SELANSI

SELANSI simulates the stochastic dynamics of multidimensional gene regulatory networks (GRNs) to provide efficient approximations of stochastic effects in gene regulation.

Key Features:

• Stochastic Simulation: Simulates the inherent stochasticity of gene regulation for multidimensional GRNs.
• Efficient Approximation Methodology: Transforms the Chemical Master Equation into a partial integral differential equation and solves it using a semi-Lagrangian method to improve computational efficiency while preserving accuracy.
• Flexible Network Modeling: Models complex networks with multiple genes exhibiting self- and cross-regulation, multiple transcription factors, and configurable topology, kinetics, and parameters.
• Unrestricted Kinetic Validity: Is not confined to specific types of kinetics, enabling application across diverse kinetic rate laws.
• MATLAB Environment Compatibility: Implements algorithms within the MATLAB environment for integration with MATLAB-based computational workflows.

Scientific Applications:

• Reverse Engineering of Genetic Circuits: Supports inference of regulatory mechanisms by modeling stochastic effects in observed biological data.
• De Novo Design of Genetic Networks: Enables design and evaluation of synthetic genetic circuits by predicting stochastic behavior under different designs.
• Exploration of Gene Regulation Dynamics: Allows analysis of noise-driven dynamics in gene expression to study the impact of stochastic fluctuations on cellular processes.

Methodology:

Transforms the Chemical Master Equation into a partial integral differential equation using intrinsic structural properties of GRNs and solves it numerically with a semi-Lagrangian method.