MEANS

MEANS implements moment expansion approximation methods to derive closed differential equations for the time-evolution of moments in stochastic biochemical systems, enabling deterministic analysis of their probabilistic behavior.

Key Features:

• Moment Closure Approximation: Generates equations for the time-evolution of system moments and applies a closure ansatz to derive a closed set of differential equations for deterministic analysis of stochastic outputs.
• Parametric Closures: Incorporates parametric closures to improve the accuracy and flexibility of moment approximations.
• Model Scope and Rate Laws: Supports any number of species and moments and does not restrict the type of rate laws used in biochemical models.
• Integration with IPython: Integrates with the IPython interactive environment.

Scientific Applications:

• Stochastic biochemical modeling: Enables deterministic analysis of stochastic descriptions of biochemical systems via moment-based representations.
• Synthetic biology: Supports analysis and design of synthetic biological networks where stochastic effects influence function.
• Pharmacology and systems biology: Applies to pharmacology and systems biology studies that require understanding the probabilistic behavior of biochemical reactions.

Methodology:

Generates differential equations from moments using moment expansion approximation and then applies a closure ansatz with parametric closures to obtain a solvable set of deterministic differential equations.