Bioinformatics Advance Access originally published online on March 16, 2006
Bioinformatics 2006 22(9):1150-1151; doi:10.1093/bioinformatics/btl091
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
WebCell: a web-based environment for kinetic modeling and dynamic simulation of cellular networks
,
1 Department of Chemical and Biomolecular Engineering, Metabolic and Biomolecular Engineering National Research Laboratory 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea
2 Bioinformatics Research Center, Korea Advanced Institute of Science and Technology 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea
3 Department of BioSystems, Korea Advanced Institute of Science and Technology 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea
*To whom correspondence should be addressed.
 |
ABSTRACT |
|---|
 TOP ABSTRACT 1 INTRODUCTION2 SYSTEM OVERVIEW3 SUMMARY AND FUTURE...REFERENCES |
|---|
Summary: WebCell is a web-based environment for managing quantitative and qualitative information on cellular networks and for interactively exploring their steady-state and dynamic behaviors in response to systemic perturbations. It is designed as a user-friendly web interface, allowing users to efficiently construct, visualize, analyze and store reaction network models, thereby facilitating kinetic modeling and in silico simulation of biological systems of interest. A collected model library is also available to provide comprehensive implications for cellular dynamics of the published models.
Availability: WebCell is accessible at http://webcell.kaist.ac.kr or http://webcell.org.
Contact: leesy{at}kaist.ac.kr
|
1 INTRODUCTION |
|---|
|
TOPABSTRACT1 INTRODUCTION2 SYSTEM OVERVIEW3 SUMMARY AND FUTURE...REFERENCES |
|---|
It is increasingly accepted that kinetic modeling and simulation of biological systems are promising to discover a knowledge map for elucidating the functions and characteristics of the living system in detail. Given the biochemical reaction network, kinetic and regulatory information can be incorporated into a model comprising the continuous differential equations, thereby completely describing the dynamic system and predicting its behavior under any perturbations. Thus, it is highly desirable to establish an effective method and develop software tools for such kinetic modeling and concomitant simulation. To this end, various softwares and computational environments have been developed as listed in the systems biology community (http://sbml.org/); each tool has its own capability and strength in a wide range of feature combinations, e.g. user interface, mathematical framework and database functionality (Hucka et al., 2004). Among them, however, only a handful of projects adopt the platform-independent web-based approach, which is a desirable direction for simulation research and development. The most relevant initiatives include Virtual Cell (Loew and Schaff, 2001) and JWS Online (Olivier and Snoep, 2004), which are implemented through Java Web Start and Java applets, respectively, on a web browser. Virtual Cell provides the generalized simulation environment allowing kinetic/spatial modeling and analysis of biological systems, while JWS Online focuses on web-based repository of kinetic models which are interrogated and analyzed through the well-designed graphical interface. Here, we report the development of WebCell, an integrated web-based environment, which facilitates investigation of dynamic behaviors of cellular systems and provides a collected model library as a pedagogic tool.
|
2 SYSTEM OVERVIEW |
|---|
|
TOPABSTRACT1 INTRODUCTION2 SYSTEM OVERVIEW3 SUMMARY AND FUTURE...REFERENCES |
|---|
The WebCell system is designed by combining Java Server Page (JSP) and Java applet-servlet technologies, running on Windows Server 2003 where Apache's Jakarta Tomcat application server, Java 2 Platform Standard Edition Development Kit (JDK) 1.4.0 and MySQL are installed. This allows interactive kinetic modeling and simulation of complex biochemical systems through both client- and server-side executions over the web. The web pages are dynamically generated using JSP and Jython scripts.
2.1 Model construction and validation
WebCell is interfaced to provide a simple and comprehensive modeling environment (Fig. 1). Users can login to a WebCell modeling session after creating the user account in the database of the web-server. The login initiates the creation of new model project, followed by building a kinetic reaction model. The well-designed web interface allows the users to describe the project summary, define components (e.g. molecular compounds or metabolites) and their relations (e.g. reactions or interactions), and to set relevant kinetic information (e.g. rate equations, user- or pre-defined kinetic types and relevant parameters, and initial concentrations).
|
WebCell supports Systems Biology Markup Language (SBML), which is an eXtensible Markup Language (XML)-based modeling language for representing biochemical networks (Hucka et al., 2003), in order to enhance the modeling capability. This makes it possible to directly import existing SBML-formatted models from their local computers into the WebCell database system on our remote server. Imported models are then mapped into the new model projects of the users for the effective construction of cellular network models. A tabularized view page of entire model summary is dynamically generated, allowing access to more detailed information on each reaction or compound using the hyperlinks provided. Such information can be readily edited, stored and modified. Conversely, project models can be exported as non-specific tab delimited text files in various formats, e.g. SBML and MATLAB (http://www.mathworks.com), which in turn can be easily imported to and executed by other programs. The resulting model network can also be visualized to represent a whole picture of the network through automatic layout algorithm.
As a unique feature, WebCell allows model validation by considering the energy balance constraints of kinetic parameters in the model. Given the closed reaction network and the kinetic schemes of its participating reactions, a set of cyclic pathways can be detected by calculating the null space (or kernel) of the network stoichiometry. It is followed by checking if the kinetic parameters in those cyclic pathways are log-linearly balanced according to the thermodynamic principle (Beard et al., 2002). In this way, the appropriateness of the kinetic models can be validated automatically. This feature should be invaluable in building a robust and accurate model.
2.2 Model analysis
Once the valid kinetic model is constructed, the dynamic behaviors of the cellular system can be explored by solving ordinary differential equations (ODEs) or differential algebraic equations (DAEs) with conserved moieties which can be identified through conservation analysis (Sauro and Ingalls, 2004). After the user selects one of the ODE and DAE solvers and specifies simulation settings, the model can be run by clicking the ‘SIMULATE’ button. Simulation results are then returned to display the time course of selected compounds and/or reactions in the plot view. The results can also be presented and acquired in comma delimited, comma separated value (CSV) text file format. In addition to this time-course simulation, WebCell integrates structural pathway analysis and metabolic control analysis (MCA) for steady-state and sensitivity analyses of biological systems. In MCA (Kacser and Burns, 1973), the sensitivity of network components to perturbations is analyzed by computing control, elasticity and response coefficients, thus identifying the inherent relationship between relative changes in the system variables and perturbed parameters.
2.3 Model library
For the tutorial purposes, WebCell allows users to access to the model library where various kinetic models are compiled from the published works, SBML model repository (http://sbml.org/models.html), JWS Online (http://jjj.biochem.sun.ac.za) and BioModels database (http://www.ebi.ac.uk/biomodels). In accordance, the users can perform in silico perturbations of rigorously validated models which are categorized by metabolic, signaling and regulatory networks.
|
3 SUMMARY AND FUTURE DEVELOPMENT |
|---|
|
TOPABSTRACT1 INTRODUCTION2 SYSTEM OVERVIEW3 SUMMARY AND FUTURE...REFERENCES |
|---|
WebCell is a comprehensive, web-accessible and integrative system which not only serves as an educational system for revisiting publicly available kinetic models, but also provides the customized modeling environment for quantitatively and dynamically analyzing the cellular network. WebCell is complementary to the MetaFluxNet, which allows static analysis of genome-scale network (Lee et al., 2003). Currently, the incorporation of model reduction techniques (Okino and Mavrovouniotis, 1998), e.g. lumping, sensitivity analysis and time-scale analysis, into the WebCell system is in progress. By doing so, large-scale models of biochemical and biological networks can be simplified to relieve their complexity, which will allow their systems level understanding under the web-based simulation environment.
|
Acknowledgments |
|---|
This work was supported by the Korean Systems Biology Research Program (M10309020000-03B5002-00000) of the MOST and by the BK21 project. Further supports by LG Chem Chair Professorship, Microsoft and IBM-SUR program are greatly appreciated.
Conflict of Interest: none declared.
|
FOOTNOTES |
|---|
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors. 
Present address: Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576, and Bioprocessing Technology Institute, Agency for Science and Technology Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore 138668.
Associate Editor: Alfonso Valencia
Received on January 3, 2006; revised on March 7, 2006; accepted on March 7, 2006
|
REFERENCES |
|---|
|
TOPABSTRACT1 INTRODUCTION2 SYSTEM OVERVIEW3 SUMMARY AND FUTURE...REFERENCES |
|---|
Beard, D.A., et al. (2002) Energy balance for analysis of complex metabolic networks. Biophys. J, . 83, 79–86[Web of Science][Medline].
Lee, D.-Y., et al. (2003) MetaFluxNet: the management of metabolic reaction information and quantitative metabolic flux analysis. Bioinformatics, 19, 2144–2146
Loew, L.M. and Schaff, J.C. (2001) The virtual cell: a software environment for computational cell biology. Trends Biotechnol, . 19, 401–406[CrossRef][Web of Science][Medline].
Hucka, M., et al. (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics, 19, 524–531
Hucka, M., et al. (2004) Evolving a lingua franca and associated software infrastructure for computational systems biology: the Systems Biology Markup Language (SBML) project. Syst. Biol, . 1, 41–53[CrossRef].
Kacser, H. and Burns, J.A. (1973) The control of flux. Symp. Soc. Exp. Biol, . 27, 65–104[Medline].
Okino, M.S. and Mavrovouniotis, M.L. (1998) Simplification of mathematical models of chemical reaction systems. Chem. Rev, . 98, 391–408[CrossRef][Web of Science][Medline].
Olivier, B.G. and Snoep, J.L. (2004) Web-based kinetic modelling using JWS Online. Bioinformatics, 20, 2143–2144
Sauro, H.M. and Ingalls, B. (2004) Conservation analysis in biochemical networks: computational issues for software writers. Biophys. Chem, . 109, 1–15[CrossRef][Web of Science][Medline].
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  L. Endler, N. Rodriguez, N. Juty, V. Chelliah, C. Laibe, C. Li, and N. Le Novere Designing and encoding models for synthetic biology J R Soc Interface, August 6, 2009; 6(Suppl_4): S405 - S417. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D.-Y. Lee, R. Saha, F. N. K. Yusufi, W. Park, and I. A. Karimi Web-based applications for building, managing and analysing kinetic models of biological systems Brief Bioinform, January 1, 2009; 10(1): 65 - 74. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Weidemann, S. Richter, M. Stein, S. Sahle, R. Gauges, R. Gabdoulline, I. Surovtsova, N. Semmelrock, B. Besson, I. Rojas, et al. SYCAMORE--a systems biology computational analysis and modeling research environment Bioinformatics, June 15, 2008; 24(12): 1463 - 1464. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. Stromback, V. Jakoniene, H. Tan, and P. Lambrix Representing, storing and accessing molecular interaction data: a review of models and tools Brief Bioinform, December 1, 2006; 7(4): 331 - 338. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Heinemann and S. Panke Synthetic biology--putting engineering into biology Bioinformatics, November 15, 2006; 22(22): 2790 - 2799. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||