Spatial information analysis of chemotactic trajectories

Area of Science:

• Microbiology and Biophysics
• Computational Biology
• Information Theory

Background:

• Bacterial chemotaxis involves cells sensing chemoattractant gradients to direct movement.
• Bacterial trajectories spatially represent information acquired about the environment.
• Quantifying this spatial information is crucial for understanding chemotaxis.

Purpose of the Study:

• To adapt k-space information (kSI) methods for quantifying spatial information in bacterial chemotaxis trajectories.
• To develop an experimental probability distribution (EPD) for a more constrained calculation of spatial information.
• To compare kSI and EPD methods and assess their ability to capture chemotactic responses.

Main Methods:

• Application of k-space information (kSI) using Fourier coefficient probabilities to bacterial trajectories.
• Development and application of an experimental probability distribution (EPD) derived from reference chemotactic trajectories.
• Calculation of spatial information and entropy from both kSI and EPD methods.

Main Results:

• kSI successfully captures expected responses to chemoattractant gradients.
• EPD-based spatial information also reflects gradient responses, but with significant differences from kSI.
• EPD-derived entropy serves as a measure of trajectory space complexity.

Conclusions:

• The developed kSI and EPD methods provide robust frameworks for quantifying spatial information in bacterial chemotaxis.
• EPD offers a more constrained approach, accounting for trajectory-specific limitations.
• These methods are generalizable to various trajectory types and non-trajectory data.