ReaDDy - A particle-based
reaction-diffusion simulator

Reaction Diffusion Dynamics

Welcome to the website of ReaDDy - a particle-based reaction-diffusion simulator, written in C++ with python bindings. ReaDDy is an open-source project, developed and maintained by Moritz Hoffmann, Christoph Fröhner and Frank Noé of the AI4Science (former CMB) group at the Freie Universität Berlin. This project continues the java software of the same name, by Johannes Schöneberg and Frank Noé.

What is ReaDDy?

The logo simulation mimicks a predator prey system, i.e., a population growth process that frequently occurs in biology. Sometimes, this growth process is subjected to spatial constraints. There are three different particle types, referring to that biological model:

  • Type 1, the red “logo particles“, serve as the spatial barriers. They have been given an attraction potential between them and start in a position that resembles the ReaDDy logo.
  • Type 2, the purple “prey“. If there are no predators around, they will replicate.
  • Type 3, the grey “predator” particles. They die out if there is no prey but replicate in their presence by consuming them.

It is visible during the time course of the simulation, that the spatial distribution of the particles, their crowding inducing occurrence in masses as well as spatial constraints like barriers influence the growth of the populations dramatically. What is true for this simplified example is ubiquitous not only in molecular and cellular biology but in multiple other fields.

ReaDDy has been designed to fit the modeling requirements of such processes: Particle (or agent) based reaction diffusion systems in which particle-particle interactions play an important role and where the systems are subjected to crowding or spatial constraints.

Get started

import readdy

# ----- Step 1: Set up reaction diffusion system

system = readdy.ReactionDiffusionSystem(box_size=(10, 10, 10))

system.add_species("A", diffusion_constant=2.0)
system.reactions.add("mydecay: A ->", rate=1.)
system.reactions.add("myfission: A -> A +(1) A", rate=3.)

# ----- Step 2: Create simulation instance out of configured system

simulation = system.simulation(kernel="CPU")

simulation.output_file = "out.h5"
simulation.add_particle("A", [0.,0.,0.])

# ------ Step 3: run the simulation, 0.01)

The above snippet performs a ReaDDy simulation, which consists of three steps:

  1. Configure the system
  2. Setup and run the simulation
  3. Analyze results

See this ipython notebook for an example of the basic features


When using ReaDDy in your work, please cite the following paper

  title={ReaDDy 2: Fast and flexible software framework for interacting-particle reaction dynamics},
  author={Hoffmann, Moritz and Fr{\"o}hner, Christoph and No{\'e}, Frank},
  journal={PLoS computational biology},
  publisher={Public Library of Science}

Also note these further publications related to ReaDDy:

  • Schöneberg, Johannes, et al. Lipid-mediated PX-BAR domain recruitment couples local membrane constriction to endocytic vesicle fission. Nature communications 8 (2017): 15873.
  • Schöneberg, Johannes, and Frank Noé. ReaDDy-a software for particle-based reaction-diffusion dynamics in crowded cellular environments. PloS one 8.9 (2013): e74261.
  • Dibak, Manuel, et al. Diffusion-influenced reaction rates in the presence of pair interactions. The Journal of chemical physics 151.16 (2019): 164105.
  • Fröhner, Christoph, and Frank Noé. Reversible interacting-particle reaction dynamics. The Journal of Physical Chemistry B 122.49 (2018): 11240-11250.
  • Ullrich, Alexander, et al. Dynamical organization of syntaxin-1A at the presynaptic active zone. PLoS computational biology 11.9 (2015): e1004407.
  • Gunkel, Monika, et al. Higher-order architecture of rhodopsin in intact photoreceptors and its implication for phototransduction kinetics. Structure 23.4 (2015): 628-638.
  • Biedermann, Johann, et al. ReaDDyMM: Fast interacting particle reaction-diffusion simulations using graphical processing units. Biophysical journal 108.3 (2015): 457-461.
  • Schöneberg, Johannes, et al. Explicit spatiotemporal simulation of receptor-G protein coupling in rod cell disk membranes. Biophysical journal 107.5 (2014): 1042-1053.
  • Schöneberg, Johannes, Alexander Ullrich, and Frank Noé. Simulation tools for particle-based reaction-diffusion dynamics in continuous space. BMC biophysics 7.1 (2014): 11.
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