BioSimulator.jl

BioSimulator.jl implements stochastic simulation algorithms in Julia to model random fluctuations and rare events such as mutations or extinctions in biochemical, ecological, and genetic systems.

Key Features:

• Stochastic Simulation Algorithms: Implements algorithms based on Markov chain theory, including the Gillespie algorithm and τ-leaping, to capture random fluctuations and quantify rare events.
• Modeling Flexibility: Enables programmatic construction of models in Julia to represent a wide range of system complexities from ecology to genetics.
• Petri Net Representation: Supports diagramming of Petri Nets to describe interactions among species and reactions.
• Visualization and Analysis: Computes summary statistics, plots average trajectories with standard deviations over time, and generates frequency distributions of species at specified times.

Scientific Applications:

• Ecology: Simulating species population dynamics, feedback loops, and extinction events.
• Systems Biology: Modeling stochastic biochemical networks and intracellular variability.
• Chemistry: Simulating reaction kinetics with stochasticity for small-molecule systems.
• Genetics: Modeling mutation-driven dynamics and stochastic effects in genetic systems.

Methodology:

Implements Markov chain–based stochastic simulation algorithms (Gillespie algorithm and τ-leaping), supports programmatic model specification in Julia, represents interactions with Petri Nets, and computes summary statistics, average trajectories with standard deviations, and frequency distributions of species at specified times.