VCell Modeling & Analysis Software 2019-07-23T18:16:48+00:00

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 as a National Resource Center, the National Resource for Cell Analysis and Modeling (NRCAM), by the National Institute of General Medical Sciences(NIGMS), and is currently funded by R24 GM134211. NRCAM 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?

NRCAM, responsible for the development of VCell, resides at The Center for Cell Analysis & Modeling, at UConn Health. CCAM, established in 1994, consists of expertly trained faculty of varying backgrounds. This diverse wealth of knowledge ranges 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 reference the Virtual Cell Resource
in your publications; grant # R24 GM134211.

News

For additional posts see News & Events.

New model of GIV/girdin modulation of cyclic AMP signals

2019-06-13. A new Molecular Biology of the Cell publication from the Rangamani lab uses VCell and COPASI to develop a model to examine cross-talk between receptor tyrosine […]

VCell models aid in design of SH2 domain biosensors

2019-06-04. A new publication in Science Signaling  from the Haugh and Rao labs describes the use of VCell modeling to assist in designing improved SH2 domain […]

VCell model of RAF1 membrane dynamics published

2019-02-14. A VCell spatial model created to identify mechanisms regulating membrane abundance of the small Gprotein RAF1 at the plasma membrane has been published in […]

VCell model of voltage-sensing phosphatase specificity

2019-02-04. A new publication from the Hille lab uses a VCell model to reveal emergent properties of the behavior of voltage-sensitive phosphatases. Visit our […]

New VCell analysis of FRAP experiments

2019-01-25. A new publication from Karvinen et al uses VCell models to analyze photobleaching experiments in their characterization of hydrogel properties. Visit our published models […]

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