Kinetic Diagram Analysis

Python package used for the analysis of biochemical kinetic diagrams using the diagrammatic approach developed by T.L. Hill.

WARNING: this software is in flux and is not API stable.

Examples

KDA has a host of capabilities, all beginning with defining the connections and reaction rates (if desired) for your system. This is done by constructing an NxNarray with diagonal values set to zero, and off-diagonal values (i, j) representing connections (and reaction rates) between states i and j. If desired, these can be the edge weights (denoted kij), but they can be specified later.

The following is an example for a simple 3-state model with all nodes connected:

import numpy as np
import networkx as nx
from kda import graph_utils, calculations

# define matrix with reaction rates set to 1
K = np.array(
    [
        [0, 1, 1],
        [1, 0, 1],
        [1, 1, 0],
    ]
)
# initialize an empty graph object
G = nx.MultiDiGraph()
# populate the edge data
graph_utils.generate_edges(G, K, val_key="val", name_key="name")
# calculate the state probabilities
state_probabilities = calculations.calc_state_probs(G, key="val")
# get the state probabilities in expression form
state_probability_str = calculations.calc_state_probs(G, key="name", output_strings=True)
# print results
print("State probabilities: \n", state_probabilities)
print("State 1 probability expression: \n", state_probability_str[0])

The output from the above example:

$ python example_script.py
State probabilities:
 [0.33333333 0.33333333 0.33333333]
State 1 probability expression:
 (k21*k31 + k21*k32 + k23*k31)/(k12*k23 + k12*k31 + k12*k32 + k13*k21 + k13*k23 + k13*k32 + k21*k31 + k21*k32 + k23*k31)

As expected, the state probabilities are equal because all edge weights are set to a value of 1.

Continuing with the previous example, the KDA diagrams and plotting modules can be leveraged to display the diagrams that lead to the above probability expression:

import os
from kda import diagrams, plotting

# generate the set of directional diagrams for G
directional_diagrams = diagrams.generate_directional_diagrams(G)
# get the current working directory
cwd = os.getcwd()
# specify the positions of all nodes in NetworkX fashion
node_positions = {0: [0, 1], 1: [-0.5, 0], 2: [0.5, 0]}
# plot and save the input diagram
plotting.draw_diagrams(G, pos=node_positions, path=cwd, label="input")
# plot and save the directional diagrams as a panel
plotting.draw_diagrams(
    directional_diagrams,
    pos=node_positions,
    path=cwd,
    cbt=True,
    label="directional_panel",
)

This will generate two files, input.png and directional_panel.png, in your current working directory:

input.png

directional.png

NOTE: For more examples (like the following) visit the KDA examples repository:

Installation

Development version from source

To install the latest development version from source, run

git clone git@github.com:Becksteinlab/kda.git
cd kda
python setup.py install

Copyright

Copyright (c) 2020, Nikolaus Awtrey

Acknowledgements

Project based on the Computational Molecular Science Python Cookiecutter version 1.2.