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Simulations of rotational diffusion ("tumbling") of fluorophores

This repo contains GPU-accelerated code for modeling tumbling of fluorophores. This code supports our recent paper, "From cameras to confocal to cytometry: measuring tumbling rates is a general way to reveal protein binding." Read the publication here: https://andrewgyork.github.io/tumbling_is_general

Citation:

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

Julia R. Lazzari-Dean, Austin E.Y.T. Lefebvre, Rebecca Frank Hayward, Lachlan Whitehead, Maria Ingaramo, Andrew G. York. From cameras to confocal to cytometry: measuring tumbling rates is a general way to reveal protein binding. (2023) DOI

Instructions:

To replicate the data, figures, and animations from the paper, follow these steps:

  1. Follow the setup instructions below.
  2. Navigate to the respective "get_figures" directory for the figure you would like to replicate.
  • For data replication: Run the simulation_"figure_name".py file to generate the data for the figure.
    • This data will be saved in the "data" subdirectory within the figure directory.
  • For plotting replication: Run the plot_"figure_name".py file to generate the figure.
    • Note: This automatically loads the latest data from the "data" directory.
  • For animation replication: Run the animate_"figure_name".py file to generate the animation.
    • The gif animations and the individual frames will be saved in the "images" subdirectory within the figure directory, with subdirectories based on 3 orthogonal projection-like views and a skewed 3d view.

Setup:

Packages:

  • Note: it's recommended but usually not required to use a clean virtual environment to install the packages below to avoid conflicts with other packages on your system.

  • CPU-based (slower for large simulations):

    • option 1 (recommended): pip install -r requirements.txt
    • option 2: pip install numpy pandas matplotlib

  • GPU-based (slower for small simulations)

    • Windows and Linux with NVIDIA GPUs only
    1. Install CUDA if you haven't already:
    2. Install required packages:
      • option 1 (recommended): pip install -r requirements.txt cupy-cuda12x
      • option 2: pip install numpy pandas matplotlib cupy-cuda12x
        • Note: cupy-cuda12x is the latest version of cupy that supports CUDA 12.0, but you should install the latest version of cupy that supports your specific CUDA version.

System Requirements:

Everything in this repo has only been tested on the following:

  • python 3.10
    • Mac
    • Windows
      • CPU
      • GPU
    • (It probably also works on Linux for both CPU and GPU)