Paper List
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An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
This paper addresses the critical gap between theoretical AI research and real-world clinical implementation by providing a practical framework for as...
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The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
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Hierarchical Molecular Language Models (HMLMs)
This paper addresses the core challenge of accurately modeling context-dependent signaling, pathway cross-talk, and temporal dynamics across multiple ...
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Stability analysis of action potential generation using Markov models of voltage‑gated sodium channel isoforms
This work addresses the challenge of systematically characterizing how the high-dimensional parameter space of Markov models for different sodium chan...
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Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
This paper addresses the core challenge of inferring the relative contributions of multiple, simultaneous generative mechanisms in network formation w...
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EnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic Constants
This paper addresses the core challenge of jointly predicting enzyme kinetic parameters (Kcat and Km) by modeling dynamic enzyme-substrate interaction...
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Tissue stress measurements with Bayesian Inversion Stress Microscopy
This paper addresses the core challenge of measuring absolute, tissue-scale mechanical stress without making assumptions about tissue rheology, which ...
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DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
This study addresses the critical gap between high predictive accuracy and biological interpretability in DeepFRI, revealing that the model often prio...
Single-cell directional sensing at ultra-low chemoattractant concentrations from extreme first-passage events
University of Notre Dame | University of Utah
30秒速读
IN SHORT: This work addresses the core challenge of how a cell can rapidly and accurately determine the direction of a chemoattractant source when the signal is extremely weak (femto- to attomolar), and receptor binding events are discrete and rare.
核心创新
- Methodology Derives the first analytic expressions for the joint asymptotic distribution of the earliest k hitting times and their angular locations on a 2D circular cell, revealing that θ_k,N ~ N(θ_0, σ²_k,N) where σ²_k,N ∝ ( (R-1)² / (R W) ) * (1 + (2 log k)/(1+W) ) and W ~ log N.
- Theory Quantitatively demonstrates that early binding events (e.g., the first few arrivals) carry disproportionately more directional information than later arrivals, providing a theoretical basis for rapid cellular decision-making before a steady-state gradient is established.
- Methodology Proposes and rigorously analyzes the performance of several source-direction estimators (from simple averaging of early impact locations to more complex MLEs), deriving explicit formulas for their expected error and variance (e.g., E[ρ_k^res] ≈ (D/R)(b_N + a_N(log k - 1))).
主要结论
- The angular location θ_k of the k-th arriving molecule follows a normal distribution centered on the true source direction θ_0, with a variance that increases logarithmically with k (σ²_k,N ∝ log k), formally proving that earlier arrivals provide more precise directional cues.
- A simple estimator averaging the first k impact locations (n_res) can achieve accurate directional sensing with small k; its error grows with k while its variance decreases (Var[ρ_k^res] ≈ 4D²/(R²k)*((a_N log k + b_N - a_N)² + a_N²)), highlighting a trade-off.
- The theoretical framework successfully links key physical parameters (source distance R, initial molecule number N ~ 10³-10⁶, number of observed events k) to sensing performance, showing that accurate directional inference is possible even for R > 1 (source placed multiple cell radii away).
摘要: We investigate single-cell directional sensing from diffusing chemoattractant signals released by a localized source. We focus on the low-concentration regime in which receptor activity is discrete and cellular decisions are made on timescales far shorter than those required for steady-state concentration profiles or receptor occupancy to emerge. We derive analytic expressions for the joint distribution of receptor binding times and binding locations, conditional on the position of the source. We show that early binding events carry disproportionately more information about source directionality than later arrivals. Motivated by this observation, we propose and analyze several source-localization estimates that exploit early receptor binding statistics. Our results demonstrate that, even with a small number of binding events, cells possess sufficient information to rapidly and accurately infer the directionality of a diffusing chemoattractant source.