Patient-Specific Modeling in Python
Project description
Pasmopy – Patient-Specific Modeling in Python
Pasmopy is a scalable toolkit to identify prognostic factors for cancers based on intracellular signaling dynamics generated from personalized kinetic models. It is compatible with biomass and offers the following features:
- Construction of mechanistic models from text
- Personalization of the model using transcriptome data
- Prediction of patient outcome through classification based on signaling dynamics
- Sensitivity analysis for prediction of potential drug targets
Installation
The latest stable release (and required dependencies) can be installed from PyPI:
$ pip install pasmopy
Pasmopy requires Python 3.7+ to run.
Example
Building mathematical models of biochemical systems from text
This example shows you how to build a simple Michaelis-Menten two-step enzyme catalysis model with Pasmopy.
E + S ⇄ ES → E + P
An enzyme, E, binding to a substrate, S, to form a complex, ES, which in turn releases a product, P, regenerating the original enzyme.
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Prepare a text file describing biochemical reactions (
michaelis_menten.txt
)E binds S --> ES | kf=0.003, kr=0.001 | E=100, S=50 ES dissociates to E and P | kf=0.002, kr=0 @obs Substrate: u[S] @obs E_free: u[E] @obs E_total: u[E] + u[ES] @obs Product: u[P] @obs Complex: u[ES] @sim tspan: [0, 100]
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Convert text into an executable model
from pasmopy import Text2Model description = Text2Model("michaelis_menten.txt") description.convert()
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Run simulation
from pasmopy import Model, run_simulation import michaelis_menten model = Model(michaelis_menten.__package__).create() run_simulation(model)
For more examples, please refer to the Documentation.
Patient-specific simulations and model-based stratification
Using Pasmopy, we built a mechanistic model of ErbB receptor signaling network, trained in cultured cell lines, and performed in silico simulation of 377 breast cancer patients. All code for model construction, patient-specific simulations, and model-based stratification can be found here: https://github.com/pasmopy/breast_cancer.
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