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 ```python # 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