PARM

PAR2 Activation and calcium signaling Reaction Model (PARM)

PARM contains rules-based models of PAR2 (proteinase-activated receptor isoform 2) activation, G-protein activation, and calcium signaling via the phospholipase C and IP3 (inositol triphosphate) pathway. Models are encoded as Python program modules using the PySB modeling framework. The models are designed to mathematically model the underlying signaling dynamics relevant to the inactivation of PAR2 in HEK-293 cells by Molecular Hyperthermia as described in:

Kang et al., Transient Photoinactivation of Cell Membrane Protein Activity without Genetic Modification by Molecular Hyperthermia, ACS Nano 2019, 13, 11, 12487–12499 https://doi.org/10.1021/acsnano.9b01993

Table of Contents

1. Install 1. Dependencies 2. pip install 3. Manual install 4. Recommended additional software
2. Documentation and Usage 1. Models in PARM 2. Example usage
3. License
4. Change Log
5. Contact
6. Citing

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Install

PARM installs as the parm Python package. It is tested with Python 3.8.

Dependencies

Note that parm has the following core dependency:

• PySB >= 1.13.2

pip install

First, install PySB.

You can then install parm version 0.3.0 with pip sourced from the GitHub repo:

with git installed:

Fresh install:

pip install git+https://github.com/NTBEL/PARM@v0.3.0

Or to upgrade from an older version:

pip install --upgrade git+https://github.com/NTBEL/PARM@v0.3.0

without git installed:

Fresh install:

pip install https://github.com/NTBEL/diffusion-fit/archive/refs/tags/v0.3.0.zip

Or to upgrade from an older version:

pip install --upgrade https://github.com/NTBEL/diffusion-fit/archive/refs/tags/v0.3.0.zip

Manual install

First, install PySB. Then, download the repository and from the PARM folder/directory run

pip install .

Recommended additional software

The following software is not required for the basic operation of parm, but provide extra capabilities and features when installed.

Cython

Cython is used by PySB to compile the ODE reactions on-the-fly, which can greatly improve model performance when running with the ScipyOdeSimulator.

pip:

pip install Cython

conda:

conda install cython

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Documentation and Usage

Models in PARM

The core model of PARM is defined in parm.parm and can be imported at the package level like from parm import model.

Additionally, PARM contains 2 extensions of the parm.parm model which incorporate an antagonist:

• parm.antagonist.competitive - Adds a competitive antagonist.
• parm.antagonist.noncompetitive - Adds a noncompetitive antagonist which operates via negative allosteric modulation of the agonist binding affinity. (Note: The factor which controls the allosteric modulation could also be set such that the antagonist induces positive allosteric modulation, increasing agonist binding affinity.)

There are also 4 models with mechanistic variations defined in parm.variants:

• parm.variants.precoupled - Adds pre-coupling between PAR2 and the G-protein heterotrimer such that some PAR2 can bind to the heterotrimer under resting conditions (without any agonist).
• parm.variants.classic - The receptor binding and G-protein interaction mechanism is based on a classic activation mechanism without pre-coupling.
• parm.variants.par2_synthesis_degradation - This model only contains PAR2 with reactions for its resting synthesis and degradation.
• parm.variants.LR - This model only contains the ligand-receptor binding with concerted activation.

Example usage

from parm import model
from parm.util import run_model

tspan = list(range(0, 10, 1))

traj_out = run_model(model, tspan)

Note on pre-equlibration

The main parm model includes some calcium homeostasis reactions that may require pre-eqilibration before running the actual simulation. This affects the calcium concentrations in different compartments and can affect the estimate of the FRET ratio. The model can pre-equilibrated using the parm.util.pre_equilibrate function. Here is an example:

from parm import model
from parm import util
import numpy as np
from pysb.simulator import ScipyOdeSimulator

# set the time span for pre-eqilibration.
tspan_pre = list(range(0, 3000, 1))
# Run the pre-equlibration.
param_values_eq, initials_eq = util.pre_equilibrate(model, tspan_pre)

# set the time span for the simulation.
tspan = list(range(0, 300, 1))                                                      
# Setup the PySB solver/simulator.
solver = ScipyOdeSimulator(model, tspan=tspan, integrator='lsoda')
# Run the simulation.
sim = solver.run(param_values=param_values_eq, initials=initial_eq)

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License

This project is licensed under the MIT License - see the LICENSE file for details

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Change Log

See: CHANGELOG

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Contact

Please open a GitHub Issue to report any problems/bugs or make any comments, suggestions, or feature requests for PARM.

If you need assistance with PySB-specific issues then you can also try the pysb gitter channel: https://gitter.im/pysb/pysb

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Citing

If this model or other package features are useful in your research and you wish to cite it, you can use the following software citation:

B. A. Wilson, “PARM: PAR2 Activation and calcium signaling Reaction Model” (v0.3.0), https://github.com/NTBEL/PARM, 2022.