Incorporating domain growth into hybrid methods for reaction–diffusion systems

Smith, Cameron A.; Yates, Christian A.

doi:10.1098/rsif.2020.1047

Cited by 4 publications

(2 citation statements)

References 49 publications

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“…To ensure the positivity of concentrations, we also check that the subtraction is not applied to pixels with concentrations less than the subtracted value. With this approach, the increase in the area of the simulation domain leads to a decrease in the total concentration but not the total mass of the RD components, as it was also implemented in other studies [53]. Similarly, for retraction events, the total molecular mass of the components in the RD model from the removed pixels is evenly distributed across the whole new mask.…”

Section: A Computational Methods For Coupling Reaction-diffusion Syst...mentioning

confidence: 97%

Spatiotemporal coordination of Rac1 and Cdc42 at the whole cell level during cell ruffling

Hladyshau

Stoop

Kamada

et al. 2023

Preprint

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Rho-GTPases are central regulators within a complex signaling network that controls the cytoskeletal organization and cell movement. This network includes multiple GTPases, such as the most studied Rac1, Cdc42, and RhoA, and their numerous effectors that provide mutual regulation and feedback loops. Here we investigate the temporal and spatial relationship between Rac1 and Cdc42 during membrane ruffling using a simulation model which couples GTPase signaling with cell morphodynamics to capture the GTPase behavior observed with FRET-based biosensors. We show that membrane velocity is regulated by the kinetic rate of GTPase activation rather than the concentration of active GTPase. Our model captures both uniform and polarized ruffling. We also show that cell-type specific time delays between Rac1 and Cdc42 activation can be reproduced with a single signaling motif, in which the delay is controlled by feedback from Cdc42 to Rac1. The resolution of our simulation output matches those of the time-lapsed recordings of cell dynamics and GTPase activity. This approach allows us to validate simulation results with quantitative precision using the same pipeline for the analysis of simulated and experimental data.

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Section: A Computational Methods For Coupling Reaction-diffusion Syst...mentioning

confidence: 97%

Spatiotemporal coordination of Rac1 and Cdc42 at the whole cell level during cell ruffling

Hladyshau

Stoop

Kamada

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…To ensure the positivity of concentrations, we also check that the subtraction is not applied to pixels with concentrations less than the subtracted value. With this approach, the increase in the area of the simulation domain leads to a decrease in the total concentration but not the total mass of the RD components, as it was also implemented in other studies [ 69 ]. Similarly, for retraction events, the total molecular mass of the components in the RD model from the removed pixels is evenly distributed across the whole new mask.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Coordination of Rac1 and Cdc42 at the Whole Cell Level during Cell Ruffling

Hladyshau

Stoop

Kamada

et al. 2023

Cells

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Rho-GTPases are central regulators within a complex signaling network that controls cytoskeletal organization and cell movement. The network includes multiple GTPases, such as the most studied Rac1, Cdc42, and RhoA, along with their numerous effectors that provide mutual regulation through feedback loops. Here we investigate the temporal and spatial relationship between Rac1 and Cdc42 during membrane ruffling, using a simulation model that couples GTPase signaling with cell morphodynamics and captures the GTPase behavior observed with FRET-based biosensors. We show that membrane velocity is regulated by the kinetic rate of GTPase activation rather than the concentration of active GTPase. Our model captures both uniform and polarized ruffling. We also show that cell-type specific time delays between Rac1 and Cdc42 activation can be reproduced with a single signaling motif, in which the delay is controlled by feedback from Cdc42 to Rac1. The resolution of our simulation output matches those of time-lapsed recordings of cell dynamics and GTPase activity. Our data-driven modeling approach allows us to validate simulation results with quantitative precision using the same pipeline for the analysis of simulated and experimental data.

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Detailed balance for particle models of reversible reactions in bounded domains

Zhang

Isaacson

2022

The Journal of Chemical Physics

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In particle-based stochastic reaction-diffusion models, reaction rate and placement kernels are used to decide the probability per time a reaction can occur between reactant particles, and to decide where product particles should be placed. When choosing kernels to use in reversible reactions, a key constraint is to ensure that detailed balance of spatial reaction-fluxes holds at all points at equilibrium. In this work we formulate a general partial-integral differential equation model that encompasses several of the commonly used contact reactivity (e.g. Smoluchowski-Collins-Kimball) and volume reactivity (e.g. Doi) particle models. From these equations we derive a detailed balance condition for the reversible $\textrm{A} + \textrm{B} \leftrightarrows \textrm{C}$ reaction. In bounded domains with no-flux boundary conditions, when choosing unbinding kernels consistent with several commonly used binding kernels, we show that preserving detailed balance of spatial reaction-fluxes at all points requires spatially varying unbinding rate functions near the domain boundary. Brownian Dynamics simulation algorithms can realize such varying rates through ignoring domain boundaries during unbinding and rejecting unbinding events that result in product particles being placed outside the domain.

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Incorporating domain growth into hybrid methods for reaction–diffusion systems

Cited by 4 publications

References 49 publications

Spatiotemporal coordination of Rac1 and Cdc42 at the whole cell level during cell ruffling

Spatiotemporal coordination of Rac1 and Cdc42 at the whole cell level during cell ruffling

Spatiotemporal Coordination of Rac1 and Cdc42 at the Whole Cell Level during Cell Ruffling

Detailed balance for particle models of reversible reactions in bounded domains

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