cayenne

cayenne implements stochastic simulation algorithms to simulate the time evolution of noisy biochemical and gene-regulatory systems and quantify intrinsic noise effects.

Key Features:

• Implementation of Stochastic Simulation Algorithms: Implements the Gillespie algorithm (direct method), the tau-leaping algorithm, and a tau-adaptive algorithm for stochastic simulation of reaction networks.
• Accuracy and Speed: Comparative benchmarks against BioSimulator.jl, GillespieSSA, and Tellurium demonstrate an optimal balance between accuracy and computational speed.
• Benchmarking and Validation: Provides benchmarks and accuracy testing for validating algorithm performance and the reliability of stochastic simulations.

Scientific Applications:

• Systems biology: Enables investigation of intrinsic noise effects in complex systems biology models.
• Gene regulatory networks: Supports simulation of stochastic dynamics in gene regulatory networks.
• Cellular signaling pathways: Allows simulation of stochastic behavior in cellular signaling pathways.
• Synthetic biology: Facilitates prediction of stochastic behavior in engineered biological circuits.
• Pharmacology: Supports modeling of stochasticity relevant to biochemical pharmacology and drug-response dynamics.
• Disease modeling: Aids modeling of stochastic processes relevant to disease mechanisms.

Methodology:

Uses the Gillespie algorithm (direct method) for discrete-state stochastic simulation, employs tau-leaping to approximate multiple reactions within a single time step, and applies a tau-adaptive algorithm to adjust time steps based on system conditions.