{"id":19197265,"url":"https://github.com/lolab-msm/pydyno","last_synced_at":"2025-10-24T13:03:49.403Z","repository":{"id":36819215,"uuid":"41126095","full_name":"LoLab-MSM/pydyno","owner":"LoLab-MSM","description":"Tool that uses tropical algebra concepts to 'decompose' species trajectories in the protein-protein interactions that drive changes of concentration in time","archived":false,"fork":false,"pushed_at":"2024-09-26T17:59:46.000Z","size":32025,"stargazers_count":8,"open_issues_count":11,"forks_count":1,"subscribers_count":16,"default_branch":"master","last_synced_at":"2025-05-09T00:52:32.092Z","etag":null,"topics":["clustering","dynamic-analysis","pysb","systems-biology"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-2-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/LoLab-MSM.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2015-08-21T00:59:17.000Z","updated_at":"2025-01-25T00:08:07.000Z","dependencies_parsed_at":"2024-01-09T02:55:42.021Z","dependency_job_id":"c38b8180-f98b-452d-b868-f2a70ed12825","html_url":"https://github.com/LoLab-MSM/pydyno","commit_stats":null,"previous_names":["lolab-vu/pydyno"],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LoLab-MSM%2Fpydyno","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LoLab-MSM%2Fpydyno/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LoLab-MSM%2Fpydyno/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LoLab-MSM%2Fpydyno/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LoLab-MSM","download_url":"https://codeload.github.com/LoLab-MSM/pydyno/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253171235,"owners_count":21865289,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["clustering","dynamic-analysis","pysb","systems-biology"],"created_at":"2024-11-09T12:16:11.237Z","updated_at":"2025-10-24T13:03:49.394Z","avatar_url":"https://github.com/LoLab-MSM.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Codacy Badge](https://api.codacy.com/project/badge/Grade/4dc49b4309bc4f05911eee43f932591b)](https://app.codacy.com/app/ortega2247/tropical?utm_source=github.com\u0026utm_medium=referral\u0026utm_content=LoLab-VU/tropical\u0026utm_campaign=Badge_Grade_Dashboard)\n[![Build Status](https://travis-ci.org/LoLab-VU/pydyno.svg?branch=master)](https://travis-ci.org/LoLab-VU/pydyno)\n[![Coverage Status](https://coveralls.io/repos/github/LoLab-VU/tropical/badge.svg?branch=master)](https://coveralls.io/github/LoLab-VU/tropical?branch=master)\n\n# PyDyNo\n\nPython Dynamic analysis of biochemical NetwOrks (PyDyNo) is an open source python library for the analysis of \nsignal execution in network-driven biological processes. PyDyNo supports the analysis of [PySB](http://pysb.org/)\nand [SBML](http://sbml.org/Main_Page) models.\n\n## Publications\n\nSignal execution modes emerge in biochemical reaction networks calibrated to experimental data  \nOscar O. Ortega*, Mustafa Ozen*, Blake A. Wilson, James C. Pino, Michael W. Irvin, Geena V. Ildefonso, Shawn P. Garbett, Carlos F. Lopez\n\niScience 2024; doi: 10.1016/j.isci.2024.109989\n\nThe paper can be accessed [here](https://doi.org/10.1016/j.isci.2024.109989)\n\nJupyter notebooks with the code to reproduce the paper figures can be found [here](https://github.com/LoLab-VU/pydyno/tree/master/pydyno/examples/paper1)\n\n\n## Installation\n\n### From PyPI\n\n```bash\n\u003e pip install pydyno\n```\n\n### Installing the latest unreleased version\n\n```bash\n\u003e pip install git+git:https://github.com/LoLab-VU/pydyno.git\n```\n\n### Installing from source folder\n\n- Download and extract pydyno\n- Navigate into the pydyno directory\n- Install (Python is necessary for this step):\n\n```bash\n\u003e python setup.py install\n```\n\n## How to use PyDyNo\n\n# Import libraries\n\n\n```python\nimport pydyno\nimport numpy as np\nfrom os.path import dirname, join\nfrom IPython.display import Image\nfrom pydyno.examples.double_enzymatic.mm_two_paths_model import model\nfrom pydyno.visualize_simulations import VisualizeSimulations\nfrom pydyno.discretization import PysbDomPath\nfrom pydyno.visualize_discretization import visualization_path\nfrom pysb.simulator import ScipyOdeSimulator\n```\n\n# Load the calibrated parameters and simulate the model with 100 different parameter sets\n\n\n```python\n# import calibrated parameters\nmodule_path = dirname(pydyno.__file__)\npars_path = join(module_path, \"examples\", \"double_enzymatic\", \"calibrated_pars.npy\")\npars = np.load(pars_path)\n```\n\n\n```python\n# define time for the simulation and simulate model\ntspan = np.linspace(0, 100, 101)\nsim = ScipyOdeSimulator(model, tspan=tspan).run(param_values=pars[:100])\n```\n\n# Visualize the dynamics of the model\n\n```python\nvt = VisualizeSimulations(model, sim, clusters=None)\nvt.plot_cluster_dynamics(components=[5])\n# This saves the figure in the local folder with the filename comp0_cluster0.png\n```\n![png](pydyno/examples/double_enzymatic/double_enzymatic_reaction_files/double_enzymatic_reaction_6_1.png)\n\n# Obtain the dominant paths for each of the simulations¶\n\n\n\n```python\ndp = PysbDomPath(model, sim)\nsignatures, paths = dp.get_path_signatures('s5', 'production',                                         depth=2, dom_om=1)\nsignatures.sequences.head()\n```\n\n# Obtain distance matrix and optimal number of clusters (execution modes)\n\n```python\nsignatures.dissimilarity_matrix()\nsignatures.silhouette_score_agglomerative_range(4)\n```\n\n```python\n# Select the number of cluster with highest silhouette score\nsignatures.agglomerative_clustering(2)\n```\n\n\n```python\n# Plot signatures\nsignatures.plot_sequences()\n# File is saved to the local directory with the filename modal.png\n```\n\n![png](pydyno/examples/double_enzymatic/double_enzymatic_reaction_files/double_enzymatic_reaction_13_0.png)\n\n```python\npaths\n```\n    {2: [OrderedDict([('s5', [['s3'], ['s4']])]),\n      OrderedDict([('s3', [['s0', 's1']]), ('s4', [['s0', 's2']])])],\n     1: [OrderedDict([('s5', [['s4']])]), OrderedDict([('s4', [['s0', 's2']])])],\n     0: [OrderedDict([('s5', [['s3']])]), OrderedDict([('s3', [['s0', 's1']])])]}\n\n# Visualize execution modes\n[Graphviz](https://graphviz.org/download/) is necessary to obtain these visualizations\n```python\nvisualization_path(model, \n                   path=paths[0], \n                   target_node='s5', \n                   type_analysis='production', \n                   filename='path_0.png')\n# Visualization is saved to local directory with the filename path0.png\n```\n\n![png](pydyno/examples/double_enzymatic/double_enzymatic_reaction_files/path_0.png)\n\n```python\nvisualization_path(model, \n                   path=paths[1], \n                   target_node='s5', \n                   type_analysis='production', \n                   filename='path_1.png')\n# Visualization is saved to local directory with the filename path1.png\n```\n\n![png](pydyno/examples/double_enzymatic/double_enzymatic_reaction_files/path_1.png)\n\n```python\nvisualization_path(model, \n                   path=paths[2], \n                   target_node='s5', \n                   type_analysis='production', \n                   filename='path_2.png')\n# Visualization is saved to local directory with the filename path2.png\n```\n\n![png](pydyno/examples/double_enzymatic/double_enzymatic_reaction_files/path_2.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flolab-msm%2Fpydyno","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flolab-msm%2Fpydyno","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flolab-msm%2Fpydyno/lists"}