Collective chemotaxis through noisy multicellular gradient sensing

TL;DR

This study presents a computational model showing how multicellular clusters sense chemical gradients noisily and migrate collectively, revealing an optimal size for efficient movement balancing sensitivity and drag.

Contribution

The paper introduces a novel computational framework linking noisy gradient sensing to collective migration, highlighting the optimal cluster size for effective chemotaxis.

Findings

Larger clusters improve gradient detection and polarization bias.

Increased cluster size also increases drag, hindering movement.

An optimal cluster size balances sensitivity and drag for best migration efficiency.

Abstract

Collective cell migration in response to a chemical cue occurs in many biological processes such as morphogenesis and cancer metastasis. Clusters of migratory cells in these systems are capable of responding to gradients of less than 1% difference in chemical concentration across a cell length. Multicellular systems are extremely sensitive to their environment and while the limits to multicellular sensing are becoming known, how this information leads to coherent migration remains poorly understood. We develop a computational model of multicellular sensing and migration in which groups of cells collectively measure noisy chemical gradients. The output of the sensing process is coupled to individual cells polarization to model migratory behavior. Through the use of numerical simulations, we find that larger clusters of cells detect the gradient direction with higher precision and thus achieve stronger polarization bias, but larger clusters also induce more drag on collective motion. The trade-off between these two effects leads to an optimal cluster size for most efficient migration. We discuss how our model could be validated using simple, phenomenological experiments.