Searcharxiv⌕ Search

arXiv · 2610.31788

Kinetic Proofreading Activation Times Beyond the Weibull Limit

Abstract

T cells can detect a few foreign peptide-major histocompatibility complexes (pMHCs) among a far larger number of self pMHCs. Kinetic proofreading explains this discrimination: a receptor that has bound its ligand must complete nKP signaling steps, each at rate kp, before it activates, and dissociation at any step resets its progress. When many receptors are engaged at once, the time to the first activation has been approximated by a Weibull law taken from the short-time limit of this process. The Weibull law is convenient, but it does not contain the dissociation rate koff, even though koff is what proofreading uses to tell ligands apart. Using the exact first- passage law of the underlying continuous-time Markov chain together with stochastic simulations, we show that the relative error of the Weibull law is set by a single dimensionless number Λ, the Weibull-predicted activation time measured in units of nKP + 1 bound-state lifetimes. For Λ < 1 the first receptor to activate does so before its ligand has ever dissociated, and the Weibull law holds. As Λ grows, activation relies increasingly on repeated unbinding and rebinding. The true activation time then exceeds the Weibull prediction by up to 2900-fold on our parameter grid, and the effective Weibull shape falls to one, so activation becomes memoryless instead of timer-like. We derive a closed form that keeps koff by treating the successful pass through the proofreading chain separately from the time lost to failed attempts. It matches exact medians to within 2% in 19 of 25 parameter sets, with a worst case of about 6%, and reproduces the loss of timer-like behavior. Because Λ is computed from the Weibull prediction itself, it shows in advance how far that formula can be trusted; where it cannot, the new expression requires only a one-dimensional root find.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Thomas Mourier, Cameron Hopkinson, Kenny Wong. 2026-10-22. Kinetic Proofreading Activation Times Beyond the Weibull Limit. https://arxiv.org/abs/2610.31788

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Shared clones make random cell halves look reproducible in pooled screens

Single-cell perturbation screens are used to train and evaluate models that predict perturbation effects, and model scores are judged against how reproducible the measured effects are. Reproducibility is usually judged by splitting each perturbation's cells into two random halves. This assumes that the halves are independent replicates. In pooled screens such as Perturb-seq, however, the cells carrying a guide RNA descend from a limited number of transduced cells, so two random halves contain the same clones: pseudoreplication whose unit is not marked in the data. Using perturbations that should have no effect as controls, we compared random halves with independent replicates from separate transductions, and tested directly with halves that differ only in their clones. In a genome-scale screen of primary T cells, null perturbations agreed between random halves almost always and between independent replicates only half the time, the rate expected from noise alone; measuring more cells did not remove this agreement. Splitting the cells into halves that share no clone, by the T-cell receptor constant region or by a clone barcode, largely removed the excess agreement. Its size equals the share of expression differences set by clones divided by the effective number of clones per perturbation; across 16 screens it was largest in T cells transferred into mice and in a genome-scale primary T-cell screen, one to two orders of magnitude smaller in cell lines, and smallest in non-dividing neurons. Shared clones inflate benchmark ceilings and reliability labels, and when a model's input and its scoring answer come from one transduction, published models that predict perturbation effects in a new cell state appear better than a baseline that ignores the perturbation; against cells from another transduction, most fall below it. Reproducibility should be judged across separate transductions.

q-bio.QM↗

Learning Epidemic Threshold Equations from Simulation: REEBLRA, an Interpretable Bayesian Classification Framework for Reproduction Numbers

The basic reproduction number, $R_0$, is central to infectious-disease modelling, yet analytic expressions can be difficult to derive for high-dimensional, nonlinear, or structurally complex models. We introduce REEBLRA ($R_0$ Equation Estimation using Bayesian Logistic Regression Algorithm), an interpretable framework that learns explicit surrogate equations for epidemic thresholds from parameter--response data. Rather than directly regressing $R_0$, REEBLRA estimates $\Pr\{R_0(\boldsymbolθ)>c\}$ for parameter vector $\boldsymbolθ$ and threshold $c$. It provides a binary equation for classifying epidemic invasion ($R_0>1$) and successive threshold equations defining ordered brackets that approximate $R_0$, the expected number of secondary infections caused by a typical case in a fully susceptible population. The resulting equations support parameter screening, sensitivity interpretation, threshold classification, and exploratory analysis when analytic derivation is cumbersome. Bayesian logistic regression using standardized polynomial-like features and proper weakly informative priors yields regularized estimates, posterior uncertainty, and finite coefficients under complete or quasi-complete separation. We establish a mechanistic--surrogate concordance principle linking the full fitted-score gradient to the local gradient of $\log R_0$ under explicit regularity and tangency assumptions, allowing conditional interpretation of threshold directions. Across seven analytic-label benchmarks associated with ODE models, including a 15-compartment five-group SIR system, five-fold binary AUC was 0.999--1.000 and ordered-bracket accuracy was 0.951--0.998. REEBLRA complements rather than replaces mechanistic derivations, bridging simulation, machine learning, and reproduction-number analysis. An accompanying Shiny app supports implementation.

q-bio.QM↗

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests

Synthetic data hold substantial potential to address practical challenges in epidemiology due to restricted data access and privacy concerns. However, many current methods suffer from limited quality, high computational demands, and complexity for non-experts. Furthermore, common evaluation strategies for synthetic data often fail to directly reflect statistical utility and measure privacy risks sufficiently. Against this background, a critical underexplored question is whether synthetic data can reliably reproduce key findings from epidemiological research while preserving privacy. We propose adversarial random forests (ARF) as an efficient and convenient method for synthesizing tabular epidemiological data. To evaluate its performance, we replicated statistical analyses from six epidemiological publications covering blood pressure, anthropometry, myocardial infarction, accelerometry, loneliness, and diabetes, from the German National Cohort (NAKO Gesundheitsstudie), the Bremen STEMI Registry U45 Study, and the Guelph Family Health Study. We further assessed how dataset dimensionality and variable complexity affect the quality of synthetic data, and contextualized ARF's performance by comparison with commonly used tabular data synthesizers in terms of utility, privacy, generalization, and runtime. Across all replicated studies, results on ARF-generated synthetic data consistently aligned with original findings. Even for datasets with relatively low sample size-to-dimensionality ratios, replication outcomes closely matched the original results across descriptive and inferential analyses. Reduced dimensionality and variable complexity further enhanced synthesis quality. ARF demonstrated favourable performance regarding utility, privacy preservation, and generalization relative to other synthesizers and superior computational efficiency.

q-bio.QM↗