Challenging molecular dogmas in human sepsis using mathematical reasoning

Area of Science:

• Immunology
• Systems Biology
• Computational Biology

Background:

• Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection.
• The precise definition and understanding of this dysregulated state remain incomplete.
• Current knowledge gaps hinder effective diagnosis and management strategies.

Purpose of the Study:

• To dissect the definition of sepsis and its implications for clinical practice.
• To explore pathophysiological features and dogmas associated with sepsis.
• To establish a framework for improving sepsis diagnosis and management through data science.

Main Methods:

• Deliberation on pathophysiological features including cytokine storms, immune paralysis, dormancy, and altered homeostasis.
• Application of mathematical reasoning to test plausibility of host-response trajectories.
• Consideration of multi-omics and big data for guiding artificial intelligence (AI) approaches.

Main Results:

• Identification of three interlinked cardinal rules governing host-response trajectories in sepsis.
• Rule 1: Immune response amplitude is insufficient alone.
• Rule 2 & 3: Response depends on bioenergetic capacity and stability/alterations in homeostasis setpoints.

Conclusions:

• The three cardinal rules provide a framework for understanding sepsis pathophysiology.
• These rules are essential for guiding data-science and AI in analyzing multi-omics and big data.
• Precision in diagnostic and therapeutic approaches to sepsis can be improved by applying these principles.