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Hundreds of genes interact with the yeast nuclear pore complex (NPC), localizing at the nuclear periphery and clustering with co-regulated genes. Dynamic tracking of peripheral genes shows that they cycle on and off the NPC and that interaction with the NPC slows their sub-diffusive movement. Furthermore, NPC-dependent inter-chromosomal clustering leads to coordinated movement of pairs of loci separated by hundreds of nanometers. We developed fractional Brownian motion simulations for chromosomal loci in the nucleoplasm and interacting with NPCs. These simulations predict the rate and nature of random sub-diffusion during repositioning from nucleoplasm to periphery and match measurements from two different experimental models, arguing that recruitment to the nuclear periphery is due to random sub-diffusion and transient capture by NPCs. Finally, the simulations do not lead to inter-chromosomal clustering or coordinated movement, suggesting that interaction with the NPC is necessary, but not sufficient, to cause clustering.
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http://dx.doi.org/10.7554/eLife.66238 | DOI Listing |
Chaos
June 2025
Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensée (UMR 7600), 4 Place Jussieu, 75252 Paris, Cedex 05, France.
We discuss how to construct reliably well "a lattice and an integer time" version of super-diffusive continuous-space and -time fractional Brownian motion (fBm)-an experimentally relevant non-Markovian Gaussian stochastic process with an everlasting power-law memory on the time-evolution of thermal noises extending over the entire past. We propose two algorithms, which are both validated by extensive numerical simulations showing that the ensuing lattice random walks have not only the same power-law covariance function as the standard fBm, but also individual trajectories follow those of the super-diffusive fBm. Finding a lattice and an integer time analog of sub-diffusion fBm, which is an anti-persistent process, remains a challenging open problem.
View Article and Find Full Text PDFFractional Brownian motion (fBm) exhibits both randomness and strong scale-free correlations, posing a challenge for generative artificial intelligence to replicate the underlying stochastic process. In this study, we evaluate the performance of diffusion-based inpainting methods on a specific dataset of corrupted images, which represent incomplete Euclidean distance matrices (EDMs) of fBm across various memory exponents (H). Our dataset reveals that, in the regime of low missing ratios, data imputation is unique, as the remaining partial graph is rigid, thus providing a reliable ground truth for inpainting.
View Article and Find Full Text PDFChaos
March 2025
School of Chemistry, Tel Aviv University, 6997801 Tel Aviv, Israel.
Recently introduced and explored, power Brownian motion (PBM) is a versatile generalization of Brownian motion: it is Markovian on the one hand and it displays a variety of anomalous-diffusion behaviors on the other hand. Brownian motion is the universal scaling-limit of finite-variance random walks. Shifting from the finite-variance realm to the infinite-variance realm, the counterpart of Brownian motion is Levy motion: the stable and symmetric Levy process.
View Article and Find Full Text PDFPhys Chem Chem Phys
March 2025
Post-Graduate and Research Center, Department of Chemistry, MES Abasaheb Garware College, Karve Road, Pune 411 004, India.
The transport of materials is of fundamental importance, with studies on diffusion being at the forefront. Diffusion in a simple matrix is typically considered Fickian. However, anomalous diffusion in various media is a dominant process.
View Article and Find Full Text PDFbioRxiv
February 2025
Department of Mathematics, Indiana University, Bloomington, IN, USA.
Glucose-stimulated insulin secretion (GSIS) in pancreatic cells is vital to metabolic homeostasis. Recent evidence has highlighted the critical role of the cells' microtubule (MT) cytoskeleton in regulating transport and availability of insulin containing vesicles. How these vesicles move within the cell and how that mobility is influenced by the MT network is however not well understood.
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