IdentiFlow

IdentiFlow quantifies interaction strengths in directed biological networks from targeted perturbation responses to assess parameter identifiability and optimize experimental design.

Key Features:

• Identifiability Analysis: Uses a maximum-flow formulation to analytically determine which interaction strengths and network parameters are inferable from specified perturbations and network topology.
• Optimization of Experimental Design: Applies matroid theory to characterize dependencies among parameter sets and to construct identifiable effective network models for optimized perturbation selection.
• Reduction in Perturbation Requirements: Benchmarked against a database of human pathways, it reduced the number of perturbations required for full network identifiability to less than one-third of that needed by random designs.
• Efficient Perturbation Combinations: Identifies combinations of perturbations that further decrease experimental effort compared to single-target perturbations.

Scientific Applications:

• Systems biology: Enables inference of interaction strengths and parameter identifiability in directed molecular networks.
• Pathway characterization: Supports efficient experimental design to fully characterize biological pathways.
• Cellular function and disease mechanism analysis: Facilitates interrogation of pathways underlying cellular functions and disease mechanisms.
• Drug discovery and biotechnology: Reduces experimental effort in perturbation-based assays used for pathway-level screening and target exploration.

Methodology:

Frames parameter identifiability as a maximum-flow problem and integrates mathematical optimization, network theory, and matroid theory.