Synthetic Systems

What I do

• Design complex synthetic biological systems
• Model and simulate engineered networks
• Integrate multiple subsystems
• Optimize system performance
• Apply systems engineering principles
• Build toward synthetic life

When to use me

• When designing complex gene networks
• When modeling synthetic systems
• When integrating multiple components
• When optimizing engineered pathways
• When building diagnostic devices
• When creating engineered organisms

Key Concepts

Systems Engineering

Design-Build-Test-Learn (DBTL)

• Design: Computational modeling
• Build: DNA synthesis, assembly
• Test: Experimental characterization
• Learn: Data analysis, iteration

Modeling Approaches

# Example: ODE model for synthetic gene circuit
def gene_circuit_model(y, t, params):
    """
    d[protein]/dt = k_transcription * promoter - k_translation * protein
    """
    mRNA, protein = y
    
    # Parameters
    alpha_m, alpha_p, delta_m, delta_p = params
    
    # Equations
    dm_dt = alpha_m - delta_m * mRNA
    dp_dt = alpha_p * mRNA - delta_p * protein
    
    return [dm_dt, dp_dt]

# Simulation
from scipy.integrate import odeint
t = np.linspace(0, 100, 1000)
solution = odeint(gene_circuit_model, [0, 0], t, args=(params,))

Abstraction Levels

• Parts: Promoters, RBS, terminators
• Devices: Combinational circuits
• Systems: Integrated functions
• Chassis: Host organisms

Standardization

• BBParts: Registry of standard parts
• Golden Gate: Type IIS assembly
• Gibson: Overlap-based assembly
• PCR: Amplification
• DNA synthesis: Gene synthesis

Applications

• Programmable therapeutics
• Biosensors
• Bioprocess optimization
• Engineered metabolism
• Artificial cells
• Synthetic ecosystems