Evidence map›Paper›PMID 38857304›Full record

ArticlePLoS computational biology2024

Reliable ligand discrimination in stochastic multistep kinetic proofreading: First passage time vs. product counting strategies.

Xiangting Li, Tom Chou

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Parallel reactions on a single T cell receptor offer a robust kinetic proofreading mechanism.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  2. Article
  3. With the leisure of time, kinetic proofreading can still perform reliable ligand discrimination.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Xiangting LiDepartment of Computational Medicine, University of California, Los Angeles, California, United States of America.ORCID 0000-0001-5238-7364
Tom ChouDepartment of Computational Medicine, University of California, Los Angeles, California, United States of America.ORCID 0000-0003-0785-6349

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cellular signaling, crucial for biological processes like immune response and homeostasis, relies on specificity and fidelity in signal transduction to accurately respond to stimuli amidst biological noise. Kinetic proofreading (KPR) is a key mechanism enhancing signaling specificity through time-delayed steps, although its effectiveness is debated due to intrinsic noise potentially reducing signal fidelity. In this study, we reformulate the theory of kinetic proofreading (KPR) by convolving multiple intermediate states into a single state and then define an overall "processing" time required to traverse these states. This simplification allows us to succinctly describe kinetic proofreading in terms of a single waiting time parameter, facilitating a more direct evaluation and comparison of KPR performance across different biological contexts such as DNA replication and T cell receptor (TCR) signaling. We find that loss of fidelity for longer proofreading steps relies on the specific strategy of information extraction and show that in the first-passage time (FPT) discrimination strategy, longer proofreading steps can exponentially improve the accuracy of KPR at the cost of speed. Thus, KPR can still be an effective discrimination mechanism in the high noise regime. However, in a product concentration-based discrimination strategy, longer proofreading steps do not necessarily lead to an increase in performance. However, by introducing activation thresholds on product concentrations, can we decompose the product-based strategy into a series of FPT-based strategies to better resolve the subtleties of KPR-mediated product discrimination. Our findings underscore the importance of understanding KPR in the context of how information is extracted and processed in the cell.

Indexed as

Stochastic ProcessesComputational BiologyDNA ReplicationHumansKineticsLigandsModels, BiologicalReceptors, Antigen, T-CellSignal TransductionLigandsReceptors, Antigen, T-Cell

Identifiers

PMID38857304
PMCPMC11192422

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.