitness = None
self._code_mutator = None
break
range_bottom = range_top
#if possible_transitions <= 1:
# self._frozen = True
@property
def current_code(self):
return self._current_code
@property
def frozen(self):
return self._frozen
class Evolver(object):
def __init__(self, initial_code, site_types, mu, message_mutation_matrix, pop_size, rng):
if pop_size < 1:
raise ValueError("The population size must be greater than 0.")
if not 0 <= mu <= 1:
raise RuntimeError("Mu must be in the range [0,1].")
if initial_code._trna_space.codons != message_mutation_matrix.shape[0] or \
initial_code._trna_space.codons != message_mutation_matrix.shape[1]:
raise ValueError("The message mutation matrix must be square with {} "
"codons per side. It is currently {}".format(initial_code._trna_space.codons,
message_mutation_matrix.shape))
self._current_code = initial_code
self._fitness_matrix = site_types.fitness_matrix
self._message_mutation_matrix = message_mutation_matrix
self._site_weights = site_types.weights
self._pop_size = pop_size
self._rng = rng
self._mu = mu
# All these values depend on the current code
# and are created/destroyed as needed
self._last_messages = None
self._current_code_fitness = None
self._code_mutator = None
self._frozen = False
def _code_fitness(self, code, codon_usage = None):
""" Gets the overall fitness of a code at the given codon usage,
if no codon usage is given the codon usage of the
currently fixed code at equilibrium is used. """
fitness_contributions = None
if codon_usage is None:
if self._last_messages is None:
if code == self._current_code:
msgs = Messages(self._current_code.effective_code_matrix,
self._fitness_matrix,
self._message_mutation_matrix)
msgs.calculate_at_equilibrium()
self._last_messages = msgs
fitness_contributions = msgs.fitness_contributions
else:
self._code_fitness(self._current_code)
if fitness_contributions is None:
msgs = Messages(code.effective_code_matrix,
self._fitness_matrix,
self._message_mutation_matrix)
msgs.calculate_at_codon_usage(self._last_messages.codon_usage)
fitness_contributions = msgs.fitness_contributions
overall_fitness = self._overall_fitness(fitness_contributions)
if codon_usage is None and code == self._current_code:
self._current_code_fitness = overall_fitness
return overall_fitness
def _overall_fitness(self, fitness_contributions):
""" Eq5 SellaArdell06, uses the fitness contribution of each site
and the accompanying weights of each site to get an overall
fitness of the code. """
return np.prod([fitness_contributions[site]**self._site_weights[site]
for site in xrange(len(self._site_weights))])
def _transition_probability(self, to, mutation_probability):
""" Returns the probability a code mutates from the current code
to the other code. This is just Eq3 Sella09. """
if self._current_code == to: # This case must be handled when the transition matrix is already built
# It is really 1 - summation(transition_probability(from_, to),
# for all potential values of end)
return 0
if mutation_probability == 0.0:
return 0
fix_probability = self._fixation_probability(to)
if fix_probability == 0.0:
return 0
trans_probability = self._pop_size * fix_probability * \
mutation_probability
#trans_probability = np.log(self._pop_size) + np.log(fix_probability) + \
# np.log(mutation_probability)
#trans_probability = np.exp(trans_probability)
assert 0 <= trans_probability <= 1, "Transition probability was not in the range [0,1]."
return trans_probability
def _fixation_probability(self, to):
""" Returns the proability of the next fixed code being the code passed
Eq1 Sella09 - Fixation probability of a Moran birth-death process. """
if self._last_messages is None:
# sets self._last_messages
# and self._current_code_fitness
self._code_fitness(self._current_code)
to_fitness = self._code_fitness(to, self._last_messages.codon_usage)
if to_fitness == 0.0:
# This is the limit of the function below
# as the "to_fitness" becomes 0
return 0
assert self._current_code_fitness > 0, \
"The fitness of the current code should always be greater than 0."
if np.isclose(self._current_code_fitness, to_fitness):
# This is the limit of the function below
# as the fitnesses become equal
return 1.0 / self._pop_size
fitness_ratio = self._current_code_fitness / to_fitness
# Check if calculation will overflow
if (self._pop_size * np.log10(fitness_ratio)) > 308: # largest number floats can represent ~10^308
return 0 # Same limit as above for to_fitness
# Calculates the Moran fixation probability
num = 1 - fitness_ratio
dem = 1 - (fitness_ratio)**self._pop_size
fix_prob = num / dem
assert 0 <= fix_prob <= 1, "Fixation probability must be in the range [0,1]."
return num / dem
def get_transition_probabilities(self):
""" Returns a dictionary of the probability of going from the current code
to a mutant code in one time step. """
# Keep things cached in case the code doesn't evolve over one step
if self._code_mutator is None:
self._code_mutator = Code_Mutator(self._current_code, self._mu)
mutant_prob = self._code_mutator.get_one_gene_mutation_probabilities()
self._transition_probabilities = {to_code:self._transition_probability(to_code, mutant_prob[to_code])
for to_code in mutant_prob}
# Handles the corner case in _transition_probability()
self._transition_probabilities[self._current_code] = 1.0 - sum(self._transition_probabilities.values())
return self._transition_probabilities
def step_time(self):
""" This function steps one generation allowing the current code to
potentially mutate into another code. """
#if self.frozen:
# return
transition_probabilities = self.get_transition_probabilities()
range_bottom = 0
random_num = self._rng.random()
code_changed = False
possible_transitions = 0
for to_code in transition_probabilities:
range_top = transition_probabilities[to_code] + range_bottom
# Avoid infinite loop
if transition_probabilities[to_code] > 0:
possible_transitions += 1
if range_bottom <= random_num <= range_top:
code_changed = True
if to_code != self._current_code:
self._current_code = to_code
# Invalidate all variables that depend on the current code
self._last_messages = None
self._current_code_fitness = None
self._code_mutator = None
break
range_bottom = range_top
#if possible_transitions <= 1:
# self._frozen = True
@property
def current_code(self):
return self._current_code
@property
def frozen(self):
return self._frozen