sRACIPE

sRACIPE implements a randomization-based method for gene circuit modeling that enables the study of gene expression noise and parametric variation on gene regulatory circuits using only circuit topology.

Key Features:

• Random Circuit Perturbation (RACIPE) Algorithm: Simulates an ensemble of differential equation models via randomization of kinetic parameters to explore dynamical behaviors of gene regulatory circuits.
• Deterministic and Stochastic Simulations: Provides unified modeling of gene regulatory circuits with diverse interaction types using both simulation approaches.
• Multi-Noise-Level Simulation: Simulates ensembles of models with random kinetic parameters at multiple noise levels to assess intrinsic and extrinsic noise effects.
• Perturbation Modeling: Introduces capabilities for modeling perturbation effects on gene regulatory circuits.
• Extrinsic Signaling Modeling: Supports incorporation of extrinsic signaling inputs into circuit simulations.
• Time-Corrected Noise: Implements a new feature for modeling time-corrected noise in simulations.
• Diagnostic Tool: Includes a diagnostic tool to ensure proper simulations.
• Statistical Analysis of Gene Expressions: Reveals the basin of attraction and stability of various phenotypic states and their changes associated with intrinsic and extrinsic noises.

Scientific Applications:

• Phenotypic State Characterization: Identifies basins of attraction and stability properties of phenotypic states in gene regulatory circuits.
• Noise Effect Evaluation: Evaluates the effects of both stochastic cellular processes and parametric variation on gene regulatory circuit behavior.
• Systems Biology Circuit Modeling: Serves as a versatile modeling toolkit for the systems biology community to study gene regulatory circuits with diverse interaction types.

Methodology:

sRACIPE simulates an ensemble of differential equation models via randomization of kinetic parameters, using only the topology of the gene regulatory circuit. Statistical analysis of generated gene expressions reveals properties of phenotypic states.