This repository contains the Julia code for modelling the activity of the small GTPase RhoA in response to local light stimulation at the membrane and focal adhesions. The model is based on experimental data obtained from a study by Heydasch et al. (2023), which investigates the role of DLC1, a GTPase activating protein, in the regulation of RhoA signaling.

The provided model captures the differential dynamics of RhoA activity observed during optogenetic stimulation experiments. Local and transient light activation was applied at the membrane and focal adhesions, eliciting distinct responses depending on the presence or absence of DLC1. The model is implemented using ModelingToolkit.jl and formulated as a system of ordinary differential equations (ODEs) that simulate RhoA activity over time under each experimental condition and localization.
To represent the regulatory mechanism in the wild-type condition, the model incorporates a negative feedback loop, where active RhoA promotes the activation of GAPs—presumably mediated by DLC1—which in turn inhibit RhoA. In contrast, the DLC1 knockout removes this feedback interaction: GAP activity is modeled as constant, representing a rapid steady-state due to the absence of RhoA-dependent regulation. This structural difference is reflected in the schematic below, which outlines the interactions included in the ODE model for each condition:

The parameters of the model were fitted using various optimisation packages of the SciML ecosystem such as OrdinaryDiffEq.jl and Optimization.jl.

Here are the key steps of code:

• Model Definition:
  The code defines the model using ModelingToolkit.jl, which allows for symbolic representation of the system of equations governing RhoA dynamics.
• Parameter Fitting:
  The model is fit to the experimental data to estimate the parameters governing RhoA activity in the presence and absence of DLC1. This is done using Optimization techniques in Julia.
• Results Plotting:
  Visualisation of the results for different experimental conditions (e.g., wild type vs DLC1 knockout).

This repository contains three main directories:

• Code:
  Pluto/Jupyter notebooks that were used for modeling RhoA dynamics, fitting the model to experimental data, and visualizing the results. Also Julia scripts for parameter identifiability analysis. The Jupyter notebooks work with a Julia kernel and still require Julia to be installed alongside the package IJulia.jl.
• Data:
  Experimental data for RhoA activity under various conditions (presence or knock-out of DLC1). Source: Lucien Hinderling, Pertz Lab, Institute of Cell Biology, University of Bern.
• Plots:
  Plots of the data fitting results at the membrane and focal adhesions.

To run the code in this repository, you need to have Julia installed which can be downloaded at https://julialang.org/downloads/. Please note that Julia Version 1.10.4 was used as referenced in the Manifest.toml file.

Clone this repository to your local machine.

git clone https://github.com/girochat/RhoAModelling.git

For reproducibility, it is recommended to use the directory of the project as a Julia environment:

1. Go to the directory:
   cd /your_path_to_the_repo/RhoAModelling
2. Open Julia and open the REPL by pressing ']'
3. In the REPL, activate the local environment and instantiate the packages:
   pkg> activate .
pkg> instantiate

The Pluto notebook provides an interactive environment for running and visualising the RhoA model. To open the notebook, follow these steps: Start the Pluto notebook in Julia:

using Pluto
Pluto.run()

In the Pluto interface, open the desired notebook file to start exploring the model, adjust parameters, and visualize the results.

If you prefer using Jupyter Notebook, the Jupyter version of the Pluto notebooks are also provided.

The results will include a plot of the fitted dynamics for RhoA as well as the other model species.
Here is an example of the output for the results at the focal adhesions:

If you use this code or model in your own research, please cite the following paper:

Max Heydasch, Lucien Hinderling, Jakobus van Unen, Maciej Dobrzynski, Olivier Pertz (2023). GTPase activating protein DLC1 spatio-temporally regulates Rho signaling. eLife 12:RP90305. DOI: https://doi.org/10.7554/eLife.90305.1

This repository is licensed under the MIT License. See the LICENSE for more details.