.. image:: docs/source/_static/images/logo/logo_nskinetics_light_white-circle.png
:width: 250
=============================================================
The Non-Steady state Kinetics simulation package (NSKinetics)
=============================================================
.. image:: http://img.shields.io/pypi/v/nskinetics.svg?style=flat
:target: https://pypi.python.org/pypi/nskinetics
:alt: Version_status
.. image:: http://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat
:target: https://nskinetics.readthedocs.io/en/latest/
:alt: Documentation
.. image:: http://img.shields.io/badge/license-MIT-blue.svg?style=flat
:target: https://github.com/sarangbhagwat/nskinetics/blob/main/LICENSE
:alt: license
.. image:: https://img.shields.io/pypi/pyversions/nskinetics.svg
:target: https://pypi.python.org/pypi/nskinetics
:alt: Supported_versions
.. image:: https://coveralls.io/repos/github/sarangbhagwat/nskinetics/badge.svg?cachebuster=202507072
:target: https://coveralls.io/github/sarangbhagwat/nskinetics?branch=main
**Contents**
.. contents::
:local:
What is NSKinetics?
-------------------
NSKinetics is a fast, flexible, and convenient package to simulate non-steady state reaction kinetics, especially for systems involving enzymatic conversions. Models for multiple inhibitory phenomena (competitive, non-competitive, uncompetitive, and "mechanism-based") are also included. NSKinetics enables the construction, simulation, and analysis of reaction systems governed by mass action kinetics or other user-defined rate laws. It supports features such as species concentration spikes, event triggers, inverse modeling (parameter fitting to experimental data), parameter identifiability analysis, and optimal design of experiments.
Installation
------------
Get the latest version of NSKinetics from `PyPI <https://pypi.org/project/nskinetics/>`__. If you have an installation of Python with pip, simply install it with:
.. code-block:: bash
$ pip install nskinetics
To get the git version, run:
.. code-block:: bash
$ git clone git://github.com/sarangbhagwat/nskinetics
For help on common installation issues, please visit the `documentation <https://nskinetics.readthedocs.io/en/latest/>`__.
Documentation
-------------
NSKinetic's `full documentation <https://nskinetics.readthedocs.io/en/latest/>`__ is currently being developed. In the meantime, here are some examples to get started:
**Example 1: Simple enzyme-substrate system**
.. code-block:: python
import nskinetics as nsk
# Create a SpeciesSystem object
sp_sys = nsk.SpeciesSystem('sp_sys',
['E', 'S', 'ES', 'P'], # enzyme, substrate, enzyme-substrate complex, product
concentrations=[1e-4, 1e-4, 0, 0])
# Describe reactions by writing chemical equations and kinetic parameter info
reactions = [
'E + S <-> ES; kf = 12, kb = 10.0', # kf = kon, kb = koff
'ES -> E + P; kf = 32.0' # kf = kcat (enzyme turnover number)
]
# Generate a ReactionSystem from strings
rxn_sys = nsk.ReactionSystem(ID='ESP_rxn_sys',
reactions=reactions,
species_system=sp_sys)
# Simulate the ReactionSystem
rxn_sys.solve(t_span=[0, 2*24*3600], # I want to simulate the system over 2 days
sp_conc_for_events={'S':1e-6}, # In addition to a full simulation,
) # I want to know the time at which [S] drops to 1e-6
# Plot results
rxn_sys.plot_solution()
.. image:: docs/source/_static/images/example_1_plot_i.png
:width: 400
Since [ES] was too small to view in the overall plot, let's also plot it separately:
.. code-block:: python
rxn_sys.plot_solution(sps_to_include=['ES'])
.. image:: docs/source/_static/images/example_1_plot_ii.png
:width: 400
**Example 2: Simple enzyme-substrate system + competitive inhibition + "mechanism-based" inhibition**
.. code-block:: python
import nskinetics as nsk
# Create a SpeciesSystem object
sp_sys = nsk.SpeciesSystem('sp_sys',
['E', 'S', 'ES', 'P',
'I_CI', 'EI_CI', 'Q',
'I_MBI', 'EI_MBI_unstable', 'EI_MBI_stable'], # mechanism-based_inhibitor, unstable enzyme-MBI complex, stable enzyme-MBI complex
concentrations=[1e-4, 1e-4, 0, 0,
5e-5, 0, 0,
0, 0, 0])
# Describe reactions by writing chemical equations and kinetic parameter info
reactions = [
'E + S <-> ES; kf = 12, kb = 10.0',
'ES -> E + P; kf = 32.0',
'E + I_CI <-> EI_CI; kf=12, kb=10.0',
'EI_CI -> E + Q; kf=32',
'E + I_MBI <-> EI_MBI_unstable; kf=12.0, kb=10',
'EI_MBI_unstable -> EI_MBI_stable; kf = 32'
]
# Generate a ReactionSystem from strings
rxn_sys = nsk.ReactionSystem(ID='rxn_sys',
reactions=reactions,
species_system=sp_sys)
# Simulate the ReactionSystem
rxn_sys.solve(t_span=[0, 2*24*3600],
sp_conc_for_events={'S':1e-6})
# Plot results
rxn_sys.plot_solution()
.. image:: docs/source/_static/images/example_2_plot_i.png
:width: 400
**Example 3: Simple enzyme-substrate system in a fed-batch regime**
.. code-block:: python
import nskinetics as nsk
# Create a SpeciesSystem object
sp_sys = nsk.SpeciesSystem('sp_sys',
['E', 'S', 'ES', 'P',],
concentrations=[1e-4, 1e-4, 0, 0,])
# Describe reactions by writing chemical equations and kinetic parameter info
reactions = [
'E + S <-> ES; kf = 12, kb = 10.0',
'ES -> E + P; kf = 32.0',
]
# Generate a ReactionSystem from strings
rxn_sys = nsk.ReactionSystem(ID='rxn_sys',
reactions=reactions,
species_system=sp_sys)
# Describe forced concentration spikes for any species
# (e.g., from feeding substrate in a fed-batch regime)
spikes = {20000: 'Target; S; 1e-4', # at t=40000, add enough S to achieve [S]=1e-4
50000: 'Target; S; 1e-4', # at t=50000, add enough S to to achieve [S]=1e-4
80000: 'Target; S; 1e-4', # at t=80000, add enough S to achieve [S]=1e-4
100000: 'Change; S; 2e-4',# at t=100000, add enough S to increase [S] by 2e-4
}
# Simulate the ReactionSystem
rxn_sys.solve(t_span=[0, 2*24*3600],
sp_conc_for_events={'S':1e-6},
spikes=spikes)
# Plot results
rxn_sys.plot_solution()
.. image:: docs/source/_static/images/example_3_plot_i.png
:width: 400
Bug reports
-----------
To report bugs, please use NSKinetics's Bug Tracker at:
https://github.com/sarangbhagwat/nskinetics
Contributing
------------
For guidelines on how to contribute, visit:
[link to be added]
License information
-------------------
See ``LICENSE.txt`` for information on the terms & conditions for usage
of this software, and a DISCLAIMER OF ALL WARRANTIES.
Although not required by the NSKinetics license, if it is convenient for you,
please cite NSKinetics if used in your work. Please also consider contributing
any changes you make back, and benefit the community.
About the authors
-----------------
NSKinetics was created and developed by `Sarang S. Bhagwat <https://github.com/sarangbhagwat>`__ as part of the `Scown Group <https://cscown.com/>`__ and the `Energy & Biosciences Institute <https://energybiosciencesinstitute.org/>`__ at the `University of California, Berkeley (UC Berkeley) <https://www.berkeley.edu/>`__.
References
----------
.. [1] ` To be added <link to be added>`__.
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"description": ".. image:: docs/source/_static/images/logo/logo_nskinetics_light_white-circle.png\r\n :width: 250\r\n\r\n=============================================================\r\nThe Non-Steady state Kinetics simulation package (NSKinetics)\r\n=============================================================\r\n\r\n.. image:: http://img.shields.io/pypi/v/nskinetics.svg?style=flat\r\n :target: https://pypi.python.org/pypi/nskinetics\r\n :alt: Version_status\r\n.. image:: http://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat\r\n :target: https://nskinetics.readthedocs.io/en/latest/\r\n :alt: Documentation\r\n.. image:: http://img.shields.io/badge/license-MIT-blue.svg?style=flat\r\n :target: https://github.com/sarangbhagwat/nskinetics/blob/main/LICENSE\r\n :alt: license\r\n.. image:: https://img.shields.io/pypi/pyversions/nskinetics.svg\r\n :target: https://pypi.python.org/pypi/nskinetics\r\n :alt: Supported_versions\r\n.. image:: https://coveralls.io/repos/github/sarangbhagwat/nskinetics/badge.svg?cachebuster=202507072\r\n :target: https://coveralls.io/github/sarangbhagwat/nskinetics?branch=main\r\n\r\n**Contents**\r\n\r\n.. contents::\r\n :local:\r\n\r\nWhat is NSKinetics?\r\n-------------------\r\n\r\nNSKinetics is a fast, flexible, and convenient package to simulate non-steady state reaction kinetics, especially for systems involving enzymatic conversions. Models for multiple inhibitory phenomena (competitive, non-competitive, uncompetitive, and \"mechanism-based\") are also included. NSKinetics enables the construction, simulation, and analysis of reaction systems governed by mass action kinetics or other user-defined rate laws. It supports features such as species concentration spikes, event triggers, inverse modeling (parameter fitting to experimental data), parameter identifiability analysis, and optimal design of experiments.\r\n\r\nInstallation\r\n------------\r\n\r\nGet the latest version of NSKinetics from `PyPI <https://pypi.org/project/nskinetics/>`__. If you have an installation of Python with pip, simply install it with:\r\n\r\n.. code-block:: bash\r\n\r\n $ pip install nskinetics\r\n\r\nTo get the git version, run:\r\n\r\n.. code-block:: bash\r\n\r\n $ git clone git://github.com/sarangbhagwat/nskinetics\r\n\r\nFor help on common installation issues, please visit the `documentation <https://nskinetics.readthedocs.io/en/latest/>`__.\r\n\r\nDocumentation\r\n-------------\r\n\r\nNSKinetic's `full documentation <https://nskinetics.readthedocs.io/en/latest/>`__ is currently being developed. In the meantime, here are some examples to get started:\r\n\r\n**Example 1: Simple enzyme-substrate system**\r\n\r\n.. code-block:: python\r\n\r\n import nskinetics as nsk\r\n\r\n # Create a SpeciesSystem object\r\n sp_sys = nsk.SpeciesSystem('sp_sys', \r\n ['E', 'S', 'ES', 'P'], # enzyme, substrate, enzyme-substrate complex, product\r\n concentrations=[1e-4, 1e-4, 0, 0])\r\n\r\n # Describe reactions by writing chemical equations and kinetic parameter info\r\n reactions = [\r\n 'E + S <-> ES; kf = 12, kb = 10.0', # kf = kon, kb = koff\r\n 'ES -> E + P; kf = 32.0' # kf = kcat (enzyme turnover number)\r\n ]\r\n\r\n # Generate a ReactionSystem from strings\r\n rxn_sys = nsk.ReactionSystem(ID='ESP_rxn_sys', \r\n reactions=reactions,\r\n species_system=sp_sys)\r\n\r\n # Simulate the ReactionSystem\r\n rxn_sys.solve(t_span=[0, 2*24*3600], # I want to simulate the system over 2 days\r\n sp_conc_for_events={'S':1e-6}, # In addition to a full simulation,\r\n ) # I want to know the time at which [S] drops to 1e-6\r\n\r\n # Plot results\r\n rxn_sys.plot_solution() \r\n\r\n\r\n.. image:: docs/source/_static/images/example_1_plot_i.png\r\n :width: 400\r\n\r\nSince [ES] was too small to view in the overall plot, let's also plot it separately:\r\n\r\n.. code-block:: python\r\n\r\n rxn_sys.plot_solution(sps_to_include=['ES'])\r\n\r\n\r\n.. image:: docs/source/_static/images/example_1_plot_ii.png\r\n :width: 400\r\n\r\n\r\n**Example 2: Simple enzyme-substrate system + competitive inhibition + \"mechanism-based\" inhibition**\r\n\r\n.. code-block:: python\r\n\r\n import nskinetics as nsk\r\n \r\n # Create a SpeciesSystem object\r\n sp_sys = nsk.SpeciesSystem('sp_sys', \r\n ['E', 'S', 'ES', 'P',\r\n 'I_CI', 'EI_CI', 'Q',\r\n 'I_MBI', 'EI_MBI_unstable', 'EI_MBI_stable'], # mechanism-based_inhibitor, unstable enzyme-MBI complex, stable enzyme-MBI complex \r\n concentrations=[1e-4, 1e-4, 0, 0,\r\n 5e-5, 0, 0,\r\n 0, 0, 0])\r\n \r\n # Describe reactions by writing chemical equations and kinetic parameter info\r\n reactions = [\r\n 'E + S <-> ES; kf = 12, kb = 10.0',\r\n 'ES -> E + P; kf = 32.0',\r\n 'E + I_CI <-> EI_CI; kf=12, kb=10.0',\r\n 'EI_CI -> E + Q; kf=32',\r\n 'E + I_MBI <-> EI_MBI_unstable; kf=12.0, kb=10',\r\n 'EI_MBI_unstable -> EI_MBI_stable; kf = 32'\r\n ]\r\n \r\n # Generate a ReactionSystem from strings\r\n rxn_sys = nsk.ReactionSystem(ID='rxn_sys', \r\n reactions=reactions,\r\n species_system=sp_sys)\r\n \r\n # Simulate the ReactionSystem\r\n rxn_sys.solve(t_span=[0, 2*24*3600],\r\n sp_conc_for_events={'S':1e-6})\r\n \r\n # Plot results\r\n rxn_sys.plot_solution()\r\n\r\n\r\n.. image:: docs/source/_static/images/example_2_plot_i.png\r\n :width: 400\r\n\r\n\r\n**Example 3: Simple enzyme-substrate system in a fed-batch regime**\r\n\r\n.. code-block:: python\r\n\r\n import nskinetics as nsk\r\n \r\n # Create a SpeciesSystem object\r\n sp_sys = nsk.SpeciesSystem('sp_sys', \r\n ['E', 'S', 'ES', 'P',],\r\n concentrations=[1e-4, 1e-4, 0, 0,])\r\n \r\n # Describe reactions by writing chemical equations and kinetic parameter info\r\n reactions = [\r\n 'E + S <-> ES; kf = 12, kb = 10.0',\r\n 'ES -> E + P; kf = 32.0',\r\n ]\r\n \r\n # Generate a ReactionSystem from strings\r\n rxn_sys = nsk.ReactionSystem(ID='rxn_sys', \r\n reactions=reactions,\r\n species_system=sp_sys)\r\n \r\n \r\n # Describe forced concentration spikes for any species \r\n # (e.g., from feeding substrate in a fed-batch regime)\r\n spikes = {20000: 'Target; S; 1e-4', # at t=40000, add enough S to achieve [S]=1e-4\r\n 50000: 'Target; S; 1e-4', # at t=50000, add enough S to to achieve [S]=1e-4\r\n 80000: 'Target; S; 1e-4', # at t=80000, add enough S to achieve [S]=1e-4\r\n 100000: 'Change; S; 2e-4',# at t=100000, add enough S to increase [S] by 2e-4\r\n }\r\n \r\n # Simulate the ReactionSystem\r\n rxn_sys.solve(t_span=[0, 2*24*3600],\r\n sp_conc_for_events={'S':1e-6},\r\n spikes=spikes)\r\n \r\n # Plot results\r\n rxn_sys.plot_solution()\r\n\r\n\r\n.. image:: docs/source/_static/images/example_3_plot_i.png\r\n :width: 400\r\n\r\n\r\nBug reports\r\n-----------\r\n\r\nTo report bugs, please use NSKinetics's Bug Tracker at:\r\n\r\n https://github.com/sarangbhagwat/nskinetics\r\n\r\nContributing\r\n------------\r\nFor guidelines on how to contribute, visit:\r\n\r\n [link to be added]\r\n\r\n\r\nLicense information\r\n-------------------\r\n\r\nSee ``LICENSE.txt`` for information on the terms & conditions for usage\r\nof this software, and a DISCLAIMER OF ALL WARRANTIES.\r\n\r\nAlthough not required by the NSKinetics license, if it is convenient for you,\r\nplease cite NSKinetics if used in your work. Please also consider contributing\r\nany changes you make back, and benefit the community.\r\n\r\n\r\nAbout the authors\r\n-----------------\r\n\r\nNSKinetics was created and developed by `Sarang S. Bhagwat <https://github.com/sarangbhagwat>`__ as part of the `Scown Group <https://cscown.com/>`__ and the `Energy & Biosciences Institute <https://energybiosciencesinstitute.org/>`__ at the `University of California, Berkeley (UC Berkeley) <https://www.berkeley.edu/>`__. \r\n\r\n\r\nReferences\r\n----------\r\n.. [1] ` To be added <link to be added>`__.\r\n\r\n\r\n",
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