Gradient Sensing via Cell Communication

TL;DR

This paper investigates how cell-to-cell communication mechanisms influence gradient sensing in biological chains, analyzing the effects of communication model complexity, cell size, and influence radius on sensing accuracy and noise susceptibility.

Contribution

It generalizes the LEGI model's communication mechanism, exploring the impact of higher order approximations on gradient sensing and noise performance.

Findings

Higher order communication models better sense external gradients.

More complex models are more prone to noise, reducing signal-to-noise ratio.

The analysis framework can be extended to heterogeneous networks.

Abstract

Experimental evidence lends support to the conjecture that the ability of chains of cells to sense the gradient of an external chemical concentration could rely on cell-to-cell communication. This is the basis for the gradient sensing nature of a specific model type of the Local Excitation, Global Inhibition (LEGI) principle, wherein the strength of the external chemical field is sensed through a comparison between a local exciting species and a global inhibitor that is shared via intra-cellular reactions in the cell chain. In this study we generalize the nearest neighbor communication mechanism in the above-mentioned LEGI model in order to explore how the chemical sensing characteristics depend on the parameterization of the communication itself, cell size, and the radius of influence of neighboring cells. It was found that the radius of influence was less important than the approximating model for communication. Higher order approximations to the communication mechanism were better able to sense an external gradient. However, an analysis of the signal to noise ratio established that higher order models for communication were more prone to noise and thus have a lower signal to noise ratio. The generalization as well as the tools used in the analysis of the dynamics can be extended to more heterogeneous networks and can thus prove useful in using models and observations in the process of understanding chemical gradient via LEGI models with a communication component.