arXiv subjects

Seongyu Park

Publications and source records attributed to Seongyu Park.

6 recordsLinked to original sources

arXiv2025

Single-Trajectory Bayesian Modeling Reveals Multi-State Diffusion of the MSH Sliding Clamp

DNA mismatch repair (MMR) is the essential mechanism for preserving genomic integrity in various living organisms. In this process, MutS homologs (MSH) play crucial roles in identifying mismatched basepairs and recruiting downstream MMR proteins. The MSH protein exhibits distinct functions and diffusion dynamics before and after the recognition of mismatches while traversing along DNA. An ADP-bound MSH, known as the MSH searching clamp, scans DNA sequences via rotational diffusion along the DNA backbone. Upon recognizing a mismatch, the MSH combines with ATP molecules, forming a stable sliding clamp. Recent experimental evidence challenges the conventional view that the sliding clamp performs a simple Brownian motion. In this study, we explore the diffusion dynamics of the ATP-bound MSH sliding clamp through single-particle tracking experiments and introduce a Bayesian single-trajectory modeling framework to analyze its motion. Our quantitative analysis reveals that the diffusion characteristics defy explanation by a single-state diffusion mechanism. Instead, our in-depth model inference uncovers three distinct diffusion states, each characterized by specific diffusion coefficients. These states alternate over time, with cross-state transitions predominantly involving one intermediate state, and direct transitions between the slowest and the fastest states being scarce. We propose that these multi-state dynamics reflect underlying conformational changes in the MSH sliding clamp, highlighting a more intricate diffusion mechanism than previously appreciated.

physics.bio-ph↗

arXiv2025

Objective comparison of methods to decode anomalous diffusion

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers.

physics.data-an↗

arXiv2025

Fickian yet non-Gaussian diffusion in an annealed heterogeneous environment

Fickian yet non-Gaussian diffusion is a ubiquitous phenomenon observed in various biological and soft matter systems. This anomalous dynamics is typically attributed to heterogeneous environments inducing spatiotemporal variations in the diffusivity of tracer particles. While previous studies have predominantly focused on systems exhibiting either spatial or temporal heterogeneity, this work bridges the gap by introducing a model based on an annealed extreme landscape to simultaneously account for both types of heterogeneities. Through a combination of computational analyses and analytical derivations, we investigate how the interplay of spatial and temporal heterogeneities in the energy landscape gives rise to Fickian yet non-Gaussian diffusion. Furthermore, we demonstrate that in the presence of temporal environmental fluctuations, the heterogeneous diffusion inevitably converges to classical Brownian motion via a homogenization process. We derive an analytical expression for the homogenization time as a function of key parameters governing the system's spatiotemporal heterogeneities. Additionally, we quantify particle-to-particle diffusion heterogeneity and examine the ergodic properties of this model, providing deeper insights into the dynamics of complex, heterogeneous systems.

cond-mat.stat-mech↗

arXiv2022

Bayesian inference of scaled versus fractional Brownian motion

We present a Bayesian inference scheme for scaled Brownian motion, and investigate its performance on synthetic data for parameter estimation and model selection in a combined inference with fractional Brownian motion. We include the possibility of measurement noise in both models. We find that for trajectories of a few hundred time points the procedure is able to resolve well the true model and parameters. Using the prior of the synthetic data generation process also for the inference, the approach is optimal based on decision theory. We include a comparison with inference using a prior different from the data generating one.

stat.ME↗

arXiv2021

Bayesian inference of Lévy walks via hidden Markov models

The Lévy walk is a non-Brownian random walk model that has been found to describe anomalous dynamic phenomena in diverse fields ranging from biology over quantum physics to ecology. Recurrently occurring problems are to examine whether observed data are successfully quantified by a model classified as Lévy walks or not and extract the best model parameters in accordance with the data. Motivated by such needs, we propose a hidden Markov model for Lévy walks and computationally realize and test the corresponding Bayesian inference method. We introduce a Markovian decomposition scheme to approximate a renewal process governed by a power-law waiting time distribution. Using this, we construct the likelihood function of Lévy walks based on a hidden Markov model and the forward algorithm. With the Lévy walk trajectories simulated at various conditions, we perform the Bayesian inference for parameter estimation and model classification. We show that the power-law exponent of the flight-time distribution can be successfully extracted even at the condition that the mean-squared displacement does not display the expected scaling exponent due to the noise or insufficient trajectory length. It is also demonstrated that the Bayesian method performs remarkably inferring the Lévy walk trajectories from given unclassified trajectory data set if the noise level is moderate.

physics.bio-ph↗

arXiv2020

A mini-review of the diffusion dynamics of DNA-binding proteins: Experiments and models

In the course of various biological processes, specific DNA-binding proteins must find a particular target sequence/protein or a damaged site on the DNA efficiently. DNA-binding proteins perform this task based on diffusion. Yet, investigations over recent decades have found that the diffusion dynamics of DNA-binding proteins are generally complicated and, further, protein-specific. In this review, we collect the experimental and theoretical studies that quantify the diffusion dynamics of DNA-binding proteins and review them from the viewpoint of diffusion processes.

physics.bio-ph↗