def test_optimization_2():
sequence_path = os.path.join("tests", "data",
"test_optimization_sequence_2.fa")
sequence = str(load_record(sequence_path).seq)[:5500]
deluxe_dna = CommercialDnaOffer(
name="DeluxeDNA.com",
sequence_constraints=[SequenceLengthConstraint(max_length=4000)],
pricing=PerBasepairPricing(0.20),
lead_time=10,
)
cheap_dna = CommercialDnaOffer(
name="CheapDNA.com",
sequence_constraints=[
NoPatternConstraint(enzyme="BsaI"),
EnforceGCContent(0.3, 0.7, window=60),
],
pricing=PerBasepairPricing(0.10),
lead_time=15,
)
# BLOCKS TO CHUNKS ASSEMBLY
gibson_blocks_assembly_station = DnaAssemblyStation(
name="Gibson Blocks Assembly",
assembly_method=GibsonAssemblyMethod(
overhang_selector=FixedSizeSegmentSelector(10),
min_segment_length=1000,
max_segment_length=6000,
duration=8,
cost=16,
),
supplier=[deluxe_dna, cheap_dna],
coarse_grain=30,
fine_grain=False,
memoize=True,
# a_star_factor="auto",
)
quote_before = gibson_blocks_assembly_station.get_quote(sequence)
assert quote_before.price > 850
objective = OptimizeManufacturability(gibson_blocks_assembly_station)
problem = DnaOptimizationProblem(
sequence=sequence,
constraints=[EnforceTranslation(location=(0, 4998))],
objectives=[objective],
)
problem.randomization_threshold = 0 # Forces "random search" mode
problem.max_random_iters = 5
problem.optimize()
print("OPTIMIZATION DONE, GENERATING REPORT")
quote_after = gibson_blocks_assembly_station.get_quote(problem.sequence)
assert quote_after.price < 580
def test_EnforceSequence():
# Two enzymes, BsmBI(CGTCTC) is GC-rich, EcoRI(GAATTC) is GC-poor, which
# enzyme will be chosen and inserted in the sequence depends on the other
# constraint on GC content
numpy.random.seed(1234)
for symbol, nucleotides in [('W', 'AT'), ('S', 'GC')]:
n_nucleotides = 15
start = 50
location = (start, start + n_nucleotides)
problem = DnaOptimizationProblem(sequence=25 * "ATGC",
constraints=[
AvoidPattern("ATGC"),
AvoidPattern("AAA"),
AvoidPattern("GGG"),
EnforceSequence(n_nucleotides *
symbol,
location=location)
])
problem.max_random_iters = 10000
problem.resolve_constraints()
s, e = start, start + n_nucleotides
assert all([n in nucleotides for n in problem.sequence[s:e]])
def test_EnforceSequence():
# Two enzymes, BsmBI(CGTCTC) is GC-rich, EcoRI(GAATTC) is GC-poor, which
# enzyme will be chosen and inserted in the sequence depends on the other
# constraint on GC content
numpy.random.seed(1234)
for symbol, nucleotides in [("W", "AT"), ("S", "GC")]:
n_nucleotides = 15
start = 50
location = (start, start + n_nucleotides)
problem = DnaOptimizationProblem(
sequence=25 * "ATGC",
constraints=[
AvoidPattern("ATGC"),
AvoidPattern("AAA"),
AvoidPattern("GGG"),
EnforceSequence(n_nucleotides * symbol, location=location),
],
)
problem.max_random_iters = 10000
problem.resolve_constraints()
s, e = start, start + n_nucleotides
assert all([n in nucleotides for n in problem.sequence[s:e]])
# Test -1 strand:
seq = "ATG" + "CAG" + "AGCAAGGTGCTGCT"
problem = DnaOptimizationProblem(
sequence=seq,
constraints=[
EnforcePatternOccurence(
pattern="CTG", # CAG on strand +1
occurences=2,
strand=-1,
location=Location(start=0, end=50),
)
],
)
assert not problem.all_constraints_pass()
problem.resolve_constraints()
assert problem.all_constraints_pass()
def test_optimization_1():
company_ingen = CommercialDnaOffer(
name="Company InGen",
pricing=PerBasepairPricing(0.08),
sequence_constraints=[NoPatternConstraint(enzyme="AarI")],
)
company_delux = CommercialDnaOffer(
name="Company Delux",
pricing=PerBasepairPricing(0.66),
sequence_constraints=[],
)
assembly_station = DnaAssemblyStation(
name="Gibson Assembly Station",
assembly_method=GibsonAssemblyMethod(
overhang_selector=FixedSizeSegmentSelector(20),
min_segment_length=200,
max_segment_length=1200,
),
supplier=[company_ingen, company_delux],
coarse_grain=20,
# a_star_factor="auto",
)
sequence_path = os.path.join("tests", "data",
"test_optimization_sequence_1.fa")
sequence = load_record(sequence_path)
objective = OptimizeManufacturability(assembly_station)
problem = DnaOptimizationProblem(sequence=sequence, objectives=[objective])
quote = objective.get_quote(problem)
score = problem.objective_scores_sum()
assert -367 < score < -366
problem.randomization_threshold = 0
problem.max_random_iters = 5
problem.optimize()
score = problem.objective_scores_sum()
assert -244 < score < -243
AvoidPattern,
EnforceGCContent,
)
import numpy
# We setup the randomizer to always get the same sequence
numpy.random.seed(123)
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(10000),
constraints=[
AvoidPattern("BsaI_site"),
EnforceGCContent(mini=0.3, maxi=0.7, window=50),
],
objectives=[EnforceGCContent(target=0.4)],
)
print("\n\n=== Status before optimization ===")
print(problem.constraints_text_summary())
print(problem.objectives_text_summary())
print("Now solving constraints...")
problem.resolve_constraints()
print("Done. Now optimizing objectives...")
problem.max_random_iters = 10000
problem.optimize()
print("\n\n=== Status after optimization ===\n")
print(problem.constraints_text_summary())
print(problem.objectives_text_summary())
