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Next Generation Simulation Tools: The Systems Biology Workbench ...
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Short Description: attempt to resolve this has been to develop a software framework called the System Biology Workbench. The workbench allows different tools to expose ...
Content Inside: OMICS A Journal of Integrative Biology Volume 7, Number 4, 2003 © Mary Ann Liebert, Inc. Next Generation Simulation Tools: The Systems Biology Workbench and BioSPICE Integration HERBERT M. SAURO,1,2 MICHAEL HUCKA,2 ANDREW FINNEY,2 CAMERON WELLOCK,1 HAMID BOLOURI,2,3 JOHN DOYLE,2 and HIROAKI KITANO4 ABSTRACT Researchers in quantitative systems biology make use of a large number of different soft- ware packages for modelling, analysis, visualization, and general data manipulation. In this paper, we describe the Systems Biology Workbench (SBW), a software framework that al- lows heterogeneous application components--written in diverse programming languages and running on different platforms--to communicate and use each others' capabilities via a fast binary encoded-message system. Our goal was to create a simple, high performance, open- source software infrastructure which is easy to implement and understand. SBW enables applications (potentially running on separate, distributed computers) to communicate via a simple network protocol. The interfaces to the system are encapsulated in client-side libraries that we provide for different programming languages. We describe in this paper the SBW architecture, a selection of current modules, including Jarnac, JDesigner, and SBWMeta- tool, and the close integration of SBW into BioSPICE, which enables both frameworks to share tools and compliment and strengthen each others capabilities. INTRODUCTION THE APPLICATION OF MATHEMATICS and computer science to understanding biochemical networks has a long history, going back in fact to the initial development of computers in the 1930s and 1940s (Chance et al., 1962; Burns, 1971). More recently and especially since the development of high-throughput data col- lection and the completion of the human genome project, there has been a renewed and vigorous interest in understanding the dynamic aspects of cellular networks (Endy and Brent, 2001; Rao and Arkin, 2002; Tyson et al., 2003). Although it has been appreciated for many years that cellular networks were dynamic, intricate control systems, the molecular biology revolution of the last 30 years, with its focus on DNA and protein structure, has taken center stage in mainstream biology at the expense of other studies. It is only in the last few years that "quantitative systems biology" is finally becoming a mainstream topic in biology. One of the important techniques at the disposal of the quantitative systems biologist is computer model- 11Keck Graduate Institute, Claremont, California. 2Control and Dynamical Systems, California Institute of Technology, California. 3Institute of Systems Biology, Seattle, Washington. 4ERATO Kitano Symbiotic Systems Project, Jingumae Shibuya-ku, Tokyo, Japan. 353
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