GRiNS

GRiNS integrates parameter-agnostic simulation frameworks RACIPE and Boolean Ising formalism into a single Python library for efficient and scalable simulation of gene regulatory network dynamics.

Key Features:

• Parameter-agnostic simulation: GRiNS integrates RACIPE and Boolean Ising formalism without requiring explicit parameterization of computational models.
• GPU acceleration: The library leverages GPU acceleration for efficient and scalable simulations.
• Modular ODE-based RACIPE extension: GRiNS extends the ordinary differential equations (ODE) based RACIPE framework with a more modular design, allowing choice of parameters, initial conditions, and time-series outputs for greater customisability and accuracy in simulations.
• Boolean Ising formalism for large networks: For large networks where ODE-based simulation formalisms do not scale well, GRiNS implements Boolean Ising formalism as a simplified, coarse-grained alternative that significantly reduces computational cost while capturing key dynamical behaviours of large regulatory networks.

Scientific Applications:

• Gene regulatory network dynamics: GRiNS enables parameter-agnostic modeling of gene regulatory networks to study their dynamic and steady-state behaviors.
• Large network simulation: The Boolean Ising formalism allows simulation of large regulatory networks that are computationally intractable with ODE-based methods.

Methodology:

GRiNS integrates RACIPE (an ordinary differential equations-based framework) and Boolean Ising formalism into a unified Python library with GPU acceleration.