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Coincubation Assay for Quantifying Competitive Interactions between Vibrio fischeri Isolates

Coincubation Assay for Quantifying Competitive Interactions between Vibrio fischeri Isolates

Published on: July 22, 2019

Feedback-based, system-level properties of vertebrate-microbial interactions.

Ariel L Rivas¹, Mark D Jankowski, Renata Piccinini

¹Center for Global Health, University of New Mexico, Albuquerque, New Mexico, USA. alrivas@unm.edu

|February 26, 2013

Summary

A novel three-dimensional (3D) systems biology (SB) and evolutionary biology (EB) approach effectively analyzed infectious disease data, outperforming classic methods. This advanced analysis identified distinct disease phases and improved discrimination of infections across species.

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Area of Science:

Infectious disease dynamics
Systems biology
Evolutionary biology
Data analysis

Background:

Improved characterization of infectious disease dynamics is crucial.
Three-dimensional (3D) data analysis of feedback-like processes offers a potential avenue for improved characterization.
Current methods may not fully capture the complexity of host-microbial interactions.

Purpose of the Study:

To evaluate a systems biology (SB) and evolutionary biology (EB) approach for detecting infectious disease data patterns.
To compare the efficacy of classic data structures with multi-dimensional SB/EB approaches (2D, 3D, rotating 3D).
To assess the ability of these methods to discriminate between disease-positive and disease-negative states and identify disease phases.

Main Methods:

Utilized leukocyte data structures designed to diminish variability and enhance discrimination.
Explored four data structures: classic single-leukocyte counts and three SB/EB levels (2D, 3D, multi-dimensional).
Applied the approach to avian, human, and bovine species infected with various agents, including antimicrobial-resistant bacteria.

Main Results:

Classic data structures failed to discriminate between disease-positive (D+) and disease-negative (D-) groups due to overlapping distributions.
Multi-dimensional SB/EB analysis revealed a continuous, circular data structure that facilitated statistically significant partitioning.
The 3D SB/EB approach successfully distinguished three feedback phases (steady, positive, negative) and differentiated infections like MRSA and malaria, identifying false-negative observations.

Conclusions:

Structuring data and considering 3D relationships allows for greater information extraction from existing datasets.
Well-conserved feedback-like functions estimated from these structures can distinguish between ancient and recent host-microbial interactions.
This approach has potential applications in disease diagnosis, error detection, and epidemiological modeling.