VCell Modeling & Analysis Software 2021-03-12T16:00:26+00:00

 
 

Register for the

22nd CCB Workshop – Online

June 21-23, 2021

VCell

VCell (Virtual Cell) is a comprehensive platform for modeling cell biological systems that is built on a central database and disseminated as a web application.

  • One-stop simulation shopping: deterministic (compartmental ODE or reaction-diffusion-advection PDE with support for 2D kinematics), stochastic reactions (SSA solvers), spatial stochastic (reaction-diffusion with Smoldyn), hybrid deterministic/stochastic and network-free agent based simulations. Support for membrane flux, lateral membrane diffusion and electrophysiology.
  • Explicit network or graphically expressed rules can be used to model
  • Free with automatic installers for Windows, Mac OS and Linux.
  • Biology-based interface for inexperienced modelers; enter reactions and pathways and VCell automatically creates the math for you. Experienced modelers can enter math directly.
  • Our remote servers can run complex simulations from any low-cost laptop
  • Geometries from 2D or 3D microscope images or from idealized analytical expressions.
  • Access models and simulations from anywhere using the VCell database; models can be shared among collaborators or made publicly available.

SpringSaLaD

SpringSaLaDSpringSaLaD is a stand-alone software tool to explicitly model binding events and state changes among multivalent molecules. It is one of the first algorithms to account for crowding effects within multimolecular clusters. Spring SaLaD models proteins as sets of reactive sites (spheres) connected by stiff springs. The impenetrable spheres capture excluded volume and steric hindrance effects. Langevin dynamics are used to model diffusion of each reaction site, and binding reactions are governed by probability based on diffusion coefficients of the sites, the site radii and the macroscopic on rate. Go here to download the software or read more about the about Spring SaLaD.

Who Are We?

The Virtual Cell was developed with funding from the National Institute of General Medical Sciences (NIGMS) as a Biomedical Technology Research Resource at the Center for Cell Analysis and Modeling (CCAM), and is currently funded by R24 GM137787. CCAM continues to develop new technologies for mathematical models of cell and systems biology through development of new physical formulations of biological mechanisms, developing the numerical methods for mathematically simulating these mechanisms, and bulding software infrastructure to deliver these tools for different types of modeling applications including large reaction network applications, spatial applications and detailed molecular interactions. Meet the VCell Team.

Where Are We?

VCell is developed at The Center for Cell Analysis & Modeling, at UConn Health. Established in 1994, CCAM consists of faculty trained in diverse backgrounds from chemistry, physics, and experimental cell biology to software engineering. Research at CCAM focuses on the development of new approaches for in vivo measurements and manipulation of molecular events within the cell, as well as new computational approaches to organize such data into quantitative models.  CCAM is home to the Microscopy Facility, housing numerous extensive fluorescent imaging microscopes, and the High Performance Computing facility.

Please acknowledge the VCell Resource in all publications. VCell is supported by NIH Grant Number R24 GM137787 from the National Institute for General Medical Sciences. And please reference the appropriate citations.

News

For additional posts see News & Events.

VCell model of Axon Initial Segment

2021-4-05. A VCell model was used to assess the role of casein kinase 2 and ankyrin-G on sodium channel recruitment to the axon initial segment. Find links the citation on our published models page.

VCell model of Mad signaling

2021-3-30. A new paper from the Inaba lab uses a VCell model to explore how pMad is asymmetrically partitioned to forming daughter nuclei during division of stem cells in the Drosophila ovary.  Find links to the model and citation on […]

VCell 7.4 released

2021-03-23.VCell 7.4 has been released.  New in VCell 7.4 is the ability to preview geometries from the VCell database, vastly accelerated multiple trajectory stochastic simulations using the Gibson-Bruck solver, extension of unit display and automatic conversion to rate and […]

New VCell model of Ras signaling

2021-3-18. A new paper by Kerbai Eroume in the Carlier lab published in PLOS One explores how cell shape affects the signaling mechanisms that determine cell polarization. Find links to the model and citation on our published […]

VCell 7.3 released

2020-11-30. Announcing the release of VCell 7.3!  VCell 7.3 adds capabilities for multiple trajectories for non-spatial stochastic simulationss, interchange with the Open Modeling EXchange format (.omex), and automatic assignment of catalysts in the reaction diagram as well as numerous additional […]

VCell Partners

VCell acknowledges our collaborative partners that enhance VCell capabilities

Collaborations currently in development

Software Associates

EJ Technologies

VCell uses Install4J, a multi-platform installer builder to create our executables.

What Our Users Say!

“Searching through existing software packages, our attention focused on Virtual Cell….For solving differential equations, as well as storing and sharing models, Virtual Cell provides free, remote solvers and storage servers available to a worldwide community of users.”
Baik J, Rosania GR.
2013 Modeling and Simulation of Intracellular Drug Transport and Disposition Pathways with Virtual Cell.
Journal of pharmaceutics & pharmacology:1(1) .
“A key advantage of Virtual Cell is that the math is performed “behind the scenes”, so the user can focus on the biochemical reactions and biology of interest. Virtual Cell was developed … to be a simple yet powerful tool to allow students and biologists with relatively little math background to perform computational modeling.”
Greenwald EC, Polanowska-Grabowska RK, Saucerman JJ. 2014. Integrating fluorescent biosensor data using computational models. Methods in Molecular Biology:1071:227-48.
“An advantage of the compartmentalized models supported by Virtual Cell is the ease of building complex, but well constrained models that are informed by experimental data.”
Hake, J., P.M. Kekenes-Huskey, and A.D. McCulloch. 2014. Computational modeling of subcellular transport and signaling.
Current Opinion in Structural Biology. 25:92-97.

Funding

NIHBTR
NIGMS