Detecting concentration changes with cooperative receptors

TL;DR

This paper models cells as Neyman-Pearson detectors to analyze how receptor cooperativity enhances the detection of environmental concentration changes, especially in difficult detection scenarios.

Contribution

It introduces a hypothesis testing framework for receptor cooperativity, revealing its role in reducing missed detections and identifying optimal cooperation levels.

Findings

Cooperativity decreases the probability of missed detections.

Optimal cooperation levels exist for balancing detection speed and accuracy.

Easy detection tasks are faster with noncooperative receptors.

Abstract

Cells constantly need to monitor the state of the environment to detect changes and timely respond. The detection of concentration changes of a ligand by a set of receptors can be cast as a problem of hypothesis testing, and the cell viewed as a Neyman-Pearson detector. Within this framework, we investigate the role of receptor cooperativity in improving the cell's ability to detect changes. We find that cooperativity decreases the probability of missing an occurred change. This becomes especially beneficial when difficult detections have to be made. Concerning the influence of cooperativity on how fast a desired detection power is achieved, we find in general that there is an optimal value at finite levels of cooperation, even though easy discrimination tasks can be performed more rapidly by noncooperative receptors.