StochDecomp

StochDecomp decomposes noise in stochastic biochemical reaction models to quantify individual reaction contributions to system variability.

Key Features:

• Matlab implementation: Provided as a Matlab package for computational analysis of stochastic biochemical models.
• Noise decomposition: Calculates contributions of individual biochemical reactions to the overall variability in a system's output.
• Linear Noise Approximation (LNA): Uses the Linear Noise Approximation to represent stochastic fluctuations in molecular populations.
• Bayesian parameter inference: Incorporates Bayesian inference to estimate model parameters from experimental data for noise attribution.
• Applicability to complex systems: Demonstrated on the JAK-STAT signaling pathway to analyze noise propagation in intricate biochemical networks.

Scientific Applications:

• Signaling pathway analysis: Identifies and quantifies noise sources in signaling networks, exemplified by the JAK-STAT pathway.
• Gene expression variability: Quantifies contributions of reactions to variability in gene expression and signal transduction.
• Systems biology: Enables quantitative analysis of stochasticity in mechanistic network models.
• Synthetic biology: Supports design considerations by attributing variability to specific reactions in engineered circuits.
• Pharmacology: Assesses how stochastic reaction contributions influence drug-targeted signaling dynamics.

Methodology:

Represent the system using stochastic biochemical reaction models; apply the Linear Noise Approximation (LNA) to approximate fluctuations; decompose total variability into contributions from individual reactions; perform Bayesian parameter inference using experimental data to estimate model parameters for noise attribution.