Diffusive lensing as a mechanism of intracellular transport and compartmentalization
- Department of Biochemistry, Stanford University, United States
- Department of Biological Sciences, Indian Institute of Science Education and Research (IISER) Mohali, India
- Department of Physics, University of California, Santa Barbara, United States
Figures
Figure 1 with 1 supplement
Low diffusivity leads to accumulation of particles.
(A) Particle distribution at various timesteps of a simulation with a step-like lower-diffusivity region. (B) Particle distribution at various timesteps for a simulation with a diffusivity gradient. …
Figure 1—figure supplement 1
Itô convention leads to Fokker-Planck diffusion, contrasting canonical (‘Fickian’) homogenization.
(A) Agent-based modeling of particle dynamics was used in this study. Choosing the diffusivity at the start point of a particle hop is in line with the Itô interpretation. (B) Numerical solutions …
Figure 2 with 1 supplement
Interaction-driven clustering is modulated by heterogenous diffusivity.
(A) Progress of a simulation comprising particles possessing weak interactions ( is the interaction strength; see Methods), initialized with a uniform concentration of particles. (B) Progress of a …
Figure 2—figure supplement 1
Particle clustering at different strengths in homogeneous versus heterogeneous diffusivity environments.
(A) Progress of a simulation comprising particles possessing weak interactions (), initialized with a uniform concentration of particles; no diffusivity gradient used here. (B) Progress of a …
Figure 3 with 1 supplement
Heterogeneous diffusion alters bulk particle motion as measured by in silico microrheology.
(A) mean squared displacement (MSD) versus time for homogeneous diffusion of 10,000 particles in a 5 mm × 5 mm simulation region. (B) Same as (A) for homogeneous diffusion in a more tightly bounded …
Figure 3—figure supplement 1
Magnitude and distribution of inhomogeneity in diffusivity affects diffusive lensing.
(A) Analysis of simulation trajectories via in silico microrheology. (B) Increasing diffusiophoretic extent due to variation of the zone diffusivity in a chamber comprising a low-diffusive end. (C) …
Figure 4 with 1 supplement
A decrease in granule diffusivity, an increase in granule radius, or packing density slows down mesoscale dynamics.
(A) Simulated fluorescence recovery after photobleaching (FRAP) t1/2 as a function of granule:bulk diffusivity ratio (, ). (B) Simulated FRAP as a function of granule radius (, ). (C) …
Figure 4—figure supplement 1
Dwell times for particles in low-diffusive granules dictate fluorescence recovery after photobleaching (FRAP) kinetics.
(A) The in silico implementation of FRAP was used in this study. (B) Methodology for determining mean dwell time of particles in low-diffusive granules, from a set of simulation trajectories. (C) …
Tables
Appendix 1—table 1
Converting between stochastic integration conventions.
 |  |  |
| Active noise/memory effects/other sources of nonequilibrium behaviors | Nonequilibrium in case of Itô and Stratonovich, equilibrium in case of isothermal | Consistent with equilibrium; thermal noise dominates |
| Diffusive lensing occurs | Diffusive lensing is seen in the Itô and Stratonovich cases (Figure 1—figure supplement 1B) | Diffusive lensing does not occur |
