Specificity and Completion Time Distributions of Biochemical Processes

TL;DR

This paper analytically investigates the temporal dynamics of kinetic proofreading in biochemical systems, providing formulas for completion time distributions, specificity, and variance, thereby enhancing understanding of process efficiency and accuracy.

Contribution

It offers the first analytical solutions for the distribution of completion times in stochastic kinetic proofreading models, linking specificity with temporal behavior.

Findings

Derived explicit formulas for completion time distributions.

Showed process simplification to a three-point model under certain conditions.

Provided insights into the relationship between specificity and timing in biochemical processes.

Abstract

In order to produce specific complex structures from a large set of similar biochemical building blocks, many biochemical systems require high sensitivity to small molecular differences. The first and most common model used to explain this high specificity is kinetic proofreading, which has been extended to a variety of systems from detection of DNA mismatch to cell signaling processes. While the specification properties of the kinetic proofreading model are well known and were studied in various contexts, very little is known about its temporal behavior. In this work, we study the dynamical properties of discrete stochastic two branch kinetic proofreading schemes. Using the Laplace transform of the corresponding chemical master equation, we obtain an analytical solution for the completion time distribution. In particular we provide expressions for the specificity and the mean and the variance of the process completion times. We also show that, for a wide range of parameters a process distinguishing between two different products can be reduced to a much simpler three point process. Our results allow for the systematic study of the interplay between specificity and completion times as well as testing the validity of the kinetic proofreading model in biological systems.