IBRENA

IBRENA analyzes and simulates cellular biochemical reaction networks to perform forward and adjoint sensitivity analyses and enable model reduction and dimensionality-reduction-based identification of critical components.

Key Features:

• Simulation of cellular reaction networks: Simulates cellular biochemical reaction networks to study system dynamics.
• Forward and adjoint sensitivity analysis: Performs sensitivity analyses using both forward and adjoint methods to quantify parameter effects on system behavior.
• Principal component analysis (PCA) and singular-value decomposition (SVD): Applies PCA and SVD for dimensionality reduction and identification of dominant modes.
• Model reduction: Implements model reduction techniques to simplify complex networks while preserving essential dynamics.
• High-throughput genomic and proteomic data analysis: Extends analysis to high-throughput genomic and proteomic datasets for integration with biochemical network models.

Scientific Applications:

• Biochemical network simulation: Investigating dynamics of biochemical and cellular reaction networks through in silico simulation.
• Parameter sensitivity analysis: Assessing how variations in kinetic or regulatory parameters affect network behavior using forward and adjoint methods.
• Dimensionality reduction and critical component identification: Identifying critical components and dominant dynamical modes in large networks using PCA and SVD.
• Omics integration: Integrating and analyzing high-throughput genomic and proteomic data in the context of network models.

Methodology:

Simulation of biochemical reaction networks; forward and adjoint sensitivity analysis; principal component analysis (PCA) and singular-value decomposition (SVD); and model reduction.