Identification of Critical States in Complex Biological Systems Using Cell-Specific Causal Network Entropy.

Area of Science:

• Complex biological systems
• Systems biology
• Computational biology

Background:

• Complex biological systems often undergo critical transitions, marked by abrupt qualitative changes between stable states.
• Identifying these critical states and their signaling molecules is crucial for understanding biological mechanisms.
• Traditional methods struggle with high-dimensional, noisy single-cell data for early warning of critical states.

Purpose of the Study:

• To propose a novel quantitative method for identifying critical states in complex biological processes at the single-cell level.
• To enable early detection and characterization of critical transitions in biological systems.
• To address the limitations of existing methods in model-free, high-dimensional single-cell data analysis.

Main Methods:

• Developed cell-specific causal network entropy (CCNE), a novel quantitative method.
• Inferred cell-specific causal networks and quantified dynamic causal changes.
• Validated CCNE using numerical simulations and five distinct real-world single-cell datasets.

Main Results:

• CCNE accurately identifies critical states and early warning signals of critical transitions.
• CCNE outperforms existing methods in detecting critical transition signals.
• CCNE serves as a computational tool for analyzing temporal changes in cellular heterogeneity and clustering cells.

Conclusions:

• CCNE is an effective method for identifying critical states in complex biological processes using single-cell data.
• The CCNE score provides valuable insights into dynamic causal changes and cellular heterogeneity.
• The reliability of CCNE is supported by functional enrichment and pathway analysis of signaling molecules.