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: 8 months 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 (almost 11 years ago)
- Default Branch: master
- Last Pushed: 2024-09-26T17:59:46.000Z (over 1 year ago)
- Last Synced: 2025-05-09T00:52:32.092Z (about 1 year 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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# 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
Signal execution modes emerge in biochemical reaction networks calibrated to experimental data
Oscar O. Ortega*, Mustafa Ozen*, Blake A. Wilson, James C. Pino, Michael W. Irvin, Geena V. Ildefonso, Shawn P. Garbett, Carlos F. Lopez
iScience 2024; doi: 10.1016/j.isci.2024.109989
The paper can be accessed [here](https://doi.org/10.1016/j.isci.2024.109989)
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
```
