Java Web Simulation (JWS); a web based database of kinetic models J.L. Snoep (
[email protected]) and B.G. Olivier (
[email protected]) Dept. of Biochemistry, University of Stellenbosch, Private Bag X1, Matieland 7602, South Africa Molecular Cell Physiology, Vrije Universiteit, de Boelelaan 1087, 1081 HV Amsterdam, The Netherlands Abstract. Software to make a database of kinetic models accessible via the internet has been developed and a core database has been set up at http://jjj.biochem.sun.ac.za/. This repository of models, available to everyone with internet access, opens a whole new way in which we can make our models public. Via the database, a user can change enzyme parameters and run time simulations or steady state analyses. The interface is user friendly and no additional software is necessary. The database currently contains 10 models, but since the generation of the program code to include new models has largely been automated the addition of new models is straightforward and people are invited to submit their models to be included in the database. Keywords: Mathematica, Java, JLink, Metabolic Control Analysis
1. The problem Isolation and characterization, the paradigms in biochemistry have been important in the analysis of components but do not necessarily lead to a systemic understanding. Yeast glycolysis, the first metabolic pathway discovered and all of its enzymes extensively studied, serves as a good example. After a century of research we still do not know what controls the glycolytic flux in this organism. The classic textbook view on control of glycolysis, i.e. control resides in glucokinase, phosphofructokinase and pyruvate kinase (e.g. Garrett and Grisham, 1999) is based on a semi-quantitative interpretation of enzyme kinetic data and is plainly wrong (e.g. Schaaff et al., 1989). To understand glycolysis in terms of its enzyme kinetics one needs a quantitative integration and a robust theoretical framework. A main obstacle in the integration of enzyme kinetic data was the non-linear character of the kinetics making it often impossible to find analytic solutions. Pioneers in the modelling of metabolic pathways were Chance, Garfinkel and Hess (e.g. Garfinkel and Hess, 1964), with models of glycolysis first described in terms of elementary rate constants. Detailed models are often difficult to understand and a higher level of description is necessary for this. The usage of overall rate equations such as the Michaelisc 2002 Kluwer Academic Publishers. Printed in the Netherlands.
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Menten equation makes a model much more accessible but with many enzymes working together one still needs robust techniques to analyze kinetic models. In the seventies Metabolic Control Analysis (MCA) was developed (Kacser and Burns, 1973; Heinrich and Rapoport, 1974), providing exactly such a technique. Via so-called control coefficients, dependencies of system variables to rate velocities are quantified and these systemic properties can be expressed in the characteristics of the isolated components. MCA has a solid mathematical background but is much more than just a theoretical framework (see Fell 1992, for a review and some applications). Its definitions are cast in a very applicable form and make the framework ideal for experimental approaches. With the development of the personal computer and user friendly software, incorporating MCA, (Sauro, 1993; Mendes, 1997), the making of computer models was in principle possible for everyone. In the last decades hundreds of computer models of metabolic systems have been made and the rapid development in bioinformatics might lead to another boost in the making of computer models. Most of these models are published in scientific journals and in principle it should be possible for anyone to construct these models. However, this can be a daunting task if the model consists of many reactions, and more often than not the model description is incomplete. A solution to this problem could be to decide on a general language to describe the models. If such a description file could be used as input file for modelling software then this would prevent a user from having to build the model from scratch. Such an effort has been initialized in the Systems Biology Markup Language (SBML) http://www.cds.caltech.edu/erato. But this does not solve the problem completely. The user of such a model would still need to download, install and learn to use a software package in order to run the model. Although this would be an effort that needs to be made only once, it might be a big step for users unfamiliar to modelling. On top of this, independent of which language or modelling program used, no database exists where users can retrieve existing kinetic models.
2. The solution To solve these problems we decided to make a web-based database of kinetic models available to everyone that has access to the internet. Currently we have developed the software for such a database (Java Web Simulation for Metabolic Modelling (JWS) software) and coded 10 models that can be accessed and run via the internet. Details of the models and bibliographic references can be found on the website (http://jjj.biochem.sun.ac.za). The JWS software is implemented in
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the Java programming language using a client server model. All development was done using Java Software Development Kit 1.3 by Sun Microsystems as well as J/Link Java Toolkit 1.1.2 and Mathematica 4.0 by Wolfram Research, running under Microsoft Windows 2000. The JWS server runs as a stand alone Java program which uses J/Link as an interface to facilitate all communication with Mathematica. All calculations are performed by the Mathematica Kernel. Models are written in J/Link code and compiled as Java classes, which are linked into the server as modules. The server is multi-threaded and can handle multiple simultaneous sessions, each active session loading an individual Mathematica Kernel for processing. The JWS client is a Java Applet that provides a graphical interface for the user, establishing communication links with the server and displaying the results of the calculation. A typical web modelling session involves loading the JWS webpage (typing the URL http://jjj.biochem.sun.ac.za into a web browser) and navigating to the models page. Subsequently a choice must be made between the different models in the database. After such a selection has been made the user automatically downloads a model applet which provides a user interface to the server. A typical screen capture is shown in Figure 1. The user can change enzyme kinetic parameters on the graphical interface and choose between either a steady-state analysis or a time simulation. If the user selects a steady-state analysis (by clicking the radio button ‘steady state’) and subsequently presses the evaluate button a table with the steady-state metabolite concentrations and fluxes will be given as output. If the user selects ‘time simulation’ an additional choice must be made between metabolites and rates to be plotted in the resulting graph. All numerical and graphics processing is handled by the server and therefore the processor and memory requirements of the client are not related to the size and complexity of the model. Transient conditions over the Internet may vary from location to location and affect the performance of the software. We have therefore set up two JWS servers; located in South Africa and the Netherlands. The JWS Applet will run in any browser that supports Java2. Typically, these capabilities are provided by the Java Runtime Environment (JRE) plug-in (version 1.3 or higher), which is freely available for download from Sun Microsystems. Using the JRE plug- in we have successfully tested the applets using the following web browsers and operating systems: Microsoft Internet Explorer (versions 5.0 and 6.0), Netscape Navigator (versions 4.7 and 6.1), all running under Microsoft Windows (versions 98 and 2000) and Mozilla running under Linux (Mandrake 8.2). We have also tested the JWS software using a standard analogue modem and telephone line, although a minimum transfer rate of 56kb/s is recommended.
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Figure 1. Output of a time simulation The graphical output of a time simulation of the detailed model of yeast glycolysis by Teusink et al. (2000) is shown. In such a time simulation the user can select the length of the time simulation, the metabolites or rates that should be plotted, and change parameters of the model (in this case the Vmax values of the enzymes). When the Metabolic Pathway button is clicked a scheme of the pathway that is modelled is shown and on this scheme the user can click on any of the enzymes to reveal the rate equation and the enzyme parameters.
3. Future projects Currently only a steady-state or time-course evaluation can be performed with the models, however we are extending the software’s capabilities to include an MCA evaluation using the Reder method (Reder, 1988). This will allow the calculation of both the elasticity and control coefficients. In future we will extend the capabilities of the database to also include a scanning option. In addition we have set up a bulletin board discussion forum to encourage an active discussion of the models in the database. We have automated the generation of Java code from a standard input file and are considering the possibility to have a separate section in the database where users are able to submit their own models.
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4. WWW links Primary JWS server (South Africa): http://jjj.biochem.sun.ac.za/ Mirror JWS server (The Netherlands): http://www.jjj.bio.vu.nl Sun Microsystems Java resources: http://java.sun.com/ Wolfram Research: http://www.wolfram.com/ J/Link @ Wolfram Research: http://www.wolfram.com/solutions/mathlink/jlink/
5. References Fell, D. A. 1992. Biochem J 286: 313-330. Garfinkel and Hess 1964. J. Biol. Chem. 239:971-983. Garrett and Grisham, 1999. Biochemistry, second edition. Saunders College publishing, Fort Worth. Heinrich, R. and T. A. Rapoport 1974. Eur. J. Biochem. 42: 89-95. Kacser, H. and J. A. Burns 1973. Symp. Soc. Exp. Biol. 27: 65-104. Mendes, P., 1997. TIBS 22: 361-363. Reder, C., 1988. J theor. Biol. 135:175-201. Sauro, H. M., 1993. Comput. Appl. Biosci. 9: 441-450. Schaaff, I., Heinisch, J., and Zimmerman, F.K., 1989. Yeast 5:285-290. Teusink, B., J. Passarge, Reijenga, C. A. Esgalhado, E. Van der Weijden, C. C. Schepper, M. Walsh, M. C. Bakker, B. M. Van Dam, K. Westerhoff, H. V. Snoep, J. L. 2000. Eur J Biochem 267: 5313-5329.
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