Predicting critical transitions in a model of systemic inflammation

Area of Science:

• Physiology
• Dynamical Systems Theory
• Mathematical Biology

Background:

• The human body functions as a dynamical system with health and disease as steady states.
• Identifying transitions between these states is vital due to inter- and intra-individual variability.
• Existing methods for detecting critical transitions are limited by data availability in physiology.

Purpose of the Study:

• To develop a model-based approach for identifying critical transitions in systemic inflammation.
• To create a warning signal metric with minimal data and system structure assumptions.
• To assess the efficacy of early intervention based on the warning signal.

Main Methods:

• Developed a mathematical model of systemic inflammation with gradually increasing bacterial load.
• Derived a warning signal metric to predict abrupt state transitions.
• Simulated interventions to restore homeostasis based on warning signal levels.

Main Results:

• The warning signal metric successfully predicted forthcoming abrupt transitions in the inflammation model.
• Intervention was effective in restoring homeostasis when initiated upon elevated warning signals.
• Early intervention prevented the system from reaching a new, potentially detrimental, steady state.

Conclusions:

• A model-based approach combined with data analysis can predict physiological transitions.
• The derived warning signal offers a promising tool for early detection of critical transitions in systemic inflammation.
• This quantitative approach advances the study of complex biological systems and timely medical interventions.