Metadata-Version: 2.1
Name: pasmopy
Version: 0.0.5
Summary: Patient-Specific Modeling in Python
Home-page: https://github.com/pasmopy/pasmopy
Author: Hiroaki Imoto
Author-email: himoto@protein.osaka-u.ac.jp
Maintainer: Hiroaki Imoto
Maintainer-email: himoto@protein.osaka-u.ac.jp
License: Apache 2.0
Download-URL: https://github.com/pasmopy/pasmopy/releases
Project-URL: Documentation, https://pasmopy.readthedocs.io/en/latest/
Project-URL: Source Code, https://github.com/pasmopy/pasmopy
Project-URL: Bug Tracker, https://github.com/pasmopy/pasmopy/issues
Description: # Pasmopy – Patient-Specific Modeling in Python
        
        ![overview](https://raw.githubusercontent.com/pasmopy/pasmopy/master/docs/_static/img/overview.png)
        
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        [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
        
        **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](https://github.com/biomass-dev/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](https://pypi.python.org/pypi/pasmopy):
        
        ```bash
        $ 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]
           ```
        
        1. Convert text into an executable model
        
           ```python
           from pasmopy import Text2Model
        
           description = Text2Model("michaelis_menten.txt")
           description.convert()
           ```
        
        1. Run simulation
        
           ```python
           from pasmopy import Model, run_simulation
           import michaelis_menten
        
           model = Model(michaelis_menten.__package__).create()
           run_simulation(model)
           ```
        
           ![michaelis_menten](https://raw.githubusercontent.com/pasmopy/pasmopy/master/docs/_static/img/michaelis_menten_sim.png)
        
        For more examples, please refer to the [Documentation](https://pasmopy.readthedocs.io/en/latest/).
        
        ### 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.
        
        ## Author
        
        [Hiroaki Imoto](https://github.com/himoto)
        
        ## License
        
        [Apache License 2.0](https://github.com/pasmopy/pasmopy/blob/master/LICENSE)
        
Keywords: systems,biology,cancer,classification,patient,modeling,simulation
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Healthcare Industry
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: docs
