Dry Lab

Code

Interactive Python implementations of TRACER's computational models and simulations

Model Implementation Code

Explore the Python implementations of TRACER's computational models. All code is read-only for documentation purposes.

1# Concentration Profiler
2# Calculate max SynNotch and Inhibitor concentrations for various promoters
3
4promoters = {
5    'SFFV': {'synnotch': 248.388723, 'inhibitor': 295.647789},
6    'CAG': {'synnotch': 165.592482, 'inhibitor': 197.098526},
7    'CMV': {'synnotch': 144.893422, 'inhibitor': 172.461210},
8    'EF1a': {'synnotch': 99.355489, 'inhibitor': 118.259116},
9    'SV40': {'synnotch': 31.498570, 'inhibitor': 37.491568},
10    'PGK': {'synnotch': 16.199265, 'inhibitor': 19.281378},
11    'U6': {'synnotch': 11.699469, 'inhibitor': 13.925439}
12}
13
14for promoter, values in promoters.items():
15    print(f"[{promoter}] Max SynNotch concentration: {values['synnotch']:.6f} mM")
16    print(f"[{promoter}] Max Inhibitor concentration: {values['inhibitor']:.6f} mM\n")

About This Code

This code is provided for documentation and educational purposes. The implementations use standard scientific Python libraries including NumPy, SciPy, and SALib for ODE solving and sensitivity analysis. For the complete interactive notebook with widgets and visualizations, please refer to the original Jupyter notebook file.