nach oben

Erschienen in:

2009 | OriginalPaper | Buchkapitel

Ontologies for Formal Representation of Biological Systems

verfasst von : Nigam Shah, Mark Musen

Erschienen in: Handbook on Ontologies

Verlag: Springer Berlin Heidelberg

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Using the PSL Ontology

Nächstes Kapitel Ontologies for Cultural Heritage

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

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.

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.

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.

Source public domain, non copy righted image.

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.

http://www.w3.org/2001/sw/SW-FAQ

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.CrossRef

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.CrossRef

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.CrossRef

O. Bodenreider and R. Stevens. Bio-ontologies: current trends and future directions. Brief Bioinform, 7(3):256–274, Sep 2006.CrossRef

A. Brazma. On the importance of standardisation in life sciences. Bioinformatics, 17(2):113–4, 2001. 21138228 1367-4803 Editorial.CrossRef

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.CrossRef

T. Clark and J. Kinoshita. Alzforum and swan: the present and future of scientific web communities. Brief Bioinform, 8(3):163–171, May 2007.CrossRef

10.

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.CrossRef

11.

N. Fedoroff, S. A. Racunas, and J. Shrager. Making biological computing smarter. The Scientist, 19(11):20–21, 2005.

12.

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.CrossRef

13.

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.CrossRef

14.

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.

15.

D. K. Gifford. Blazing pathways through genetic mountains. Science, 293(5537):2049–51, 2001. 0036-8075 Journal Article.CrossRef

16.

B. M. Good and M. D. Wilkinson. The life sciences semantic web is full of creeps! Brief Bioinform, 7(3):275–286, Sep 2006.CrossRef

17.

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.

18.

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.

19.

Y. C. Ho. Special issue on discrete event dynamical systems: Editorial. Proc IEEE, 77(1):24–38, 1989.MathSciNetCrossRef

20.

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.CrossRef

21.

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.CrossRef

22.

P. Karp. An ontology for biological function based on molecular interactions. Bioinformatics, 16(3):269–85–, 2000.CrossRef

23.

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.CrossRef

24.

P. D. Karp. Pathway databases: a case study in computational symbolic theories. Science, 293(5537):2040–4, 2001. 0036-8075 Journal Article.CrossRef

25.

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.

26.

T. Kazic. Putting semantics into the semantic web: how well can it capture biology? Pac Symp Biocomput, pages 140–151, 2006.

27.

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.CrossRef

28.

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.

29.

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.

30.

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.

31.

J. P. Massar, M. Travers, J. Elhai, and J. Shrager. Biolingua: a programmable knowledge environment for biologists. Bioinformatics, 21(2):199–207, Jan 2005.CrossRef

32.

M. A. Musen. Scalable software architectures for decision support. Methods Inf Med, 38(4-5):229–238, Dec 1999.

33.

E. Neumann. A life science semantic web: are we there yet? Sci STKE, 2005(283):pe22, 2005. 1525-8882 (Electronic) Journal Article Review.

34.

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.

35.

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.

36.

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.CrossRef

37.

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.

38.

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.CrossRef

39.

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.

40.

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.

41.

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.CrossRef

42.

V. N. Reddy. Modeling biological pathways: A discrete event systems approach. Master’s thesis, University of Maryland, College Park, 1994.CrossRef

43.

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.

44.

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.CrossRef

45.

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.

46.

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.

47.

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.CrossRef

48.

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.

49.

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.CrossRef

50.

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.CrossRef

51.

N. Shah and M. M.A. Which annotation did you mean? Technical Report SMI-2007-1247, Stanford Medical Informatics, May 2007.

52.

N. H. Shah. Formal Methods for Genomic Data Integration. PhD thesis, The Pennsylvania State University, University Park, 2005.

53.

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.CrossRef

54.

J. Shrager, R. Waldinger, M. Stickel, and J. P. Massar. Deductive biocomputing. PLoS ONE, 2:e339, 2007.

55.

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.CrossRef

56.

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.CrossRef

57.

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.

58.

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.CrossRef

59.

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.CrossRef

Titel: Ontologies for Formal Representation of Biological Systems
verfasst von: Nigam Shah
Mark Musen
Verlag: Springer Berlin Heidelberg
Buch: Handbook on Ontologies
Print ISBN: 978-3-540-70999-2

Electronic ISBN: 978-3-540-92673-3

Copyright-Jahr: 2009
DOI: https://doi.org/10.1007/978-3-540-92673-3_20

Springer Professional

Summary

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"