Protein Diffusion in Mammalian Cell Cytoplasm

Kühn, Thomas; Ihalainen, Teemu O.; Hyväluoma, Jari; Dross, Nicolas; Willman, Sami F.; Langowski, Jörg; Vihinen‐Ranta, Maija; Timonen, J.

doi:10.1371/journal.pone.0022962

Cited by 162 publications

(167 citation statements)

References 35 publications

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“…The average diffusion coefficients that we obtained in the nucleus and cytoplasm are close to what has been found in previous studies for EYFP and structurally very similar GFP [12,33,34]. The average diffusion coefficients are about 3-6 times less in the cellular environment than in water [34].…”

Section: Discussionsupporting

confidence: 89%

“…This is also the approach taken in Ref. [12] to model the nuclear envelope. However, if the membrane restricts the transport strongly, relaxation time at the membrane sites can be inconveniently small (with respect to the numerical stability [13]), or too large in the surrounding environment (with respect to the numerical error [11]).…”

Section: Lattice-boltzmann Modeling Of Diffusion Across a Membranementioning

confidence: 93%

“…Even though we imaged the photobleaching process in one confocal plane, the laser beam causes the bleached profile to extend also in the direction perpendicular to the imaged plane. We assumed that the intensity profile of the laser was constant in the z direction [12] and calculated a fractional loss of intensity, γ (x,y), such that for the before-and after-bleach intensities the relation I after = γ I before held. The whole-cell initial concentration distribution for the simulation, I sim (r,t = 0), was then set by multiplying the intensities of the imaged equilibrium concentration of EYFP in every layer with γ (x,y) (extrapolation of the profile in the z direction).…”

Section: B Simulation Setupmentioning

confidence: 99%

“…Besides the volume exclusion effect mentioned above, the cellular structures also reduce the mobility of molecules in their vicinity [12]. Therefore we had to scale the diffusion coefficients in the cell based on the amount of volume exclusion in the imaged equilibrium distribution.…”

Section: B Simulation Setupmentioning

confidence: 99%

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Diffusion through thin membranes: Modeling across scales

et al. 2016

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From macroscopic to microscopic scales it is demonstrated that diffusion through membranes can be modeled using specific boundary conditions across them. The membranes are here considered thin in comparison to the overall size of the system. In a macroscopic scale the membrane is introduced as a transmission boundary condition, which enables an effective modeling of systems that involve multiple scales. In a mesoscopic scale, a numerical lattice-Boltzmann scheme with a partial-bounceback condition at the membrane is proposed and analyzed. It is shown that this mesoscopic approach provides a consistent approximation of the transmission boundary condition. Furthermore, analysis of the mesoscopic scheme gives rise to an expression for the permeability of a thin membrane as a function of a mesoscopic transmission parameter. In a microscopic model, the mean waiting time for a passage of a particle through the membrane is in accordance with this permeability. Numerical results computed with the mesoscopic scheme are then compared successfully with analytical solutions derived in a macroscopic scale, and the membrane model introduced here is used to simulate diffusive transport between the cell nucleus and cytoplasm through the nuclear envelope in a realistic cell model based on fluorescence microscopy data. By comparing the simulated fluorophore transport to the experimental one, we determine the permeability of the nuclear envelope of HeLa cells to enhanced yellow fluorescent protein.

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Section: Discussionsupporting

confidence: 89%

Section: Lattice-boltzmann Modeling Of Diffusion Across a Membranementioning

confidence: 93%

Section: B Simulation Setupmentioning

confidence: 99%

Section: B Simulation Setupmentioning

confidence: 99%

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Diffusion through thin membranes: Modeling across scales

et al. 2016

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“…(19) is presented in Appendix A. The PDF (20) can be normalized and represents an acceptable solution only when −…”

Section: External Force That Does Not Limit the Anomalous Diffusionmentioning

confidence: 99%

Influence of external potentials on heterogeneous diffusion processes

Kazakevičius

Ruseckas

2017

2017 International Conference on Noise and Fluctuations (ICNF)

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In this paper we consider heterogeneous diffusion processes with the power-law dependence of the diffusion coefficient on the position and investigate the influence of external forces on the resulting anomalous diffusion. The heterogeneous diffusion processes can yield subdiffusion as well as superdiffusion, depending on the behavior of the diffusion coefficient. We assume that not only the diffusion coefficient but also the external force has a power-law dependence on the position. We obtain analytic expressions for the transition probability in two cases: when the power-law exponent in the external force is equal to 2η − 1, where 2η is the power-law exponent in the dependence of the diffusion coefficient on the position, and when the external force has a linear dependence on the position. We found that the power-law exponent in the dependence of the mean square displacement on time does not depend on the external force, this force changes only the anomalous diffusion coefficient. In addition, the external force having the power-law exponent different from 2η − 1 limits the time interval where the anomalous diffusion occurs. We expect that the results obtained in this paper may be relevant for a more complete understanding of anomalous diffusion processes.

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Impact of In‐Cell and In‐Vitro Crowding on the Conformations and Dynamics of an Intrinsically Disordered Protein

et al. 2021

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The conformations and dynamics of proteins can be influenced by crowding from the large concentrations of macromolecules within cells. Intrinsically disordered proteins (IDPs) exhibit chain compaction in crowded solutions in vitro, but no such effects were observed in cultured mammalian cells. Here, to increase intracellular crowding, we reduced the cell volume by hyperosmotic stress and used an IDP as a crowding sensor for in‐cell single‐molecule spectroscopy. In these more crowded cells, the IDP exhibits compaction, slower chain dynamics, and much slower translational diffusion, indicating a pronounced concentration and length‐scale dependence of crowding. In vitro, these effects cannot be reproduced with small but only with large polymeric crowders. The observations can be explained with polymer theory and depletion interactions and indicate that IDPs can diffuse much more efficiently through a crowded cytosol than a globular protein of similar dimensions.

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Protein Diffusion in Mammalian Cell Cytoplasm

Cited by 162 publications

References 35 publications

Diffusion through thin membranes: Modeling across scales

Diffusion through thin membranes: Modeling across scales

Influence of external potentials on heterogeneous diffusion processes

Impact of In‐Cell and In‐Vitro Crowding on the Conformations and Dynamics of an Intrinsically Disordered Protein

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