Python scripts that process BFM traces and compares the results with different prescribed kinetic metamodels, currently Hill-Langmuir, speed-rate, and two-state model.
- data_analysis.py Loads experimental traces and creates summary statistics that can be used to compare with theoretical results through the function
DistanceLikelihood() - integration_ME.py Integrates the Master Equation for different kinetic models through the diagonalization of the rate matrix. It also includes routines to manipulate the resulting probability vectors, integrate Mean-Field approximations, and reproduce stochastic trajectories.
- models.py Properties of different models related them to the respective father metamodel e.g. relating the cathbond model to the generic two-state model. It also includes the routines to define, sample and evaluate the prior distributions used in the ABC.
- abc_smc.py Aproximate Bayesian Computation using Sequential Monte Carlo sampling for the models defined in models.py and using the distance function defined in data_analysis.py
- plots.py Plots the results of the ABC-SMC.
The external library versions used are:
scipy==1.5.0
seaborn==0.10.1
matplotlib==3.2.2
numpy==1.18.5
tqdm==4.47.0
pandas==1.0.5
p_tqdm==1.3.3 - Only required to use parallel mapping in the ABC-SMC sampling
- Ruben Perez-Carrasco - 2piruben
This project is licensed under the MIT License - see the LICENSE.md file for details