aiming-lab/autoresearchclaw/flux-analyzer

import cobra
import cobra.io
import cobra.flux_analysis
import cobra.sampling
import pandas as pd
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
model = cobra.io.load_json_model("my_model.json")
fba_fluxes = pd.read_csv("fba_fluxes.csv", index_col=0)["flux_mmol_gDW_h"]
wt_growth = model.optimize().objective_value
print(f"Wild-type growth: {wt_growth:.4f} h^-1")

from cobra.flux_analysis import single_gene_deletion, double_gene_deletion
# --- Single gene essentiality ---
sg_deletion = single_gene_deletion(model)
sg_deletion.columns = ["growth", "status"]
sg_deletion["is_essential"] = sg_deletion["growth"] < 0.05 * wt_growth
sg_deletion["growth_fraction"] = sg_deletion["growth"] / wt_growth
essential_genes = sg_deletion[sg_deletion["is_essential"]]
print(f"Essential genes: {len(essential_genes)} / {len(model.genes)}")
sg_deletion.to_csv("gene_essentiality.csv")
# --- Double gene essentiality (synthetic lethality) ---
# Limit to a focused gene set to reduce compute time
target_genes = list(model.genes)[:50]   # adjust as needed
dg_deletion = double_gene_deletion(model, target_genes, target_genes)
dg_deletion.columns = ["growth", "status"]
dg_deletion["is_synthetic_lethal"] = dg_deletion["growth"] < 0.05 * wt_growth
dg_deletion.to_csv("double_gene_essentiality.csv")

from cobra.flux_analysis import single_reaction_deletion
sr_deletion = single_reaction_deletion(model)
sr_deletion.columns = ["growth", "status"]
sr_deletion["is_essential"] = sr_deletion["growth"] < 0.05 * wt_growth
essential_rxns = sr_deletion[sr_deletion["is_essential"]]
print(f"Essential reactions: {len(essential_rxns)} / {len(model.reactions)}")
sr_deletion.to_csv("reaction_essentiality.csv")

from cobra.flux_analysis import production_envelope
# Phase plane: glucose uptake vs. oxygen uptake
ppp = production_envelope(
    model,
    ["EX_glc__D_e", "EX_o2_e"],   # x and y axes
    objective=model.reactions.get_by_id("BIOMASS_Ec_iJO1366_core_53p95M"),
    points=20,
)
print(ppp.head())
# Plot heatmap
fig, ax = plt.subplots(figsize=(8, 6))
pivot = ppp.pivot_table(
    index="EX_o2_e", columns="EX_glc__D_e", values="flux_maximum"
)
im = ax.imshow(pivot.values, aspect="auto", origin="lower",
               cmap="viridis",
               extent=[ppp["EX_glc__D_e"].min(), ppp["EX_glc__D_e"].max(),
                       ppp["EX_o2_e"].min(),    ppp["EX_o2_e"].max()])
plt.colorbar(im, ax=ax, label="Growth rate (h$^{-1}$)")
ax.set_xlabel("Glucose uptake (mmol/gDW/h)")
ax.set_ylabel("O$_2$ uptake (mmol/gDW/h)")
ax.set_title("Phenotypic Phase Plane")
fig.tight_layout()
fig.savefig("phenotypic_phase_plane.pdf", dpi=300)
fig.savefig("phenotypic_phase_plane.png", dpi=150)
plt.close(fig)
print("PPP saved to phenotypic_phase_plane.pdf")

# OptGP sampler: Markov-chain Monte Carlo in flux cone
samples = cobra.sampling.sample(model, n=1000, method="optgp",
                                thinning=100, processes=4)
# samples is a DataFrame: rows = samples, columns = reaction IDs
samples.to_csv("flux_samples.csv", index=False)
# Violin plot for key central metabolism reactions
KEY_REACTIONS = ["PFK", "PGI", "PDH", "CS", "AKGDH",
                 "EX_glc__D_e", "EX_ac_e", "EX_co2_e"]
key_data = samples[[r for r in KEY_REACTIONS if r in samples.columns]]
fig, ax = plt.subplots(figsize=(10, 5))
ax.violinplot([key_data[c].values for c in key_data.columns],
              positions=range(len(key_data.columns)),
              showmedians=True)
ax.set_xticks(range(len(key_data.columns)))
ax.set_xticklabels(key_data.columns, rotation=45, ha="right")
ax.set_ylabel("Flux (mmol/gDW/h)")
ax.set_title("Flux Sampling Distribution (n=1000)")
fig.tight_layout()
fig.savefig("flux_sampling_violin.pdf", dpi=300)
plt.close(fig)
print("Flux sampling violin plot saved.")

pathway_flux = {}
for rxn in model.reactions:
    subsystem = rxn.subsystem or "Unknown"
    flux_val = abs(fba_fluxes.get(rxn.id, 0.0))
    pathway_flux[subsystem] = pathway_flux.get(subsystem, 0.0) + flux_val
pathway_df = (pd.Series(pathway_flux, name="total_abs_flux")
              .sort_values(ascending=False)
              .reset_index()
              .rename(columns={"index": "subsystem"}))
pathway_df.to_csv("pathway_flux_summary.csv", index=False)
# Bar chart of top 15 pathways
top15 = pathway_df.head(15)
fig, ax = plt.subplots(figsize=(10, 6))
ax.barh(top15["subsystem"][::-1], top15["total_abs_flux"][::-1],
        color="steelblue")
ax.set_xlabel("Sum of |flux| (mmol/gDW/h)")
ax.set_title("Top 15 Pathway Activities (FBA)")
fig.tight_layout()
fig.savefig("pathway_activity.pdf", dpi=300)
plt.close(fig)
print("Pathway activity chart saved.")

secretion = {}
for rxn in model.exchanges:
    flux = fba_fluxes.get(rxn.id, 0.0)
    if flux > 1e-6:   # positive = secretion
        met = list(rxn.metabolites)[0]
        secretion[rxn.id] = {
            "metabolite": met.name,
            "formula": met.formula,
            "flux_mmol_gDW_h": flux,
        }
sec_df = pd.DataFrame(secretion).T.sort_values("flux_mmol_gDW_h",
                                                ascending=False)
print("\nSecreted products:")
print(sec_df.to_string())
sec_df.to_csv("secretion_profile.csv")