Abstract
Systems biology develops mathematical models of biological systems that seek to explain, or better still predict, how the system behaves. In bottom-up systems biology, systematic quantitative experimentation is carried out to obtain the data required to parameterize models, which can then be analyzed and simulated. This paper describes an approach to integrated information management that supports bottom-up systems biology, with a view to automating, or at least minimizing the manual effort required during, creation of quantitative models from qualitative models and experimental data. Automating the process makes model construction more systematic, supports good practice at all stages in the pipeline, and allows timely integration of high throughput experimental results into models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Herrgard, M.J., et al.: A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat. Biotechnol. 26(10), 1155–1160 (2008)
Klipp, E., Herwig, R., Kowald, A., Wierling, C., Lehrach, H.: Systems Biology in practice. Wiley, Chichester (2005)
Rojas, I., Golebiewski, M., Kania, R., Krebs, O., Mir, S., Weidemann, A., Wittig, U.: Storing and annotating of kinetic data. Silico Biol. 7 (suppl. 2), S37–S44 (2007)
Mendes, P., Hoops, S., Sahle, S., Gauges, R., Dada, J., Kummer, U.: Computational modeling of biochemical networks using COPASI. Methods Mol. Biol. 500, 17–59 (2009)
Rivers, J., Simpson, D.M., Robertson, D.H., Gaskell, S.J., Beynon, R.J.: Absolute multiplexed quantitative analysis of protein expression during muscle development using QconCAT. Mol. Cell. Proteomics 6(8), 1416–1427 (2007) (Epub May 17, 2007)
Spasic, I., et al.: MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics. BMC Bioinformatics 7, 281 (2006)
Jones, P., Côté, R.G., Martens, L., Quinn, A.F., Taylor, C.F., Derache, W., Hermjakob, H., Apweiler, R.: PRIDE: a public repository of protein and peptide identifications for the proteomics community. Nucleic Acids Res. 34, D659–D663 (2006)
Oinn, T., et al.: Taverna: a tool for the composition and enactment of bioinformatics workflows. Bioinformatics 20, 3045–3054 (2004)
Hucka, M., et al.: The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19(4), 524–531 (2003)
Dada, J.O., Mendes, P.: Design and Architecture of Web Services for Simulation of Biochemical Systems. In: Paton, N.W., Missier, P., Hedeler, C. (eds.) DILS 2009. LNCS, vol. 5647, pp. 182–195. Springer, Heidelberg (2009)
Dada, J.O., Spasic, I., Paton, N.W., Mendes, P.: SBRML: a markup language for associating systems biology data with models. Bioinformatics (Feburary 21, 2010) (Epub ahead of print)
Siepen, J.A., et al.: An informatic pipeline for the data capture and submission of quantitative proteomic data using iTRAQTM. Proteome Sci. 5, 4 (2007)
Perkins, D.N., et al.: Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20(18), 3551–3567 (1999)
Nilse, L., et al.: SILACAnalyzer - a tool for differential quantitation of stable isotope derived data. In: CIBB, 6th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Genoa (2009)
Garwood, K., et al.: Model-driven user interfaces for bioinformatics data resources: regenerating the wheel as an alternative to reinventing it. BMC Bioinformatics 7, 532 (2006)
Swainston, N., et al.: Enzyme kinetics informatics: from instrument to browser. FEBS J. (submitted 2010)
Michaelis, L., Menten, M.L.: Die Kinetik der Invertinwirkung. Biochem. Z, 49, 333–369 (1913)
Jameson, D., et al.: Lightweight Experimental Data Management for Systems Biology (submitted 2010)
Degtyarenko, K., et al.: ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res. 36, D344–D350 (2008)
Issel-Tarver, L., et al.: Saccharomyces Genome Database. Methods Enzymol. 350, 329–346 (2002)
Schneider, M., et al.: The UniProtKB/Swiss-Prot knowledgebase and its Plant Proteome Annotation Program. J. Proteomics 72, 567–573 (2009)
Li, P., et al.: Automated manipulation of systems biology models using libSBML within Taverna workflows. Bioinformatics 24, 287–289 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Swainston, N., Jameson, D., Li, P., Spasic, I., Mendes, P., Paton, N.W. (2010). Integrative Information Management for Systems Biology. In: Lambrix, P., Kemp, G. (eds) Data Integration in the Life Sciences. DILS 2010. Lecture Notes in Computer Science(), vol 6254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15120-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-15120-0_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15119-4
Online ISBN: 978-3-642-15120-0
eBook Packages: Computer ScienceComputer Science (R0)