nskinetics 0.2.4

Simulation of Non-Steady state enzyme Kinetics and inhibitory phenomena

The Non-Steady state Kinetics simulation package (NSKinetics)

Contents

• What is NSKinetics?

• Installation

• Documentation

• Bug reports

• Contributing

• License information

• About the authors

• References

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. If you have an installation of Python with pip, simply install it with:

$ pip install nskinetics

To get the git version, run:

$ git clone git://github.com/sarangbhagwat/nskinetics

For help on common installation issues, please visit the documentation.

Documentation

NSKinetic’s full documentation is currently being developed. In the meantime, here are some examples to get started:

Example 1: Simple enzyme-substrate system

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()

Since [ES] was too small to view in the overall plot, let’s also plot it separately:

rxn_sys.plot_solution(sps_to_include=['ES'])

Example 2: Simple enzyme-substrate system + competitive inhibition + “mechanism-based” inhibition

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()

Example 3: Simple enzyme-substrate system in a fed-batch regime

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()

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 as part of the Scown Group and the Energy & Biosciences Institute at the University of California, Berkeley (UC Berkeley).

References

[1]

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