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https://github.com/lolab-msm/pydyno
Tool that uses tropical algebra concepts to 'decompose' species trajectories in the protein-protein interactions that drive changes of concentration in time
https://github.com/lolab-msm/pydyno
clustering dynamic-analysis pysb systems-biology
Last synced: about 1 month ago
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Tool that uses tropical algebra concepts to 'decompose' species trajectories in the protein-protein interactions that drive changes of concentration in time
- Host: GitHub
- URL: https://github.com/lolab-msm/pydyno
- Owner: LoLab-MSM
- License: bsd-2-clause
- Created: 2015-08-21T00:59:17.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2024-04-10T11:59:28.000Z (9 months ago)
- Last Synced: 2024-04-29T15:44:47.814Z (8 months ago)
- Topics: clustering, dynamic-analysis, pysb, systems-biology
- Language: Jupyter Notebook
- Size: 30.5 MB
- Stars: 8
- Watchers: 16
- Forks: 1
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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[](https://travis-ci.org/LoLab-VU/pydyno)
[](https://coveralls.io/github/LoLab-VU/tropical?branch=master)
# 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.
## Publications
PrePrint: Probability-based mechanisms in biological networks with parameter uncertainty
Oscar O. Ortega, Blake A. Wilson, James C. Pino, Michael W. Irvin, Geena V. Ildefonso, Shawn P. Garbett, Carlos F. Lopez
bioRxiv 2021.01.26.428266; doi: https://doi.org/10.1101/2021.01.26.428266
The preprint paper can be found [here](https://www.biorxiv.org/content/10.1101/2021.01.26.428266v1.full)
Jupyter notebooks with the code to reproduce the paper figures can be found [here](https://github.com/LoLab-VU/pydyno/tree/master/pydyno/examples/paper1)
## 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
```
# 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
```
# 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
```
```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
[Graphviz](https://graphviz.org/download/) is necessary to obtain these visualizations
```python
visualization_path(model,
path=paths[0],
target_node='s5',
type_analysis='production',
filename='path_0.png')
# Visualization is saved to local directory with the filename path0.png
```
```python
visualization_path(model,
path=paths[1],
target_node='s5',
type_analysis='production',
filename='path_1.png')
# Visualization is saved to local directory with the filename path1.png
```
```python
visualization_path(model,
path=paths[2],
target_node='s5',
type_analysis='production',
filename='path_2.png')
# Visualization is saved to local directory with the filename path2.png
```
