synthetic-biology

Synthetic Biology: Design & Simulation

Overview

Synthetic Biology provides computational tools for designing and simulating engineered biological systems. This skill covers codon optimization with species-specific usage tables, gene circuit ODE modeling (repressilator, toggle switch, inducible promoters) with growth dilution coupling, SBML model creation and validation using python-libsbml, bifurcation analysis for bistable circuits, barcode sequencing fitness analysis, and genome engineering with expression cassette insertion. All simulations produce quantitative outputs suitable for guiding experimental design.

When to Use This Skill

• Optimizing gene sequences for heterologous expression (codon adaptation)
• Simulating gene circuit dynamics (toggle switches, repressilators, inducible systems)
• Creating standardized SBML models of biological networks
• Analyzing bistability and bifurcation behavior in synthetic circuits
• Processing barcode sequencing data for fitness landscape analysis
• Designing expression cassettes and generating annotated plasmid maps
• Sensitivity analysis of circuit parameters for robust design

Related Skills: For constraint-based metabolic modeling use cobrapy. For sequence manipulation and file parsing use biopython. For molecular cloning simulation use molecular-cloning.

Installation

uv pip install python-libsbml scipy biopython numpy pandas matplotlib

Quick Start

import numpy as np
from scipy.integrate import solve_ivp

# Toggle switch: two mutually repressing genes
def toggle_switch(t, y, alpha1, alpha2, beta, n, gamma):
    u, v = y  # Protein concentrations
    du = alpha1 / (1 + v**n) - (beta + gamma) * u  # gamma = growth dilution
    dv = alpha2 / (1 + u**n) - (beta + gamma) * v
    return [du, dv]

sol = solve_ivp(toggle_switch, [0, 50], [0.1, 3.0],
                args=(5.0, 5.0, 0.5, 2.0, 0.1),
                t_eval=np.linspace(0, 50, 500))

print(f"Final state: u={sol.y[0,-1]:.3f}, v={sol.y[1,-1]:.3f}")
print(f"Bistable: {'Yes' if abs(sol.y[0,-1] - sol.y[1,-1]) > 0.5 else 'No'}")