pocketz.doi.bio/2025-08-23_145130_simulariumallencellorg_simularium

Simularium

URL: https://simularium.allencell.org/

Saved: 2025-08-23T21:51:30.430Z

| SIMULARIUM HOME Download Share Load model Resources Simularium Share, visualize, & interrogate biological simulations online

The Simularium Viewer makes it easy to share and interrogate interactive 3D visualizations of biological simulation trajectories and related plots directly in a web browser. Its primary goal is to facilitate collaborations among experimental and computational biologists by removing major challenges to sharing, accessing, and comparing simulation results.

Try Simularium now View example simulations or load your own data

With this first release we present the Simularium Viewer, an online visual analysis tool, which is one component of the larger Simularium platform. The Simularium Viewer provides an online application for visualizing simulation trajectories and related plots.

v1.0 0s

Blood Plasma

Graham T Johnson, Ludovic Autin, Mostafa Al-Alusi, David S Goodsell, Michel F Sanner & Arthur J Olson

cellPACK: a virtual mesoscope to model and visualize structural systems biology. Nature Methods (2015)

A cellPACK model of a 100 nm × 100 nm × 15 nm volume of human blood plasma based on a mesoscale recipe containing information for the most abundant macromolecules.

Software used to generate this data is available here.

Please note that the use of this software is subject to third party licensing requirements, which are currently available here.

0.4s

Condensate Formation: Above Ksp

Aniruddha Chattaraj et al.

The solubility product extends the buffering concept to heterotypic biomolecular condensates. eLife (2021)

A SpringSaLaD model of liquid-liquid phase separation above Ksp where condensate forms.

Software used to generate this data is available here.

Load your data

Load your own data into the viewer. Learn how to convert your data here.

0.4s

Condensate Formation: At Ksp

Aniruddha Chattaraj et al.

The solubility product extends the buffering concept to heterotypic biomolecular condensates. eLife (2021)

A SpringSaLaD model of liquid-liquid phase separation at Ksp where condensate forms.

Software used to generate this data is available here.

Condensate Formation: Below Ksp

Aniruddha Chattaraj et al.

The solubility product extends the buffering concept to heterotypic biomolecular condensates. eLife (2021)

A SpringSaLaD model of liquid-liquid phase separation below Ksp where no condensate forms.

Software used to generate this data is available here.

33min

Actin Bundle Dynamics with α–Actinin and Myosin: High Myosin Activity

Chandrasekaran et al.

Remarkable structural transformations of actin bundles are driven by their initial polarity, motor activity, crosslinking, and filament treadmilling. PLoS Computational Biology (2019)

A MEDYAN model of interacting actin filaments, α–actinin crosslinkers, and myosin motors. High myosin activity causes formation of an aster-like structure.

Software used to generate this data is available here. The outputs that were visualized can be downloaded here.

Please note that the use of this software is subject to third party licensing requirements, which are currently available here.

33min

Actin Bundle Dynamics with α–Actinin and Myosin: Low Myosin Activity

Chandrasekaran et al.

Remarkable structural transformations of actin bundles are driven by their initial polarity, motor activity, crosslinking, and filament treadmilling. PLoS Computational Biology (2019)

A MEDYAN model of interacting actin filaments, α–actinin crosslinkers, and myosin motors. Low myosin activity maintains the bundle structure.

Software used to generate this data is available here. The outputs that were visualized can be downloaded here.

Please note that the use of this software is subject to third party licensing requirements, which are currently available here.

1.5s

Sequestration of CaMKII in dendritic spines

Shahid Khan et al.

Sequestration of CaMKII in dendritic spines in silico. J Comp Neuro (2011)

A Smoldyn model of a dendritic spine with CaMKII and molecules of the postsynaptic density at the spine tip.

Software used to generate this data is available here. The input data file is here.

Please note that the use of this software is subject to third party licensing requirements, which are currently available here.

100s

Spatiotemporal oscillations in the E. coli Min system

Steve Andrews et al.

Detailed Simulations of Cell Biology with Smoldyn 2.1. PLoS Computational Biology (2010)

A Smoldyn example model of the E. coli Min system, which is used to find the cell center during cell division.

Software used to generate this data is available here. The input data file is here.

Please note that the use of this software is subject to third party licensing requirements, which are currently available here.

5.48ms

Membrane Wrapping a Nanoparticle

Mohsen Sadeghi et al.

Particle-based membrane model for mesoscopic simulation of cellular dynamics. J Chem Phys (2018)

A model of a coarse-grained particle-based membrane wrapping a nanoparticle.

v4.1 24h

SARS-CoV-2 Dynamics in Human Lung Epithelium

Michael Getz et al.

Rapid community-driven development of a SARS-CoV-2 tissue simulator. bioRxiv (2020)

A PhysiCell model of SARS-CoV-2 dynamics in human lung epithelium.

Software used to generate this data is available here.

Please note that the use of this software is subject to third party licensing requirements, which are currently available here.

15s

Actin in Clathrin-mediated Endocytosis

Matthew Akamatsu et al.

Principles of self-organization and load adaptation by the actin cytoskeleton during clathrin-mediated endocytosis. eLife (2020)

A CytoSim model of a branched actin network internalizing an endocytic pit against membrane tension.

Software used to generate this data is available here.

Please note that the use of this software is subject to third party licensing requirements, which are currently available here.

Click on any of the examples above to interact with example trajectories from various types of previously published simulations.

Simularium v1.0 allows you to interact with precalculated simulation results — including your own results converted into the Simularium file format (JSON). The Simularium Viewer uses advanced rendering techniques developed by Le Muzic et al. (2015) and Mindek et al. (2017) to enable meaningful interpretation of spatial relationships among thousands of moving components. Users can import a trajectory file (JSON format) from their computer, or they can stream provided examples by choosing one from the Load model dropdown or by clicking on a card above. Future versions of Simularium will allow you to modify, run, and even create simulations.

Current object representation options:

Spheres: by default, each agent in a scene is represented as a single sphere Mesh surfaces: represent each agent as a mesh file, e.g. coarse molecular surfaces Multi-sphere: provide Protein Databank .pdb files Line representations for fibers, filaments, or bonds

Planned for future:

Volume rendering for RDME or PDE-based simulation results Support for .cif files and coarse-grain sphereTree files for multi-sphere rendering Cite Simularium The Simularium Viewer: an interactive online tool for sharing spatiotemporal biological models.

Blair Lyons, Eric Isaac, Na Hyung Choi, Thao P. Do, Justin Domingus, Janet Iwasa, Andrew Leonard, Megan Riel-Mehan, Emily Rodgers, Lisa Schaefbauer, Daniel Toloudis, Olivia Waltner, Lyndsay Wilhelm & Graham T. Johnson. Nature Methods (2022). https://doi.org/10.1038/s41592-022-01442-1

Connect with us Receive updates & technical support, or provide feedback

We are collecting user feedback to improve this application. To find tutorials, seek technical support, report bugs or request features, use the Help menu found in the upper right corner of this website. Please contact us if you have ideas for potential collaborations, or want to be alerted to major updates.

Future plans for Simularium Create, modify, run, experiment, share, interoperate, & grow as a community

In the next phase of its development, Simularium will enable users to modify simulation parameters for provided models through the web-based interface, to run the modified simulations on the cloud, and to analyze the results. Simularium currently supports CytoSim and ReaDDy. We plan to wrap several published packages that can serve as templates for community development and to encourage growth of the system by providing a well- documented API for simulation engine integration.

We are investigating the use of these types of simulations and the various simulation engines that generated them for use in building an integrated, mechanistic understanding of human cells. We will also work with educators at all levels of science teaching to integrate and test Simularium’s potential for use in active learning classroom/lab/homework activities.

Acknowledgments We'd like to thank the following people for their contributions to Simularium

• For training and development with their simulation engines:

Johannes Schöneberg, Moritz Hoffmann, Christoph Fröhner, Frank Noé, François Nédélec, Adam Husar, Tom Bartol, Jacob Czech, Leslie Loew, Aniruddha Chattaraj, Michael Blinov, Ann Cowen, Ion Moraru, Garegin Papoian, Steve Andrews, Paul Macklin, Randy Heiland, Ryan Spangler, Eran Agmon, and James Faeder

• For providing example data:

Mohsen Sadeghi, Matthew Akamatsu, Daniel Serwas, Leslie Loew, Aniruddha Chattaraj, Jessica Yu, Julie Cass, and Jonathan Alberts

• For their guidance over Simularium's goals and progress as part of the project advisory committee:

Ron Vale, Torsten Moeller, Omar Quintero, Tom Goddard, and David Goodsell

• For their input on how Simularium could be used for education:

Omar Quintero, Derek Applewhite, Le Paliulis, Erin Dolan, Lalitha Jayant, and Carlos Goller

• For participating in user tests to improve the Viewer and other prototypes:

Matthew Akamatsu, Lindsay Case, Alyssa Harker, Kristen Verhey, Daniel Cortes, Andrew Nguyen, Cuncheng Zhu, and Karthik Vegesna

• For discussion on how building and analyzing simulations could help them in their research or teaching:

Julian Berro, Jiongyi Tan, Margot Quinlan, Lam Nguyen, Tamara Bidone, Glen Hocky, Christian Suarez, Simon Freedman, Carolyn Moores, Matt Akamatsu, Dave Kovar, Tom Pollard, Will Hancock, Michio Tomishige, Kimberly Tanner, Naina Phadnis, Sabrice Guerrier, Christina Vizcarra, Sarah Goodwin, Kathy Perkins, Kristen Verhey, David Frischer, Josh Sandquist, Melinda Owens, Brian Young, Ann Miller, and Jennifer Sallee

• For visualization inspiration and discussion:

Ludovic Autin and Ivan Viola

• For inspiration and/or comments on manuscripts:

Rick Horwitz, Julie Theriot