Preliminary evidence for chaotic signatures in host-microbe interactions

Area of Science:

• Microbial Pathogenesis
• Dynamical Systems Theory
• Infectious Disease Dynamics

Background:

• Host-microbe interactions can be deterministic, stochastic, or chaotic, but their dynamical characteristics are poorly understood experimentally.
• Time-to-death data is a common experimental outcome but often obscures underlying interaction dynamics.
• Determining the nature of these dynamics is vital for predicting disease outcomes and ensuring experimental reproducibility.

Purpose of the Study:

• To investigate if time-to-event data can reveal chaotic signatures in host-microbe interactions.
• To assess the presence of chaotic dynamics in model host-pathogen systems.
• To explore the implications of chaotic dynamics for microbial pathogenesis and disease prediction.

Main Methods:

• Developed an inversion measure to detect chaotic signatures in simulated time-to-event data.
• Successfully distinguished chaotic from stochastic time-to-event distributions.
• Analyzed time-to-death data in *Caenorhabditis elegans* and *Drosophila melanogaster* infected with *Pseudomonas* species.

Main Results:

• Demonstrated the ability to detect chaos in simulated time-to-event data.
• Found preliminary suggestions of chaotic signatures in *C. elegans* and *D. melanogaster* infection models.
• Highlighted the need for larger, more detailed datasets to confirm findings.

Conclusions:

• Host-microbe interactions, specifically infectious diseases, may exhibit chaotic dynamics.
• Chaos in these systems could impact disease predictability and experimental reproducibility.
• Further research with refined data collection is necessary to validate these preliminary findings.