News

January 2, 2008
Hardware upgrade
using clustering system.


December 23, 2007
WebCell 2.0 released.
- Site renewal
- Extension for SBML level2
version3


January 11, 2007
WebCell 1.6 released.
- Conservation analysis

March 08, 2006
Accepted for publication in Bioinformatics.
Read More

January 04, 2006
WebCell 1.3 released.
- Metabolic control analysis
- Structural pathway analysis
- 37 pre-defined kinetic types

Jun 12, 2005
WebCell 1.2 released.
- Open to public
- Model construction/build
- Model exchange
(SBML import/export; MATLAB export)


November 10, 2004
WebCell 1.0 released.
- Basic simulation environment
- SBML level 1 and 2 support (import)

See older news items.

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Systems Biology Markup Language    JWS Online
BioModels    Metabolic & Biomolecular Engineerging Lab.
Process Systems Lab.    Bioinformatics Research Center Korea Advanced Institute of Science & Technology

Welcome to WebCell!
Modeling and Simulation of Cellular Networks Online

WebCell is an integrated simulation environment for managing quantitative and qualitative information on cellular networks, and for interactively exploring their steady-state and dynamic behaviors over the web. A user-friendly web interface allows users to efficiently create, visualize, simulate and store their reaction network models, thereby facilitating kinetic modeling and simulation of biological systems of interest. Supported analysis methods for such models include, but not limited to, structural pathway analysis, metabolic control analysis (MCA), conservation analysis and dynamic simulation. A variety of model collections publicly available have been compiled to provide comprehensive implications for cellular dynamics of the models.


WebCell Features

  • Model construction: Provide a simple and comprehensive environment for modeling of cellular networks through the well-designed web interface
    1. Describe the project summary
    2. Define model components (e.g., molecular compounds or metabolites) and their relations (e.g., reactions or interactions)
    3. Set relevant kinetic information (e.g., rate equations, user- or pre-defined kinetic type and relevant parameters, and initial concentrations)
    4. Easily edit, modify and save model information on the project summary, components, relations and kinetics
    5. Dynamically generate tabularized view pages for model information on summary, compounds, reactions and kinetic equations
    6. Provide interactive access to detailed information on each reaction or compound using the hyperlinks
    7. Automatically layout the model network for the dynamic visualization
  • Model exchange: Enhance the modeling capability
    1. SBML support (import/export)
      • Systems Biology Markup Language (SBML) is an eXtensible Markup Language (XML)-based modeling language for representing biochemical networks (Hucka et al., 2003)
    2. MATLAB export
  • Model analysis: Provide various analysis techniques
    1. Validate a kinetic model
    2. Time course simulation
      • Once the valid kinetic model is constructed, we can explore dynamic behavior of the cellular system by solving ordinary differential equations (ODEs) or differential algebraic equations (DAEs) with conserved moieties which can be identified through conservation analysis.
    3. Conservation analysis
      • Some models contain combinations of metabolite masses, that are constant in time, due to stoichiometric constraints. These conserved masses, also known as conserved moieties, are time-invariant property of the systems. WebCell reveals all conserved moieties automatically from the initial concentrations.
    4. Structural pathway analysis: Null space analysis/Elementary mode analysis
      • Pathway analysis investigates structural and functional properties of the given biological network by identifying independent pathways, futile cycles as well as optimal pathways with respect to product/biomass yield.
    5. Metabolic control analysis (MCA)
      • Metabolic Control Analysis (MCA) is a mathematical formalism for the steady-state control and sensitivity analysis of biological systems (Kacser and Burns, 1973). MCA attempts to describe the relative control of each component in the systems (the independent variables or parameters) exerting on the pathway fluxes and metabolite concentrations (the dependent variables). The sensitivity of network components to perturbations is quantified by evaluating flux control, concentration control , elasticity and reponse coefficients, thus identifying the inherent relationship between relative changes in the system variable (e.g., flux or metabolite concentration) and perturbed parameter.