A stochastic version of the Hopfield-Ninio kinetic proofreading model

TL;DR

This paper introduces a stochastic version of the Hopfield-Ninio kinetic proofreading model, demonstrating its high specificity and sensitivity in ligand discrimination, with robustness across various parameters and insights into energy use and response time.

Contribution

The paper develops a stochastic model of kinetic proofreading that proves high specificity and sensitivity, extending understanding of ligand discrimination mechanisms.

Findings

Model achieves extreme specificity in ligand discrimination.

System maintains high sensitivity over wide ligand concentration ranges.

Results are robust to parameter variations.

Abstract

In this paper we study a simple stochastic version of the Hopfield-Ninio kinetic proofreading model. The model is characterized by means of two parameters, the unbinding time, which depends on the binding energy between a ligand and a receptor, and the number of times $M \geq 1$ that a ligand attaches to a receptor. We prove that, under suitable assumptions on M, our model has an extreme specificity, i.e. it is capable to discriminate between different ligands, and a high sensitivity, i.e. the response of the system does not change in a significant manner for ranges of ligands varying within several orders of magnitude. Additional quantities like the amount of energy used by the network or the time required to yield a response will be also computed. We also show that our results are robust, i.e., they do not depend on the specific choice of parameters that we make in this paper.