Detection and characterization of chemotaxis without cell tracking.

Area of Science:

• Cellular Biology
• Biophysics
• Mathematical Biology

Background:

• Swarming behavior is crucial in biological systems, including immune cell function.
• Chemotactic factor secretion drives cellular swarming, but robust quantification methods are lacking.

Purpose of the Study:

• To present a novel computational method for analyzing and quantifying chemotactic swarming in cellular systems.
• To enable inference of underlying signaling mechanisms from agent positional data.

Main Methods:

• Developed a method to convert time series of agent positional data into functional time series representing agent aggregation.
• Introduced a 'swarming metric' for visualizing collective cell behavior.
• Extended the method to estimate drift and diffusivity parameters in drift-diffusion partial differential equations (PDEs) using functional linear models.

Main Results:

• The functional time series and swarming metric effectively capture the evolution of collective cell movement.
• The method accurately estimates drift coefficients, allowing inference of chemotactic attraction/repulsion strengths and ranges.
• The approach does not require tracking individual agents or mapping chemokine distributions.

Conclusions:

• This novel method provides a robust way to quantify cellular swarming and infer chemotactic signaling from positional data alone.
• The technique is applicable to both simulated agent-based models and experimental data, such as cytotoxic T cell interactions.