pasmopy 0.0.4

Patient-Specific Modeling in Python

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.

1. 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]
   

2. Convert text into an executable model

   from pasmopy import Text2Model
   
   description = Text2Model("michaelis_menten.txt")
   description.convert()
   

3. Run simulation with biomass

   from biomass import Model, run_simulation
   import michaelis_menten
   
   model = Model(michaelis_menten.__package__).create()
   run_simulation(model)
   

   

For more examples, please refer to the Documentation.

Author

Hiroaki Imoto

License

Apache License 2.0