Metadata-Version: 2.1
Name: pydyno
Version: 0.1.2
Summary: Dynamic analysis of systems biology models
Home-page: UNKNOWN
Author: Oscar Ortega
Author-email: oscar.ortega@vanderbilt.edu
License: UNKNOWN
Description: [![Codacy Badge](https://api.codacy.com/project/badge/Grade/4dc49b4309bc4f05911eee43f932591b)](https://app.codacy.com/app/ortega2247/tropical?utm_source=github.com&utm_medium=referral&utm_content=LoLab-VU/tropical&utm_campaign=Badge_Grade_Dashboard)
        [![Build Status](https://travis-ci.org/LoLab-VU/pydyno.svg?branch=master)](https://travis-ci.org/LoLab-VU/pydyno)
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        # PyDyNo
        
        Python Dynamic analysis of Biochemical Networks (PyDyNo) is an open source python library for the analysis of 
        signal execution in network-driven biological processes. PyDyNo supports the analysis of [PySB](http://pysb.org/)
        and [SBML](http://sbml.org/Main_Page) models.
        
        ## Installation
        
        ### From PyPI
        
        ```bash
        > pip install pydyno
        ```
        
        ### Installing the latest unreleased version
        
        ```bash
        > pip install git+git:https://github.com/LoLab-VU/pydyno.git
        ```
        
        ### Installing from source folder
        
        - Download and extract pydyno
        - Navigate into the pydyno directory
        - Install (Python is necessary for this step):
        
        ```bash
        > python setup.py install
        ```
        
        ## How to use PyDyNo
        
        # Import libraries
        
        
        ```python
        import pydyno
        import numpy as np
        from os.path import dirname, join
        from IPython.display import Image
        from pydyno.examples.double_enzymatic.mm_two_paths_model import model
        from pydyno.visualize_simulations import VisualizeSimulations
        from pydyno.discretization import PysbDomPath
        from pydyno.visualize_discretization import visualization_path
        from pysb.simulator import ScipyOdeSimulator
        %matplotlib inline
        ```
        
        # Load the calibrated parameters and simulate the model with 100 different parameter sets
        
        
        ```python
        # import calibrated parameters
        module_path = dirname(pydyno.__file__)
        pars_path = join(module_path, "examples", "double_enzymatic", "calibrated_pars.npy")
        pars = np.load(pars_path)
        ```
        
        
        ```python
        # define time for the simulation and simulate model
        tspan = np.linspace(0, 100, 101)
        sim = ScipyOdeSimulator(model, tspan=tspan).run(param_values=pars[:100])
        ```
        
        # Visualize the dynamics of the model
        
        ```python
        vt = VisualizeSimulations(model, sim, clusters=None)
        vt.plot_cluster_dynamics(components=[5])
        # This saves the figure in the local folder with the filename comp0_cluster0.png
        ```
        ![png](pydyno/examples/double_enzymatic/double_enzymatic_reaction_files/double_enzymatic_reaction_6_1.png)
        
        # Obtain the dominant paths for each of the simulations¶
        
        
        
        ```python
        dp = PysbDomPath(model, sim)
        signatures, paths = dp.get_path_signatures('s5', 'production',                                         depth=2, dom_om=1)
        signatures.sequences.head()
        ```
        
        # Obtain distance matrix and optimal number of clusters (execution modes)
        
        ```python
        signatures.dissimilarity_matrix()
        signatures.silhouette_score_agglomerative_range(4)
        ```
        
        ```python
        # Select the number of cluster with highest silhouette score
        signatures.agglomerative_clustering(2)
        ```
        
        
        ```python
        # Plot signatures
        signatures.plot_sequences()
        # File is saved to the local directory with the filename modal.png
        ```
        
        ![png](pydyno/examples/double_enzymatic/double_enzymatic_reaction_files/double_enzymatic_reaction_13_0.png)
        
        ```python
        paths
        ```
            {2: [OrderedDict([('s5', [['s3'], ['s4']])]),
              OrderedDict([('s3', [['s0', 's1']]), ('s4', [['s0', 's2']])])],
             1: [OrderedDict([('s5', [['s4']])]), OrderedDict([('s4', [['s0', 's2']])])],
             0: [OrderedDict([('s5', [['s3']])]), OrderedDict([('s3', [['s0', 's1']])])]}
        
        # Visualize execution modes
        
        ```python
        visualization_path(model, 
                           path=paths[0], 
                           target_node='s5', 
                           type_analysis='production', 
                           filename='path_0.png')
        # Visualization is saved to local directory wit the filename path0.png
        ```
        
        ![png](pydyno/examples/double_enzymatic/double_enzymatic_reaction_files/path_0.png)
        
        ```python
        visualization_path(model, 
                           path=paths[1], 
                           target_node='s5', 
                           type_analysis='production', 
                           filename='path_1.png')
        # Visualization is saved to local directory wit the filename path1.png
        ```
        
        ![png](pydyno/examples/double_enzymatic/double_enzymatic_reaction_files/path_1.png)
        
        ```python
        visualization_path(model, 
                           path=paths[2], 
                           target_node='s5', 
                           type_analysis='production', 
                           filename='path_2.png')
        # Visualization is saved to local directory wit the filename path2.png
        ```
        
        ![png](pydyno/examples/double_enzymatic/double_enzymatic_reaction_files/path_2.png)
        
Keywords: systems,biology,model
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Mathematics
Description-Content-Type: text/markdown
