ppsim: A software package for efficiently simulating and visualizing population protocols

TL;DR

ppsim is a highly efficient software package for simulating population protocols, combining advanced algorithms with visualization tools to enable exact stochastic simulation of complex chemical reaction networks at unprecedented scales.

Contribution

The paper introduces ppsim, a software that implements a quadratic speedup algorithm for population protocol simulation, with dynamic switching and visualization capabilities, enabling large-scale exact stochastic simulations.

Findings

Simulates hundreds of billions of reactions in seconds.

Achieves asymptotic efficiency gains over traditional methods.

Effectively models protocols in molecular programming and DNA reactions.

Abstract

We introduce ppsim, a software package for efficiently simulating population protocols, a widely-studied subclass of chemical reaction networks (CRNs) in which all reactions have two reactants and two products. Each step in the dynamics involves picking a uniform random pair from a population of $n$ molecules to collide and have a (potentially null) reaction. In a recent breakthrough, Berenbrink, Hammer, Kaaser, Meyer, Penschuck, and Tran [ESA 2020] discovered a population protocol simulation algorithm quadratically faster than the naive algorithm, simulating $\Theta(\sqrt{n})$ reactions in *constant* time (independently of $n$, though the time scales with the number of species), while preserving the *exact* stochastic dynamics. ppsim implements this algorithm, with a tightly optimized Cython implementation that can exactly simulate hundreds of billions of reactions in seconds. It dynamically switches to the CRN Gillespie algorithm for efficiency gains when the number of applicable reactions in a configuration becomes small. As a Python library, ppsim also includes many useful tools for data visualization in Jupyter notebooks, allowing robust visualization of time dynamics such as histogram plots at time snapshots and averaging repeated trials. Finally, we give a framework that takes any CRN with only bimolecular (2 reactant, 2 product) or unimolecular (1 reactant, 1 product) reactions, with arbitrary rate constants, and compiles it into a continuous-time population protocol. This lets ppsim exactly sample from the chemical master equation (unlike approximate heuristics such as tau-leaping or LNA), while achieving asymptotic gains in running time. In linked Jupyter notebooks, we demonstrate the efficacy of the tool on some protocols of interest in molecular programming, including the approximate majority CRN and CRN models of DNA strand displacement reactions.