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Databases for Systems Biology

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons127401

Ginkel,  Martin
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Zitation

Eils, J., Lawerenz, C., Astrahantseff, K., Ginkel, M., & Eils, R. (2005). Databases for Systems Biology. In A. Kriete, & R. Eils (Eds.), Computational Systems Biology (pp. 15-38). Amsterdam [u.a.]: Elsevier.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-9D49-7
Zusammenfassung
The ultimate goal of researchers in the interdisciplinary field of systems biology is to solve biological problems at the level of an entire system. Achieving this goal requires supporting the efforts of experimental biologists and computational modelers. Optimally, the phases of planning, actual experimentation, and data analysis (as well as model development, testing, and validation) would all be supported by one database solution. There is currently no integrative source for all information required in a computer-generated model of a biological system, and no system capable of providing support for all three phases of a systems biology endeavor. We present the concept of an integrative database for systems biology that functions as a data warehouse system and supports all three phases of a systems biology project. This database system consists of three modules with different data models supporting the particular requirements of utilizing the three general types of data required: experimental data, components, and reactions of biological systems and mathematical models. The model and experiment modules are linked through the component/reaction module, eliminating the need to store complete information about any one entity more than once in the database. Complete functional models and simulations of particular interest are stored as SBML (Systems Biology Markup Language) files and linked to all necessary information within the database. This combination of modules tailored for dealing with the different data types and the interaction of these modules via links will meet the needs of researchers in the area of systems biology. Copyright © 2006 Elsevier Inc. All rights reserved. [accessed 2014 February 12th]