A unified method for assessing the observability of dynamic complex systems.

Area of Science:

• Systems biology
• Complexity science
• Geometric information theory

Background:

• Systems theory in biology and medicine seeks predictive models of complex systems.
• Current methods struggle to assess universal causal principles due to multi-scale information integration and inherent uncertainty in organisms.
• Existing tools lack the ability to evaluate the stability and mechanistic nature of causal processes across different scales.

Purpose of the Study:

• To develop a novel method for assessing the stability of causal processes in biological systems.
• To introduce a complexity measure, the Φ-S diagram, for identifying mechanistic responses.
• To provide a more robust framework for representing and analyzing complex biological systems.

Main Methods:

• Analysis of time series patterns using geometric information theory and persistent homology.
• Evaluation of information within phase space trajectories to detect stability of causal processes.
• Development and application of the Φ-S diagram as a complexity measure.

Main Results:

• The Φ-S diagram was successfully applied to deterministic and real-world health datasets (ICU repository).
• The method confirmed the mechanistic nature of the analyzed biological data.
• Individual variability was observed to potentially impact the detection of cardiac responses, highlighting the need for robust analytical frameworks.

Conclusions:

• The Φ-S diagram offers a reliable method for evaluating causal relationships and mechanistic responses in biological systems.
• This approach provides a more robust framework for understanding complex biological systems, addressing limitations in current methods.
• The study demonstrates the potential of geometric information theory and persistent homology in advancing systems biology analysis.