Particle dynamics in fluids with random interactions

Shagolsem, Lenin S.; Rabin, Yitzhak

doi:10.1063/1.4949546

Cited by 20 publications

(26 citation statements)

References 46 publications

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“…This can arise when a particle move through a heterogeneous medium with fluctuating interactions or topology [41,42]. In this context it should be mentioned recently a molecular dynamics simulation has been performed with an all particles different (APD) system where each particle interact with another with a different potential [43].…”

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confidence: 99%

Tracer diffusion in a sea of polymers with binding zones: mobile vs. frozen traps

Samanta

Chakrabarti

2016

Soft Matter

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We use molecular dynamics simulations to investigate the tracer diffusion in a sea of polymers with specific binding zones for the tracer. These binding zones act as traps. Our simulations show that the tracer can undergo normal yet non-Gaussian diffusion under certain circumstances, e.g, when the polymers with traps are frozen in space and the volume fraction and the binding strength of the traps are moderate.In this case, as the tracer moves, it experiences a heterogeneous environment and exhibits confined continuous time random walk (CTRW) like motion resulting a nonGaussian behavior. Also the long time dynamics becomes subdiffusive as the number or the binding strength of the traps increases. However, if the polymers are mobile then the tracer dynamics is Gaussian but could be normal or subdiffusive depending on the number and the binding strength of the traps. In addition, with increasing binding strength and the number of the polymer traps, the probability of the tracer being trapped increases. On the other hand, removing the binding zones does not result trapping, even at comparatively high crowding. Our simulations also show that the trapping probability increases with the increasing size of the tracer and for a bigger tracer with the frozen polymer background the dynamics is only weakly nonGaussian but highly subdiffusive. Our observations are in the same spirit as found in many recent experiments on tracer diffusion in polymeric materials and questions the validity of Gaussian theory to describe diffusion in crowded environment in general.

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confidence: 99%

Tracer diffusion in a sea of polymers with binding zones: mobile vs. frozen traps

Samanta

Chakrabarti

2016

Soft Matter

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“…It has been recently pointed out that dynamic heterogeneities near the glass transition in size-polydisperse Lennard-Jones fluids are strikingly similar to critical fluctuations near a critical point [16]. While in our size-monodisperse AID systems such a glass transition is preempted by crystallization, there are indications of a glass transition with respect to NIO due to slowing down of the kinetics of particle rearrangement [8].…”

Section: Discussionmentioning

confidence: 78%

Identity ordering and metastable clusters in fluids with random interactions

Azizi

Rabin

2019

The Journal of Chemical Physics

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We use Langevin dynamics simulations to study dense two-dimensional systems of particles where all binary interactions are different (AID) in the sense that each interaction parameter is characterized by a randomly chosen number. We compare two systems that differ by the probability distributions from which the interaction parameters are drawn: uniform (U) and exponential (E). Both systems undergo neighborhood identity ordering (NIO) and form metastable clusters in the fluid phase near the liquid-solid transition but the effects are much stronger in E than in U systems. Possible implications of our results for the control of the structure of multicomponent alloys are discussed.

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“…Under this model, proteins do not form stable complexes anymore, but rather have a high number of weakly interacting partners. Indeed, simulation studies have shown that phase separation yielding structures of reduced dimensionality can occur under very minimal hypotheses, such as weak overall protein-protein attraction [ 51 , 52 ]. In this context, the traditional representation of protein-protein interaction networks as graphs and arrows becomes less informative as the protein network gets fully connected, and can be replaced by representations such as pairwise interaction matrices ( Figure 1d , right) [ 53 ].…”

Section: Weak Interactions In the Nucleusmentioning

confidence: 99%

Protein motion in the nucleus: from anomalous diffusion to weak interactions

Woringer

Darzacq

2018

Biochemical Society Transactions

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Understanding how transcription factors (TFs) regulate mammalian gene expression in space and time is a central topic in biology. To activate a gene, a TF has first to diffuse in the available space of the nucleus until it reaches a target DNA sequence or protein (target site). This eventually results in the recruitment of the whole transcriptional machinery. All these processes take place in the mammalian nucleoplasm, a highly organized and dynamic environment, in which some complexes transiently assemble and break apart, whereas others appear more stable. This diversity of dynamic behaviors arises from the number of biomolecules that make up the nucleoplasm and their pairwise interactions. Indeed, interactions energies that span several orders of magnitude, from covalent bounds to transient and dynamic interactions, can shape nuclear landscapes. Thus, the nuclear environment determines how frequently and how fast a TF contacts its target site, and it indirectly regulates gene expression. How exactly transient interactions are involved in the regulation of TF diffusion is unclear, but are reflected by live cell imaging techniques, including single-particle tracking (SPT). Overall, the macroscopic result of these microscopic interactions is almost always anomalous diffusion, a phenomenon widely studied and modeled. Here, we review the connections between the anomalous diffusion of a TF observed by SPT and the microscopic organization of the nucleus, including recently described topologically associated domains and dynamic phase-separated compartments. We propose that anomalous diffusion found in SPT data result from weak and transient interactions with dynamic nuclear substructures, and that SPT data analysis would benefit from a better description of such structures.

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Particle dynamics in fluids with random interactions

Cited by 20 publications

References 46 publications

Tracer diffusion in a sea of polymers with binding zones: mobile vs. frozen traps

Tracer diffusion in a sea of polymers with binding zones: mobile vs. frozen traps

Identity ordering and metastable clusters in fluids with random interactions

Protein motion in the nucleus: from anomalous diffusion to weak interactions

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