The Experts below are selected from a list of 34842 Experts worldwide ranked by ideXlab platform
David Holcman - One of the best experts on this subject based on the ideXlab platform.
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advances using single Particle Trajectories to reconstruct chromatin organization and dynamics
Trends in Genetics, 2019Co-Authors: Ofir Shukron, Andrew Seeber, Assaf Amitai, David HolcmanAbstract:Chromatin organization remains complex and far from understood. In this article, we review recent statistical methods of extracting biophysical parameters from in vivo single-Particle Trajectories of loci to reconstruct chromatin reorganization in response to cellular stress such as DNA damage. We look at methods for analyzing both single locus and multiple loci tracked simultaneously and explain how to quantify and describe chromatin motion using a combination of extractable parameters. These parameters can be converted into information about chromatin dynamics and function. Furthermore, we discuss how the timescale of recurrent encounter between loci can be extracted and interpreted. We also discuss the effect of sampling rate on the estimated parameters. Finally, we review a polymer method to reconstruct chromatin structure using crosslinkers between chromatin sites. We list and refer to some software packages that are now publicly available to simulate polymer motion. To conclude, chromatin organization and dynamics can be reconstructed from locus Trajectories and predicted based on polymer models.
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reconstructing wells from high density regions extracted from super resolution single Particle Trajectories
bioRxiv, 2019Co-Authors: Pierre Parutto, Jennifer Heck, Martin Heine, David HolcmanAbstract:Abstract Large amount of super-resolution single Particle Trajectories has revealed that the cellular environment is enriched in heterogenous regions of high density, which remain unexplained. The biophysical properties of these regions are characterized by a drift and their extension (a basin of attraction) that can be estimated from an ensemble of Trajectories. We develop here two statistical methods to recover the dynamics and local potential wells (field of force and boundary) using as a model a truncated Ornstein-Ulhenbeck process. The first method uses the empirical distribution of points, which differs inside and outside the potential well, while the second focuses on recovering the drift field. Finally, we apply these two methods to voltage-gated calcium channels and phospholipids moving on the surface of neuronal cells and recover the energy and size of these high-density regions with nanometer precision.
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single Particle Trajectories reveal active endoplasmic reticulum luminal flow
Nature Cell Biology, 2018Co-Authors: David Holcman, Pierre Parutto, Joseph E Chambers, Marcus Fantham, Laurence J Young, Stefan J Marciniak, Clemens F Kaminski, David Ron, Edward AvezovAbstract:The endoplasmic reticulum (ER), a network of membranous sheets and pipes, supports functions encompassing biogenesis of secretory proteins and delivery of functional solutes throughout the cell1,2. Molecular mobility through the ER network enables these functionalities, but diffusion alone is not sufficient to explain luminal transport across supramicrometre distances. Understanding the ER structure–function relationship is critical in light of mutations in ER morphology-regulating proteins that give rise to neurodegenerative disorders3,4. Here, super-resolution microscopy and analysis of single Particle Trajectories of ER luminal proteins revealed that the topological organization of the ER correlates with distinct trafficking modes of its luminal content: with a dominant diffusive component in tubular junctions and a fast flow component in tubules. Particle trajectory orientations resolved over time revealed an alternating current of the ER contents, while fast ER super-resolution identified energy-dependent tubule contraction events at specific points as a plausible mechanism for generating active ER luminal flow. The discovery of active flow in the ER has implications for timely ER content distribution throughout the cell, particularly important for cells with extensive ER-containing projections such as neurons. Using super-resolution microscopy, tracking of single Particle Trajectories of endoplasmic reticulum (ER) luminal proteins traversing tubular ER, and measuring ER dynamics, Holcman et al. show that ER content is propelled by active luminal flow.
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two loci single Particle Trajectories analysis constructing a first passage time statistics of local chromatin exploration
Scientific Reports, 2017Co-Authors: Ofir Shukron, David Holcman, Michael H HauerAbstract:Stochastic single Particle Trajectories are used to explore the local chromatin organization. We present here a statistical analysis of the first contact time distributions between two tagged loci recorded experimentally. First, we extract the association and dissociation times from data for various genomic distances between loci, and we show that the looping time occurs in confined nanometer regions. Second, we characterize the looping time distribution for two loci in the presence of multiple DNA damages. Finally, we construct a polymer model, that accounts for the local chromatin organization before and after a double-stranded DNA break (DSB), to estimate the level of chromatin decompaction. This novel passage time statistics method allows extracting transient dynamic at scales varying from one to few hundreds of nanometers, it predicts the local changes in the number of binding molecules following DSB and can be used to characterize the local dynamic of the chromatin.
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two loci single Particle Trajectories analysis constructing a first passage time statistics of local chromatin exploration
bioRxiv, 2017Co-Authors: Ofir Shukron, David HolcmanAbstract:Stochastic single Particle Trajectories are used to explore the local chromatin organization. We present here a statistical analysis of the first contact time distributions between two tagged loci recorded experimentally. First, we extract the association and dissociation times from data for various genomic distances between loci and we show that the looping time occurs in confined nanometer regions. Second, we characterize the looping time distribution for two loci in the presence of multiple DNA damages. Finally, we construct a polymer model that accounts for the local chromatin organization before and after a double-stranded DNA break (DSB) to estimate the level of chromatin decompaction. This novel passage time statistics method allows extracting transient dynamic at scales from one to few hundreds of nanometers, predicts the local changes in the number of binding molecules following DSB and can be used to better characterize the local dynamic of the chromatin.
Uriel Frisch - One of the best experts on this subject based on the ideXlab platform.
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time analyticity of lagrangian Particle Trajectories in ideal fluid flow
Journal of Fluid Mechanics, 2014Co-Authors: Vladislav Zheligovsky, Uriel FrischAbstract:It is known that the Eulerian and Lagrangian structures of fluid flow can be drastically different; for example, ideal fluid flow can have a trivial (static) Eulerian structure, while displaying chaotic streamlines. Here, we show that ideal flow with limited spatial smoothness (an initial vorticity that is just a little better than continuous) nevertheless has time-analytic Lagrangian Trajectories before the initial limited smoothness is lost. To prove these results we use a little-known Lagrangian formulation of ideal fluid flow derived by Cauchy in 1815 in a manuscript submitted for a prize of the French Academy. This formulation leads to simple recurrence relations among the time-Taylor coefficients of the Lagrangian map from initial to current fluid Particle positions; the coefficients can then be bounded using elementary methods. We first consider various classes of incompressible fluid flow, governed by the Euler equations, and then turn to highly compressible flow, governed by the Euler–Poisson equations, a case of cosmological relevance. The recurrence relations associated with the Lagrangian formulation of these incompressible and compressible problems are so closely related that the proofs of time-analyticity are basically identical.
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Time-analyticity of Lagrangian Particle Trajectories in ideal fluid flow
Journal of Fluid Mechanics, 2014Co-Authors: Vladislav Zheligovsky, Uriel FrischAbstract:It is known that the Eulerian and Lagrangian structures of fluid flow can be drastically different; for example, ideal fluid flow can have a trivial (static) Eulerian structure, while displaying chaotic streamlines. Here we show that ideal flow with limited spatial smoothness (an initial vorticity that is just a little better than continuous), nevertheless has time-analytic Lagrangian Trajectories before the initial limited smoothness is lost. For proving such results we use a little-known Lagrangian formulation of ideal fluid flow derived by Cauchy in 1815 in a manuscript submitted for a prize of the French Academy. This formulation leads to simple recurrence relations among the time-Taylor coefficients of the Lagrangian map from initial to current fluid Particle positions; the coefficients can then be bounded using elementary methods. We first consider various classes of incompressible fluid flow, governed by the Euler equations, and then turn to a case of compressible flow of cosmological relevance, governed by the Euler-Poisson equations.
Ofir Shukron - One of the best experts on this subject based on the ideXlab platform.
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advances using single Particle Trajectories to reconstruct chromatin organization and dynamics
Trends in Genetics, 2019Co-Authors: Ofir Shukron, Andrew Seeber, Assaf Amitai, David HolcmanAbstract:Chromatin organization remains complex and far from understood. In this article, we review recent statistical methods of extracting biophysical parameters from in vivo single-Particle Trajectories of loci to reconstruct chromatin reorganization in response to cellular stress such as DNA damage. We look at methods for analyzing both single locus and multiple loci tracked simultaneously and explain how to quantify and describe chromatin motion using a combination of extractable parameters. These parameters can be converted into information about chromatin dynamics and function. Furthermore, we discuss how the timescale of recurrent encounter between loci can be extracted and interpreted. We also discuss the effect of sampling rate on the estimated parameters. Finally, we review a polymer method to reconstruct chromatin structure using crosslinkers between chromatin sites. We list and refer to some software packages that are now publicly available to simulate polymer motion. To conclude, chromatin organization and dynamics can be reconstructed from locus Trajectories and predicted based on polymer models.
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two loci single Particle Trajectories analysis constructing a first passage time statistics of local chromatin exploration
Scientific Reports, 2017Co-Authors: Ofir Shukron, David Holcman, Michael H HauerAbstract:Stochastic single Particle Trajectories are used to explore the local chromatin organization. We present here a statistical analysis of the first contact time distributions between two tagged loci recorded experimentally. First, we extract the association and dissociation times from data for various genomic distances between loci, and we show that the looping time occurs in confined nanometer regions. Second, we characterize the looping time distribution for two loci in the presence of multiple DNA damages. Finally, we construct a polymer model, that accounts for the local chromatin organization before and after a double-stranded DNA break (DSB), to estimate the level of chromatin decompaction. This novel passage time statistics method allows extracting transient dynamic at scales varying from one to few hundreds of nanometers, it predicts the local changes in the number of binding molecules following DSB and can be used to characterize the local dynamic of the chromatin.
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two loci single Particle Trajectories analysis constructing a first passage time statistics of local chromatin exploration
bioRxiv, 2017Co-Authors: Ofir Shukron, David HolcmanAbstract:Stochastic single Particle Trajectories are used to explore the local chromatin organization. We present here a statistical analysis of the first contact time distributions between two tagged loci recorded experimentally. First, we extract the association and dissociation times from data for various genomic distances between loci and we show that the looping time occurs in confined nanometer regions. Second, we characterize the looping time distribution for two loci in the presence of multiple DNA damages. Finally, we construct a polymer model that accounts for the local chromatin organization before and after a double-stranded DNA break (DSB) to estimate the level of chromatin decompaction. This novel passage time statistics method allows extracting transient dynamic at scales from one to few hundreds of nanometers, predicts the local changes in the number of binding molecules following DSB and can be used to better characterize the local dynamic of the chromatin.
Adrian Constantin - One of the best experts on this subject based on the ideXlab platform.
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Particle Trajectories in linear water waves
Journal of Mathematical Fluid Mechanics, 2008Co-Authors: Adrian Constantin, Gabriele VillariAbstract:We prove that in linear periodic gravity water waves there are no closed orbits for the water Particles in the fluid. Each Particle experiences per period a backward-forward motion that leads overall to a forward drift.
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Particle Trajectories in linear deep-water waves
Nonlinear Analysis: Real World Applications, 2008Co-Authors: Adrian Constantin, Mats Ehrnström, Gabriele VillariAbstract:Using phase plane analysis we show that within the framework of linear water wave theory the Particle paths in a deep-water wave are not closed: there is a forward drift over a period, which decreases with greater depth.
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Particle Trajectories in Linear Water Waves
2007Co-Authors: Adrian Constantin, Gabriele VillariAbstract:We prove that in a linear periodic gravity water waves there are no closed orbit for the water Particles in the fluid. Each Particle experiences per period a backward-forward motion that leads to a forward drift. This result can be viewed as a more detailed version of the classical theory for the Trajectories of Particles below a water wave, in which, due to the first order linearization, all Particle paths appear to be closed. [ DOI : 10.1685 / CSC06058] About DOI
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Particle Trajectories in solitary water waves
Bulletin of the American Mathematical Society, 2007Co-Authors: Adrian Constantin, Joachim EscherAbstract:Analyzing a free boundary problem for harmonic functions in an infinite planar domain, we prove that in a solitary water wave each Particle is transported in the wave direction but slower than the wave speed. As the solitary wave propagates, all Particles located ahead of the wave crest are lifted, while those behind it experience a downward motion, with the Particle trajectory having asymptotically the same height above the flat bed.
Nilah Monnier - One of the best experts on this subject based on the ideXlab platform.
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bayesian inference to discriminate motion models from Particle Trajectories
Biophysical Journal, 2013Co-Authors: Nilah Monnier, Syuanming Guo, Arkajit Dey, Mark BatheAbstract:Quantitative analysis of Particle motion from Particle tracking datasets--such as cell Trajectories during embryonic development, receptor dynamics in cell membranes, and chromosome and kinetochore motions during spindle assembly--is a powerful approach to revealing the mechanism of transport in biological systems. However, inferring motion models from single-Particle Trajectories (SPTs) is non-trivial due to noise from both sampling limitations and heterogeneity in biological samples. We present two complementary approaches based on Bayesian inference to perform objective and automated analysis of SPTs. The first is a multiple hypothesis testing approach to determine the most likely mode of motion from mean-square displacement (MSD) curves derived from Particle Trajectories. This approach handles a large set of competing motion models--including diffusion, anomalous diffusion, confined diffusion, and directed motion--and determines which model is most justified by the evidence present in the available MSD curves. Because noise in MSD curves is highly correlated, we find that explicitly modeling the noise covariance matrix using multiple independent curves is essential for accurately determining model probabilities. The second approach fits raw Particle Trajectories with a Hidden Markov Model (HMM) to determine the most likely diffusion coefficient and velocity at each step along a trajectory, enabling the identification of transient motion states and dynamic transitions between motion models. These methods avoid overfitting by using an objective Bayesian framework to penalize model complexity and account for noise. These automated methods naturally scale to large numbers of Particle Trajectories, making them ideal for classifying motion in high-throughput screens of SPTs.
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bayesian approach to msd based analysis of Particle motion in live cells
Biophysical Journal, 2012Co-Authors: Nilah Monnier, Syuanming Guo, Masashi Mori, Peter Lenart, Mark BatheAbstract:Quantitative tracking of Particle motion using live-cell imaging is a powerful approach to understanding the mechanism of transport of biological molecules, organelles, and cells. However, inferring complex stochastic motion models from single-Particle Trajectories in an objective manner is nontrivial due to noise from sampling limitations and biological heterogeneity. Here, we present a systematic Bayesian approach to multiple-hypothesis testing of a general set of competing motion models based on Particle mean-square displacements that automatically classifies Particle motion, properly accounting for sampling limitations and correlated noise while appropriately penalizing model complexity according to Occam's Razor to avoid over-fitting. We test the procedure rigorously using simulated Trajectories for which the underlying physical process is known, demonstrating that it chooses the simplest physical model that explains the observed data. Further, we show that computed model probabilities provide a reliability test for the downstream biological interpretation of associated parameter values. We subsequently illustrate the broad utility of the approach by applying it to disparate biological systems including experimental Particle Trajectories from chromosomes, kinetochores, and membrane receptors undergoing a variety of complex motions. This automated and objective Bayesian framework easily scales to large numbers of Particle Trajectories, making it ideal for classifying the complex motion of large numbers of single molecules and cells from high-throughput screens, as well as single-cell-, tissue-, and organism-level studies.
