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import json
import numpy as np
import pandas as pd
from scipy.stats import mannwhitneyu, spearmanr, ttest_ind
RESULTS_DIR = "results"
# Population parameters (S. cerevisiae, Lynch et al. 2008)
NE = 10000
MU = 1e-9 # per bp per generation
GENERATIONS = 1000
REPLICATES = 100
SEED = 42
# Selection coefficients derived from NEM-based dN/dS proxy (2.377)
# s = (omega - 1) / (omega + 1) / (2 * Ne)
DNDS_CODING = 2.377
S_CODING = (DNDS_CODING - 1) / (DNDS_CODING + 1) / (2 * NE)
S_REGULATORY = S_CODING * 3 # 3-fold stronger on regulatory sequences
DRUG_EFFLUX_GENES = {
"PDR5", "PDR10", "PDR11", "PDR12", "PDR15", "PDR18", "SNQ2", "YOR1", "YCF1"
}
def load_nem_data():
path = os.path.join(RESULTS_DIR, "nem_comprehensive_summary.csv")
if not os.path.exists(path):
raise FileNotFoundError(f"{path} not found. Run 02_nem_analysis.py first.")
nem_df = pd.read_csv(path)
gene_df = (
nem_df[nem_df["region"] == "promoter"]
.groupby("gene")
.agg(nem_density=("nem_density_per_kb", "mean"),
total_nems=("nem_count", "sum"))
.reset_index()
)
gene_df["is_drug_efflux"] = gene_df["gene"].isin(DRUG_EFFLUX_GENES)
return gene_df
def wright_fisher_step(freq, s, Ne, mu):
fitness = 1 + s * freq
mean_w = np.mean(fitness)
freq_after_selection = freq * (1 + s) / mean_w
freq_after_mutation = freq_after_selection + mu * (1 - 2 * freq_after_selection)
freq_after_drift = np.random.binomial(
2 * Ne, np.clip(freq_after_mutation, 0, 1)
) / (2 * Ne)
return np.clip(freq_after_drift, 0, 1)
def simulate_gene(nem_density, nem_density_min, nem_density_max,
Ne=NE, mu=MU, generations=GENERATIONS,
replicates=REPLICATES, rng=None):
if rng is None:
rng = np.random.default_rng(SEED)
# Scale selection coefficient by NEM density relative to gene set
nem_range = nem_density_max - nem_density_min
if nem_range > 0:
constraint = (nem_density - nem_density_min) / nem_range
else:
constraint = 0.5
s = -S_REGULATORY * constraint
trajectories = np.zeros((replicates, generations))
for rep in range(replicates):
freq = float(rng.uniform(0.3, 0.7))
for gen in range(generations):
fitness = 1 + s * freq
mean_w = fitness # single locus
freq = freq * (1 + s) / mean_w
freq += mu * (1 - 2 * freq)
freq = float(np.random.binomial(2 * Ne, np.clip(freq, 0, 1))) / (2 * Ne)
trajectories[rep, gen] = freq
return trajectories
def main():
rng = np.random.default_rng(SEED)
np.random.seed(SEED)
gene_df = load_nem_data()
nem_min = gene_df["nem_density"].min()
nem_max = gene_df["nem_density"].max()
print(f"Simulating {len(gene_df)} genes × {REPLICATES} replicates × "
f"{GENERATIONS} generations...")
trajectory_records = []
final_freq_records = []
for _, row in gene_df.iterrows():
gene = row["gene"]
trajectories = simulate_gene(
row["nem_density"], nem_min, nem_max,
rng=rng
)
mean_traj = trajectories.mean(axis=0)
std_traj = trajectories.std(axis=0)
final_freqs = trajectories[:, -1]
for gen in range(0, GENERATIONS, 50):
trajectory_records.append({
"gene": gene,
"generation": gen,
"mean_freq": round(float(mean_traj[gen]), 4),
"std_freq": round(float(std_traj[gen]), 4),
"is_drug_efflux": bool(row["is_drug_efflux"]),
"nem_density": round(float(row["nem_density"]), 2),
})
final_freq_records.append({
"gene": gene,
"mean_final_freq": round(float(final_freqs.mean()), 4),
"std_final_freq": round(float(final_freqs.std()), 4),
"is_drug_efflux": bool(row["is_drug_efflux"]),
"nem_density": round(float(row["nem_density"]), 2),
"selection_coefficient": round(
float(-S_REGULATORY * max(0, (row["nem_density"] - nem_min) / max(nem_max - nem_min, 1e-9))),
8
),
})
traj_df = pd.DataFrame(trajectory_records)
final_df = pd.DataFrame(final_freq_records)
traj_df.to_csv(os.path.join(RESULTS_DIR, "wf_trajectories.csv"), index=False)
final_df.to_csv(os.path.join(RESULTS_DIR, "wf_final_frequencies.csv"), index=False)
# Category comparison: drug efflux vs other
drug_final = final_df[final_df["is_drug_efflux"]]["mean_final_freq"].values
other_final = final_df[~final_df["is_drug_efflux"]]["mean_final_freq"].values
t_stat, p_ttest = ttest_ind(drug_final, other_final)
delta = float(np.mean(other_final) - np.mean(drug_final))
# NEM density vs final frequency correlation
rho_nem, p_nem = spearmanr(final_df["nem_density"], final_df["mean_final_freq"])
summary = {
"parameters": {
"Ne": NE,
"mu_per_bp_per_gen": MU,
"generations": GENERATIONS,
"replicates": REPLICATES,
"dnds_proxy_coding": DNDS_CODING,
"s_coding": float(S_CODING),
"s_regulatory": float(S_REGULATORY),
"regulatory_to_coding_ratio": 3.0,
},
"results": {
"drug_efflux_mean_final_freq": round(float(np.mean(drug_final)), 4),
"drug_efflux_std_final_freq": round(float(np.std(drug_final)), 4),
"other_mean_final_freq": round(float(np.mean(other_final)), 4),
"other_std_final_freq": round(float(np.std(other_final)), 4),
"freq_delta": round(delta, 4),
"ttest_t": round(float(t_stat), 3),
"ttest_p": float(p_ttest),
"nem_freq_spearman_rho": round(float(rho_nem), 4),
"nem_freq_spearman_p": float(p_nem),
},
}
with open(os.path.join(RESULTS_DIR, "wf_simulation_summary.json"), "w") as f:
json.dump(summary, f, indent=2)
print(f"Drug efflux mean freq: {np.mean(drug_final):.4f} "
f"Other: {np.mean(other_final):.4f} "
f"Δ={delta:.4f} t={t_stat:.3f} p={p_ttest:.4f}")
print(f"NEM density vs final freq: rho={rho_nem:.4f} p={p_nem:.4f}")
if __name__ == "__main__":
main()
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