CERENA

CERENA analyzes stochastic chemical kinetics by implementing simulation and approximation methods for the Chemical Master Equation to study cellular processes such as gene expression and signal transduction.

Key Features:

• CME-derived descriptions: Implements stochastic simulation algorithms and finite state projection for microscopic modeling, system size expansion for mesoscopic descriptions, and moment equations for macroscopic descriptions.
• Conditional moment equations: Provides conditional moment equations as a hybrid description of stochastic processes.
• General kinetics support: Supports time-dependent propensities and non-mass-action kinetics for arbitrary chemical reaction networks.
• SBML and symbolic models: Supports Systems Biology Markup Language (SBML) import and symbolic model generation.
• Sensitivity analysis: Implements forward and adjoint sensitivity analyses to support parameter estimation and uncertainty analysis.
• MATLAB implementation: Implemented in MATLAB with MEX-files for computational efficiency.

Scientific Applications:

• Gene expression stochasticity: Analyze intrinsic and extrinsic noise in gene expression and its contribution to cell-to-cell variability.
• Signal transduction variability: Study stochastic dynamics and variability in signaling pathways.
• Parameter estimation and uncertainty quantification: Support parameter inference and uncertainty analysis using sensitivity methods.
• Comparative analysis of approximation methods: Compare and select CME-based approximation approaches across complex reaction networks and kinetics.

Methodology:

Approximates solution statistics of the Chemical Master Equation using stochastic simulation algorithms, finite state projection, system size expansion, moment equations, and conditional moment equations; supports time-dependent propensities and non-mass-action kinetics; provides forward and adjoint sensitivity analyses; implemented in MATLAB with MEX-files.