Critical Parameters in Dynamic Network Modeling of Sepsis

Area of Science:

• Computational Biology
• Systems Biology
• Dynamical Systems Theory

Background:

• Sepsis involves complex interactions between immune and parenchymal cells.
• Organ damage in sepsis arises from dysregulated cellular communication.
• Understanding these dynamics is crucial for predicting sepsis progression.

Purpose of the Study:

• To model sepsis and its organ-damaging effects using a dynamical systems perspective.
• To analyze the coevolutionary dynamics of parenchymal cells, immune cells, and cytokines.
• To elucidate the relationship between network dynamics and pathological states in sepsis.

Main Methods:

• Developed a functional two-layer network model for sepsis.
• Utilized a phase oscillator model within a two-layer system.
• Modeled cytokine interactions with adaptive coupling weights and cell-cell interactions with fixed coupling.

Main Results:

• Demonstrated that cellular cooperation patterns relate to network dynamical patterns.
• Explained sepsis as a transition from synchronized (healthy) to desynchronized (pathological) states.
• Identified critical parameters governing the interplay between parenchyma and immune cells.

Conclusions:

• The model provides a functional description of organ dysfunction in sepsis.
• Insights into stabilizing and destabilizing interactions offer a pathophysiological context for sepsis.
• The model aids in understanding sepsis recurrence risk.