Merge pull request #35 from LoLab-VU/update_readme

Update readme

Author: ortega2247

MANIFEST.in

@@ -1,4 +1,5 @@
 recursive-include pydyno/lcs *.c *.h *.pxd *.pyx
 include pydyno/examples/sbml_example/double_enzymatic_sbml.xml
+include pydyno/examples/double_enzymatic/calibrated_pars.npy
 include versioneer.py
 include pydyno/_version.py

README.md

@@ -4,15 +4,148 @@
 
 # PyDyNo
 
-Python Dynamic analysis of Biochemical Networks
-
-The advent of quantitative techniques to probe biomolecular-signaling processes have led to increased use of 
-mathematical models to extract mechanistic insight from complex datasets. These complex mathematical models 
-can yield useful insights about intracellular signal execution but the task to identify key molecular drivers 
-in signal execution, within a complex network, remains a central challenge in quantitative biology. This challenge 
-is compounded by the fact that cell-to-cell variability within a cell population could yield multiple signal 
-execution modes and thus multiple potential drivers in signal execution. Here we present a novel approach to 
-identify signaling drivers and characterize dynamic signal processes within a network. Our method, PyDyNo, 
-combines physical chemistry, statistical clustering, and tropical algebra formalisms to identify interactions 
-that drive time-dependent behavior in signaling pathways. 
+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)