Spatial Stochastic Modeling with MCell and CellBlender

Gupta, Sanjana; Czech, Jacob; Kuczewski, Robert; Bartol, Thomas M.; Sejnowski, Terrence J.; Lee, Robin; Faeder, James R.

doi:10.48550/arxiv.1810.00499

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…The number of possible reactions of the CaMKII holoenzyme described below would be computationally intractable in other modeling platforms. A Python API was added to MCell4 enabling the model presented here by permitting customizations to the configurations of molecules and reactions that are not easily encoded in CellBlender, the graphical user interface for MCell (69).…”

Section: Model Developmentmentioning

confidence: 99%

A spatial model of autophosphorylation of Ca²⁺/calmodulin-dependent protein kinase II in a glutamatergic spine reveals dynamics of kinase activation in the first several seconds after a complex synaptic stimulus

Bartol,

Ordyan,

Sejnowski

et al. 2024

Preprint

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Activation of N-methyl-D-aspartate-type glutamate receptors (NMDARs) at synapses in the CNS triggers changes in synaptic strength that underlie memory formation in response to strong synaptic stimuli. The primary target of Ca2+ flowing through NMDARs is Ca2+/calmodulin dependent protein kinase II (CaMKII) which forms dodecameric holoenzymes that are highly concentrated at the postsynaptic site. Activation of CaMKII is necessary to trigger long-term potentiation of synaptic strength (LTP), and is prolonged by autophosphorylation of subunits within the holoenzyme. Here we use MCell4, an agent-based, stochastic, modeling platform to model CaMKII holoenzymes placed within a realistic spine geometry. We show how two mechanisms of regulation of CaMKII, 'Ca2+-calmodulin-trapping (CaM-trapping)' and dephosphorylation by protein phosphatase-1 (PP1) shape the autophosphorylation response during a repeated high-frequency stimulus. Our simulation results suggest that autophosphorylation of CaMKII does not constitute a bistable switch. Instead, prolonged but temporary, autophosphorylation of CaMKII may contribute to a biochemical-network-based 'kinetic proof-reading' mechanism that controls the induction of synaptic plasticity.

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Section: Model Developmentmentioning

confidence: 99%

A spatial model of autophosphorylation of Ca²⁺/calmodulin-dependent protein kinase II in a glutamatergic spine reveals dynamics of kinase activation in the first several seconds after a complex synaptic stimulus

Bartol,

Ordyan,

Sejnowski

et al. 2024

Preprint

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“…Among them, particle-based stochastic models form the main class of tracking models (2)(3)(4)(5) and they are often at the basis of single molecule localization microscopy (SMLM) simulators. (6)(7)(8)(9)(10) Popular softwares providing particle-based stochastic simulations include Virtual Cell, (11) MCell, (12) and Smoldyn, (13) but they are mainly dedicated to reaction-diffusion dynamics for specific biophysics applications. In particular, as mentioned in the review paper, (14) they are "also known as Brownian motion simulators" and as such they hardly represent the diversity of particle motions observed in some applications.…”

Section: Introductionmentioning

confidence: 99%

A generative model to simulate spatiotemporal dynamics of biomolecules in cells

Balsollier,

Lavancier,

Salamero

et al. 2023

Biol. Imaging

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Generators of space-time dynamics in bioimaging have become essential to build ground truth datasets for image processing algorithm evaluation such as biomolecule detectors and trackers, as well as to generate training datasets for deep learning algorithms. In this contribution, we leverage a stochastic model, called birth-death-move (BDM) point process, in order to generate joint dynamics of biomolecules in cells. This particle-based stochastic simulation method is very flexible and can be seen as a generalization of well-established standard particle-based generators. In comparison, our approach allows us: (1) to model a system of particles in motion, possibly in interaction, that can each possibly switch from a motion regime (e.g., Brownian) to another (e.g., a directed motion); (2) to take into account finely the appearance over time of new trajectories and their disappearance, these events possibly depending on the cell regions but also on the current spatial configuration of all existing particles. This flexibility enables to generate more realistic dynamics than standard particle-based simulation procedures, by for example accounting for the colocalization phenomena often observed between intracellular vesicles. We explain how to specify all characteristics of a BDM model, with many practical examples that are relevant for bioimaging applications. As an illustration, based on real fluorescence microscopy datasets, we finally calibrate our model to mimic the joint dynamics of Langerin and Rab11 proteins near the plasma membrane, including the well-known colocalization occurrence between these two types of vesicles. We show that the resulting synthetic sequences exhibit comparable features as those observed in real microscopy image sequences. Impact StatementThe paper presents agenerator of spatio-temporal dynamic for bio-imaging, called the birth-death-move (BDM) model. This stochastic model simulates particle dynamics, accounting for interactions and colocalization. We illustrate the high flexibility of this model by presenting results on real-word image series. Model calibration from real fluorescence microscopy data shows that it faithfully reproduces the joint dynamics of the Langerin and Rab11 proteins.

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Spatial Stochastic Modeling with MCell and CellBlender

Cited by 2 publications

References 31 publications

A spatial model of autophosphorylation of Ca²⁺/calmodulin-dependent protein kinase II in a glutamatergic spine reveals dynamics of kinase activation in the first several seconds after a complex synaptic stimulus

A spatial model of autophosphorylation of Ca²⁺/calmodulin-dependent protein kinase II in a glutamatergic spine reveals dynamics of kinase activation in the first several seconds after a complex synaptic stimulus

A generative model to simulate spatiotemporal dynamics of biomolecules in cells

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Spatial Stochastic Modeling with MCell and CellBlender

Cited by 2 publications

References 31 publications

A spatial model of autophosphorylation of Ca2+/calmodulin-dependent protein kinase II in a glutamatergic spine reveals dynamics of kinase activation in the first several seconds after a complex synaptic stimulus

A spatial model of autophosphorylation of Ca2+/calmodulin-dependent protein kinase II in a glutamatergic spine reveals dynamics of kinase activation in the first several seconds after a complex synaptic stimulus

A generative model to simulate spatiotemporal dynamics of biomolecules in cells

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A spatial model of autophosphorylation of Ca²⁺/calmodulin-dependent protein kinase II in a glutamatergic spine reveals dynamics of kinase activation in the first several seconds after a complex synaptic stimulus

A spatial model of autophosphorylation of Ca²⁺/calmodulin-dependent protein kinase II in a glutamatergic spine reveals dynamics of kinase activation in the first several seconds after a complex synaptic stimulus