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Patient-Specific Modeling in Python

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Pasmopy – Patient-Specific Modeling in Python


PyPI version Actions Status Documentation Status License Downloads PyPI pyversions Language grade: Python status Code style: black Imports: isort iScience Paper

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 based on in silico signaling dynamics
  • Sensitivity analysis for prediction of potential drug targets


The latest stable release (and required dependencies) can be installed from PyPI:

$ pip install pasmopy

Pasmopy requires Python 3.7+ to run.


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 the biochemical reactions (e.g., 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 the text into an executable model

    $ python
    >>> from pasmopy import Text2Model
    >>> description = Text2Model("michaelis_menten.txt")
    >>> description.convert()
  3. 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.

Personalized signaling models for cancer patient stratification

Using Pasmopy, we built a mechanistic model of ErbB receptor signaling network, trained with protein quantification data obtained from cultured cell lines, and performed in silico simulation of the pathway activities on 377 breast cancer patients using The Cancer Genome Atlas (TCGA) transcriptome datasets. All code for model construction, patient-specific simulations, and model-based stratification can be found here:



Hiroaki Imoto


Apache License 2.0

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