import os 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()