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sMIE: revealing critical transitions in complex biological systems using single-sample mutual information entropy
Yuan Tao1, Ruoqi Lan1, Yunshan Lai1
1School of Mathematics, South China University of Technology, Guangzhou, People's Republic of China.
Summary
A new computational framework, single-sample mutual information entropy (sMIE), identifies critical transitions in biological systems. This method analyzes gene networks from single samples, aiding early disease detection and intervention.
Area of Science:
- Complex Systems Biology
- Computational Biology
- Systems Medicine
Background:
- Complex biological systems, including disease progression, undergo critical transitions or tipping points.
- Early detection of these transitions is crucial for effective intervention but challenging with limited clinical samples.
- Conventional statistical methods struggle with high-throughput biological data from small sample sizes.
Purpose of the Study:
- To develop a novel model-free computational framework for identifying critical transitions in biological systems.
- To introduce the single-sample mutual information entropy (sMIE) index for detecting tipping points.
- To enable the analysis of critical states using high-throughput biological data from individual samples.
Main Methods:
- Developed a model-free computational framework utilizing single-sample mutual information entropy (sMIE).
- The sMIE index measures differences in molecular dynamic networks of a single sample against reference samples using local gene networks.
- Applied the sMIE framework to numerical simulations and diverse real-world datasets (bulk tumor and single-cell).
Main Results:
- Successfully identified critical transitions and tipping points in numerical and seven real-world biological datasets.
- Demonstrated the reliability and accuracy of the sMIE index in detecting critical states.
- Functional analysis of signaling genes provided insights for further research.
Conclusions:
- The sMIE framework offers a robust method for identifying critical transitions in complex biological systems, even with small sample sizes.
- This approach enhances the potential for early disease detection and intervention by pinpointing critical phases.
- The findings contribute to understanding critical transitions and intelligent control in complex systems.
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