Virtual Lab: Synthetic Biology & Genomics

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EXPLORER

📄 main.py

📄 model_arch.py

📁 datasets

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# CRISPR-Cas9 genome editing
import numpy as np
import matplotlib.pyplot as plt
import io
import base64

# Simulate CRISPR-Cas9 cleavage efficiency across a gene
positions = np.arange(0, 500, 10)
base_efficiency = np.random.uniform(20, 50, len(positions))
peak = 95 * np.exp(-((positions - 250)**2) / (2 * 20**2))
efficiency = np.clip(base_efficiency + peak, 0, 100)

plt.style.use("dark_background")
fig, ax = plt.subplots(figsize=(6, 4))
ax.plot(positions, efficiency, color="#10b981", marker="o", linestyle="-", markersize=4)

# Highlight optimal site
optimal_idx = np.argmax(efficiency)
ax.axvline(x=positions[optimal_idx], color="#f59e0b", linestyle="--", alpha=0.7, label="Optimal sgRNA Target")

ax.set_title("CRISPR-Cas9 sgRNA Cleavage Efficiency")
ax.set_xlabel("Gene Position (bp)")
ax.set_ylabel("Predicted Efficiency (%)")
ax.legend()
ax.grid(True, alpha=0.2)

buf = io.BytesIO()
plt.savefig(buf, format="png", bbox_inches="tight", transparent=True)
buf.seek(0)
render_image(base64.b64encode(buf.read()).decode("utf-8"))

print(f"Analysis Complete. Optimal target found at bp {positions[optimal_idx]} with {efficiency[optimal_idx]:.1f}% predicted efficiency.")

root@phd-lab-vbox:~# python main.py

Waiting for execution...

root@phd-lab-vbox:~#