Summary
This chapter provides an overview of how the use of ontologies may enhance biomedical research by providing a basis for a formalized, and shareable descriptions, of models of biological systems.
A wide variety of artifacts are labeled as “ontologies” in the Biomedical domain, leading to much debate and confusion. The most widely used ontological artifact are controlled vocabularies (CVs). A CV provides a list of terms whose meanings are specifically defined. Terms from a CV are usually used for indexing records in a database. The Gene Ontology (GO) is the most widely used CV in databases serving biomedical researchers. The GO provides term for declaring the molecular function (MF), biological process (BP) and cellular component (CC) of gene products. The statements comprising these MF, BP and CC declaration are called annotations [51], which are predominantly used to interpret results from high throughput gene expression experiments [27, 53]. Arguably, CVs provide the most value for effort in terms of facilitating database search and interoperability.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
An automated technique for simultaneously analyzing thousands of different DNA sequences or proteins affixed to a thumbnail-sized “chip” of glass or silicon. DNA microarrays can be used to monitor changes in the expression levels of genes in response to changes in environmental conditions or in healthy vs. diseased cells. Protein arrays can be used to study protein expression, protein–protein interactions, and interactions between proteins and other molecules. From – www.niaaa.nih.gov/publications/arh26-3/165-171.htm
- 2.
High-throughput technologies are large-scale, usually automated, methods to purify, identify, and characterize DNA, RNA, proteins and other molecules. They allow rapid analysis of very large numbers of samples.
- 3.
For this current discussion, a formal representation means a computer-interpretable standardized form that can be the basis for creating unambiguous descriptions of biological systems 2.1.
- 4.
We use “models” to mean a schematic description of a system or phenomenon that accounts for its known or inferred properties and can be used for further study of its characteristics.
- 5.
Source public domain, non copy righted image.
- 6.
The cell cycle is a complicated biological process and comprises of the progression of events that occur in a cell during successive cell replication. The process can be described at varying level of details ranging from a high level qualitative description to a detailed system of differential equations. However, for most biological processes the representation is primarily in terms of qualitative interactions.
- 7.
References
T. Akutsu, S. Miyano, and S. Kuhara. Algorithms for identifying boolean networks and related biological networks based on matrix multiplication and fingerprint function. J Comput Biol, 7(3-4):331–43, 2000. 1066-5277 Journal Article.
R. Altman, M. Buda, X. Chai, M. Carillo, R. Chen, and N. Abernethy. Riboweb: an ontology-based system for collaborative molecular biology. Intelligent Systems, IEEE [see also IEEE Expert], 14(5):68–76, 1999. TY - JOUR.
G. An. Concepts for developing a collaborative in silico model of the acute inflammatory response using agent-based modeling. J Crit Care, 21(1):105–10; discussion 110–1, Mar 2006.
C. Baral, K. Chancellor, N. Tran, N. Tran, A. Joy, and M. Berens. A knowledge based approach for representing and reasoning about signaling networks. Bioinformatics, 20(suppl_1):15–22, 2004.
O. Bodenreider and R. Stevens. Bio-ontologies: current trends and future directions. Brief Bioinform, 7(3):256–274, Sep 2006.
A. Brazma. On the importance of standardisation in life sciences. Bioinformatics, 17(2):113–4, 2001. 21138228 1367-4803 Editorial.
L. Cardelli. Bioware languages. In A. Herbert and K. S. Jones, editors, Computer Systems: Theory, Technology, and Applications, pages 59–65. Springer, New York, 2005.
K. Cheung, P. Qi, D. Tuck, and M. Krauthammer. A semantic web approach to biological pathway data reasoning and integration. Journal of Web Semantics, 4:3, 2006.
T. Clark and J. Kinoshita. Alzforum and swan: the present and future of scientific web communities. Brief Bioinform, 8(3):163–171, May 2007.
E. Demir, O. Babur, U. Dogrusoz, A. Gursoy, A. Ayaz, G. Gulesir, G. Nisanci, and R. Cetin-Atalay. An ontology for collaborative construction and analysis of cellular pathways. Bioinformatics, 20(3):349–356, 2004.
N. Fedoroff, S. A. Racunas, and J. Shrager. Making biological computing smarter. The Scientist, 19(11):20–21, 2005.
C. Friedman, T. Borlawsky, L. Shagina, H. R. Xing, and Y. A. Lussier. Bio-ontology and text: bridging the modeling gap. Bioinformatics, 22(19): 2421–2429, Oct 2006.
N. Friedman, M. Linial, I. Nachman, and D. Pe’er. Using bayesian networks to analyze expression data. J Comput Biol, 7(3-4):601–20, 2000. 1066-5277 Journal Article.
Y. Gao, J. Kinoshita, E. Wu, E. Miller, R. Lee, A. Seaborne, S. Cayzer, and T. Clark. Swan: A distributed knowledge infrastructure for alzheimer disease research. Journal of Web Semantics, inpress, 2006.
D. K. Gifford. Blazing pathways through genetic mountains. Science, 293(5537):2049–51, 2001. 0036-8075 Journal Article.
B. M. Good and M. D. Wilkinson. The life sciences semantic web is full of creeps! Brief Bioinform, 7(3):275–286, Sep 2006.
A. J. Hartemink, D. K. Gifford, T. S. Jaakkola, and R. A. Young. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pac Symp Biocomput, pages 422–33, 2001. Journal Article Validation Studies.
M. Heiner. On exploiting the analysis power of petri nets for the validation of discrete event systems. In IMACS Symposium on Mathematical Modelling, pages 171–176, Wien, 1997.
Y. C. Ho. Special issue on discrete event dynamical systems: Editorial. Proc IEEE, 77(1):24–38, 1989.
M. Hucka, A. Finney, H. M. Sauro, H. Bolouri, J. C. Doyle, H. Kitano, A. P. Arkin, B. J. Bornstein, D. Bray, A. Cornish-Bowden, A. A. Cuellar, S. Dronov, E. D. Gilles, M. Ginkel, V. Gor, I. Goryanin, W. J. Hedley, T. C. Hodgman, J. H. Hofmeyr, P. J. Hunter, N. S. Juty, J. L. Kasberger, A. Kremling, U. Kummer, N. Le Novere, L. M. Loew, D. Lucio, P. Mendes, E. Minch, E. D. Mjolsness, Y. Nakayama, M. R. Nelson, P. F. Nielsen, T. Sakurada, J. C. Schaff, B. E. Shapiro, T. S. Shimizu, H. D. Spence, J. Stelling, K. Takahashi, M. Tomita, J. Wagner, and J. Wang. The systems biology markup language (sbml): a medium for representation and exchange of biochemical network models. Bioinformatics, 19(4):524–31, 2003. 1367-4803 Evaluation Studies Journal Article.
T. R. Hvidsten, A. Laegreid, and J. Komorowski. Learning rule-based models of biological process from gene expression time profiles using gene ontology. Bioinformatics, 19(9):1116–23, 2003. Evaluation Studies Journal Article Validation Studies.
P. Karp. An ontology for biological function based on molecular interactions. Bioinformatics, 16(3):269–85–, 2000.
P. Karp, C. Ouzounis, C. Moore-Kochlacs, L. Goldovsky, P. Kaipa, D. Ahren, S. Tsoka, N. Darzentas, V. Kunin, and N. Lopez-Bigas. Expansion of the biocyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res, 33(19):6083–9–, 2005.
P. D. Karp. Pathway databases: a case study in computational symbolic theories. Science, 293(5537):2040–4, 2001. 0036-8075 Journal Article.
V. Kashyap, A. Morales, and T. Hongsermeier. On implementing clinical decision support: achieving scalability and maintainability by combining business rules and ontologies. AMIA Annu Symp Proc, pages 414–418, 2006.
T. Kazic. Putting semantics into the semantic web: how well can it capture biology? Pac Symp Biocomput, pages 140–151, 2006.
P. Khatri and S. Draghici. Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics, 21(18):3587–95, 2005. 1367-4803 Journal Article.
A. Kuchinsky, K. Graham, D. Moh, M. Creech, K. Babaria, and A. Adler. Biological storytelling: a software tool for biological information organization based upon narrative structure. In Advanced Visual Interfaces, pages –, Trento, Italy, 2002.
H. Y. K. Lam, L. Marenco, T. Clark, Y. Gao, J. Kinoshita, G. Shepherd, P. Miller, E. Wu, G. T. Wong, N. Liu, C. Crasto, T. Morse, S. Stephens, and K.-H. Cheung. Alzpharm: integration of neurodegeneration data using rdf. BMC Bioinformatics, 8 Suppl 3:S4, 2007.
S. Liang, S. Fuhrman, and R. Somogyi. Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac. Symp. Biocomput., pages 18–29, 1998. in file.
J. P. Massar, M. Travers, J. Elhai, and J. Shrager. Biolingua: a programmable knowledge environment for biologists. Bioinformatics, 21(2):199–207, Jan 2005.
M. A. Musen. Scalable software architectures for decision support. Methods Inf Med, 38(4-5):229–238, Dec 1999.
E. Neumann. A life science semantic web: are we there yet? Sci STKE, 2005(283):pe22, 2005. 1525-8882 (Electronic) Journal Article Review.
E. Neumann. Biodash: A semantic web dashboard for drug development. In R. Altman, editor, Pacific Symposium in Biocomputing, volume 11, pages 176–187, Hawai, 2006.
M. J. O’Connor, D. L. Buckeridge, M. Choy, M. Crubezy, Z. Pincus, and M. A. Musen. Biostorm: a system for automated surveillance of diverse data sources. AMIA Annu Symp Proc, page 1071, 2003.
D. Pe’er, A. Regev, G. Elidan, and N. Friedman. Inferring subnetworks from perturbed expression profiles. Bioinformatics, 17(Suppl):S215–S224, 2001. 1367-4803 Journal article.
M. Peleg, S. Tu, A. Manindroo, and R. B. Altman. Modeling and analyzing biomedical processes using workflow/petri net models and tools. Medinfo, 2004:74–8, 2004. 1569-6332 Journal Article.
M. Peleg, I. Yeh, and R. B. Altman. Modelling biological processes using workflow and petri net models. Bioinformatics, 18(6):825–37, 2002. 22069932 1367-4803 Journal Article.
S. Racunas, C. Griffin, and N. Shah. A finite model theory for biological hypotheses. In Computational Systems Bioinformatics Conference, 2004, pages 585–589. IEEE, 2004. TY - CONF.
S. Racunas, N. Shah, and N. Fedoroff. A contradiction-based framework for testing gene regulation hypotheses. In Computational Systems Bioinformatics Conference, 2003, pages 634–638. IEEE, 2003. TY - CONF.
S. A. Racunas, N. H. Shah, I. Albert, and N. V. Fedoroff. Hybrow: a prototype system for computer-aided hypothesis evaluation. Bioinformatics, 20(suppl_1):257–264, 2004.
V. N. Reddy. Modeling biological pathways: A discrete event systems approach. Master’s thesis, University of Maryland, College Park, 1994.
V. N. Reddy, M. L. Mavrovouniotis, and M. N. Liebman. Petri net representations in metabolic pathways. Proc Int Conf Intell Syst Mol Biol, 1:328–36, 1993. 96038982 Journal Article.
C. Rosse and J. L. V. Mejino. A reference ontology for biomedical informatics: the foundational model of anatomy. J Biomed Inform, 36(6):478–500, Dec 2003.
D. L. Rubin, Y. Bashir, D. Grossman, P. Dev, and M. A. Musen. Using an ontology of human anatomy to inform reasoning with geometric models. Stud Health Technol Inform, 111:429–435, 2005.
D. L. Rubin, D. Grossman, M. Neal, D. L. Cook, J. B. Bassingthwaighte, and M. A. Musen. Ontology-based representation of simulation models of physiology. AMIA Annu Symp Proc, pages 664–668, 2006.
D. L. Rubin, S. E. Lewis, C. J. Mungall, S. Misra, M. Westerfield, M. Ashburner, I. Sim, C. G. Chute, H. Solbrig, M.-A. Storey, B. Smith, J. Day-Richter, N. F. Noy, and M. A. Musen. National center for biomedical ontology: advancing biomedicine through structured organization of scientific knowledge. OMICS, 10(2):185–198, 2006.
A. Ruttenberg, T. Clark, W. Bug, M. Samwald, O. Bodenreider, H. Chen, D. Doherty, K. Forsberg, Y. Gao, V. Kashyap, J. Kinoshita, J. Luciano, M. S. Marshall, C. Ogbuji, J. Rees, S. Stephens, G. T. Wong, E. Wu, D. Zaccagnini, T. Hongsermeier, E. Neumann, I. Herman, and K.-H. Cheung. Advancing translational research with the semantic web. BMC Bioinformatics, 8 Suppl 3:S2, 2007.
A. Rzhetsky, I. Iossifov, T. Koike, M. Krauthammer, P. Kra, M. Morris, H. Yu, P. A. Duboue, W. Weng, W. J. Wilbur, V. Hatzivassiloglou, and C. Friedman. Geneways: a system for extracting, analyzing, visualizing, and integrating molecular pathway data. J Biomed Inform, 37(1):43–53, 2004. 1532-0464 Journal Article.
A. Rzhetsky, T. Koike, S. Kalachikov, S. Gomez, M. Krauthammer, S. Kaplan, P. Kra, J. Russo, and C. Friedman. A knowledge model for analysis and simulation of regulatory networks. Bioinformatics, 16(12):1120–8–, 2000.
N. Shah and M. M.A. Which annotation did you mean? Technical Report SMI-2007-1247, Stanford Medical Informatics, May 2007.
N. H. Shah. Formal Methods for Genomic Data Integration. PhD thesis, The Pennsylvania State University, University Park, 2005.
N. H. Shah and N. V. Fedoroff. Clench: a program for calculating cluster enrichment using the gene ontology. Bioinformatics, 20(7):1196–7, 2004. 1367-4803 Journal Article.
J. Shrager, R. Waldinger, M. Stickel, and J. P. Massar. Deductive biocomputing. PLoS ONE, 2:e339, 2007.
B. Smith, W. Ceusters, B. Klagges, J. Khler, A. Kumar, J. Lomax, C. Mungall, F. Neuhaus, A. L. Rector, and C. Rosse. Relations in biomedical ontologies. Genome Biol, 6(5):R46, 2005.
R. Stevens, C. A. Goble, and S. Bechhofer. Ontology-based knowledge representation for bioinformatics. Brief Bioinform, 1(4):398–414, 2000. 21357582 1467-5463 Journal Article Review Review, Tutorial.
C. Talcott, S. Eker, M. Knapp, P. Lincoln, and K. Laderoute. Pathway logic modeling of protein functional domains in signal transduction. Pac Symp Biocomput, pages 568–80, 2004. Journal Article.
X. Wang, R. Gorlitsky, and J. S. Almeida. From xml to rdf: how semantic web technologies will change the design of ‘omic’ standards. Nat Biotechnol, 23(9):1099–1103, Sep 2005.
L. Yue and W. C. Reisdorf. Pathway and ontology analysis: emerging approaches connecting transcriptome data and clinical endpoints. Curr Mol Med, 5(1):11–21, Feb 2005.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Shah, N., Musen, M. (2009). Ontologies for Formal Representation of Biological Systems. In: Staab, S., Studer, R. (eds) Handbook on Ontologies. International Handbooks on Information Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92673-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-540-92673-3_20
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70999-2
Online ISBN: 978-3-540-92673-3
eBook Packages: Computer ScienceComputer Science (R0)