Distinguishing between mechanisms of cell aggregation using pair-correlation functions

Area of Science:

• * Computational biology
• * Biophysics
• * Mathematical modeling

Background:

• * Cell aggregates form in vitro via proliferation, chemotaxis, or cell contact.
• * Understanding aggregate formation mechanisms is crucial for biological research.
• * Distinguishing between different aggregation drivers remains a challenge.

Purpose of the Study:

• * To develop an agent-based model simulating cell aggregate formation.
• * To utilize a pair-correlation function for quantifying spatial patterns.
• * To differentiate between aggregate formation mechanisms like proliferation and biased motility.

Main Methods:

• * Agent-based modeling of cell behavior on a 2D substrate.
• * Simulation of unbiased random motion, rapid proliferation, and biased cell motility.
• * Application of a pair-correlation function to analyze spatial patterns.

Main Results:

• * The pair-correlation function successfully distinguishes between uniform random distribution and patterns generated by proliferation or biased motion.
• * A characteristic inter-aggregate distance was identified with dominant biased motion, absent in proliferation-driven aggregates.
• * Analysis of cancer cell aggregate images aligned with proliferation-based simulation predictions.

Conclusions:

• * The pair-correlation function is a valuable tool for analyzing spatial patterns in cell aggregates.
• * This method can provide insights into the underlying mechanisms driving aggregate formation.
• * The findings support the potential application of pair-correlation functions in experimental cell biology.