IdentiFlow-P

IdentiFlow-P performs identifiability analysis and optimal experimental design to infer interaction strengths in biological networks from perturbation-response data using maximum flow formulations and matroid theory.

Key Features:

• Identifiability Analysis: Uses maximum flow problems to determine which network interaction strengths (parameters) can be reliably inferred from perturbation experiments.
• Optimal Experimental Design: Applies matroid theory to describe relationships between parameter sets and to construct identifiable effective network models for perturbation planning.
• Perturbation Efficiency: Reduces the number of required perturbations, achieving full network identifiability on average with less than a third of the perturbations needed by conventional methods, and identifies perturbation combinations that further decrease experimental workload beyond single-target approaches.

Scientific Applications:

• Systems Biology: Optimizes perturbation experiments to characterize complex biological networks and infer molecular interactions relevant to health and disease.
• Pathway Analysis: Facilitates inference and benchmarking on human pathways and pathway databases such as KEGG to support network-level biological interpretation.

Methodology:

Uses maximum flow formulations for analytical identifiability determination; applies matroid theory to describe parameter dependencies and build identifiable models; benchmarks strategies on a database of human pathways.