A Guide to Modeling Reaction-Diffusion of Molecules with the E-Cell System

Arjunan, Satya N.V.

doi:10.1007/978-1-4614-6157-9_4

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…3. The shortcut keys to control the visualizer are provided in the Spatiocyte guide [26]. For example, the right arrow key will advance the time forward whereas the left arrow key, backward.…”

Section: Display Simulation Resultsmentioning

confidence: 99%

Multi-algorithm particle simulations with Spatiocyte

Arjunan¹,

Takahashi²

2016

Preprint

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As quantitative biologists get more measurements of spatially regulated systems such as cell division and polarization, simulation of reaction and diffusion of proteins using the data is becoming increasingly relevant to uncover the mechanisms underlying the systems. Spatiocyte is a lattice-based stochastic particle simulator for biochemical reaction and diffusion processes.Simulations can be performed at single molecule and compartment spatial scales simultaneously.Molecules can diffuse and react in 1D (filament), 2D (membrane) and 3D (cytosol) compartments. The implications of crowded regions in the cell can be investigated because each diffusing molecule has spatial dimensions. Spatiocyte adopts multi-algorithm and multitimescale frameworks to simulate models that simultaneously employ deterministic, stochastic and particle reaction-diffusion algorithms. Comparison of light microscopy images to simulation snapshots is supported by Spatiocyte microscopy visualization and molecule tagging features.Spatiocyte is open-source software and is freely available at http://spatiocyte.org.

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Section: Display Simulation Resultsmentioning

confidence: 99%

Multi-algorithm particle simulations with Spatiocyte

Arjunan¹,

Takahashi²

2016

Preprint

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“…Each module has its own set of options that are defined in the model by the user. These options are described in the general guide to build Spatiocyte models [26]. Our multi-algorithm model consists of mass action, Spatiocyte next-reaction and lattice-based particle reactiondiffusion methods.…”

Section: Build a Simulation Modelmentioning

confidence: 99%

Multi-Algorithm Particle Simulations with Spatiocyte

Arjunan¹,

Takahashi²

2017

Methods in Molecular Biology

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As quantitative biologists get more measurements of spatially regulated systems such as cell division and polarization, simulation of reaction and diffusion of proteins using the data is becoming increasingly relevant to uncover the mechanisms underlying the systems. Spatiocyte is a lattice-based stochastic particle simulator for biochemical reaction and diffusion processes. Simulations can be performed at single molecule and compartment spatial scales simultaneously. Molecules can diffuse and react in 1D (filament), 2D (membrane), and 3D (cytosol) compartments. The implications of crowded regions in the cell can be investigated because each diffusing molecule has spatial dimensions. Spatiocyte adopts multi-algorithm and multi-timescale frameworks to simulate models that simultaneously employ deterministic, stochastic, and particle reaction-diffusion algorithms. Comparison of light microscopy images to simulation snapshots is supported by Spatiocyte microscopy visualization and molecule tagging features. Spatiocyte is open-source software and is freely available at http://spatiocyte.org .

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“…These methods enable formulating both compartmental and spatial models with either 2Dor 3D-geometries, theoretically-or experimentally-derived. Several modeler environments have been developed in order to simulate and incorporate cell-based models, to describe the interactions and dynamics of cell-bound variables and to define molecular species in reaction-diffusion systems [22][23][24][25][26][27][28][29][30][31][32].…”

mentioning

confidence: 99%

Harnessing the Potential of Stem Cells for Disease Modeling: Progress and Promises

Argentati

Tortorella

Bazzucchi

et al. 2020

JPM

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Ex vivo cell/tissue-based models are an essential step in the workflow of pathophysiology studies, assay development, disease modeling, drug discovery, and development of personalized therapeutic strategies. For these purposes, both scientific and pharmaceutical research have adopted ex vivo stem cell models because of their better predictive power. As matter of a fact, the advancing in isolation and in vitro expansion protocols for culturing autologous human stem cells, and the standardization of methods for generating patient-derived induced pluripotent stem cells has made feasible to generate and investigate human cellular disease models with even greater speed and efficiency. Furthermore, the potential of stem cells on generating more complex systems, such as scaffold-cell models, organoids, or organ-on-a-chip, allowed to overcome the limitations of the two-dimensional culture systems as well as to better mimic tissues structures and functions. Finally, the advent of genome-editing/gene therapy technologies had a great impact on the generation of more proficient stem cell-disease models and on establishing an effective therapeutic treatment. In this review, we discuss important breakthroughs of stem cell-based models highlighting current directions, advantages, and limitations and point out the need to combine experimental biology with computational tools able to describe complex biological systems and deliver results or predictions in the context of personalized medicine.Since their establishment, cell cultures have been proven to be the first and most powerful tool for the in vitro investigation of cell biology, from basic research to more complex translational approaches [6]. Classical two-dimensional (2D)-cultures usually consist of a unique type of cells (primary or continuous cultures) that grow in tissue culture polystyrene as adherent monolayers or in floating suspension, both in culture media that provide essential nutrients and growth factors. 2D-strategies are widely used to obtain a large number of cells, thus allowing the in vitro study of molecular mechanisms and development of metabolic assays [7-9]. However, due to their inability to recreate the in vivo complexity of the biological environment, they are not suitable for accurate disease modeling. These limitations have been overcome by the development of three-dimensional (3D)-cell cultures systems [10]. The rationale design of 3D-cell cultures is to harvest cells in microstructures that resemble tissues or organs shape and organization, thus allowing better cell-to-cell/cell-environment contacts and signaling crosstalk [11][12][13][14][15], essential events for tissues correct development and functioning [14,15]. The 3D-cell culture strategy is articulated in many different methods and techniques that can be grouped in scaffold-based or non-scaffold based platforms, each with particular advantages and disadvantages that make them useful for different applications [10,[16][17][18][19]. 3D-cell culture strategies are supported by the in silico...

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A Guide to Modeling Reaction-Diffusion of Molecules with the E-Cell System

Cited by 4 publications

References 9 publications

Multi-algorithm particle simulations with Spatiocyte

Multi-algorithm particle simulations with Spatiocyte

Multi-Algorithm Particle Simulations with Spatiocyte

Harnessing the Potential of Stem Cells for Disease Modeling: Progress and Promises

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