Biology Reference
In-Depth Information
Provided with rich information about biochemical reactions and gene regulation, availability of var-
ious biological databases, building an integrative model of the whole cell (virtual cell modelling) that
incorporates gene regulation, metabolic reactions and signal transductions is becoming a promising field
in the post-genomic era. Several projects have been established. The challenge created with Petri nets
is to understand how all the cellular proteins work collectively as a living system. Using powerful Petri
nets and computer techniques, data of metabolic pathways, gene regulatory networks and signalling
pathways could be converted for Petri net applications. Thus, a Petri net based virtual cell model could
be implemented, and the attempt to understand the logic of the cell could be accomplished.
ACKNOWLEDGEMENTS
The work was partly supported by the Ministry of Science and Art of the Government of Sachsen-
Anhalt, and by the German Research Foundation (DFG) graduate program “Bioinformatics” in the
Uni-Bielefeld.
REFERENCES
Baxevanis, D. A. (2003). The Molecular Biology Database Collection: 2003 update. Nucleic Acids Res. 31 , 1-12.
Charles, R. S. et al. (1997). The metabolic and Molecular Bases of Inherited Disease. McGraw-Hill Companies, Inc.
Chen, M. (2002). Modelling and Simulation of Metabolic Networks: Petri Nets Approach and Perspective. In : Modelling
and Simulation 2002: Proceedings of ESM2002, Amorski K. et al. (eds.), Darmstadt, pp. 441-444.
Chen, M., Freier, A., Koehler, J. and Rueegg, A. (2002). The Biology Petri Net Markup Language. In: Lecture Notes in
Informatics: Proceedings of Promise2002, Desel J. et al. (eds.), Potsdam, Vol. 21, pp. 150-161.
David, R. and Alla, H. (1992). Petri Nets and Grafcet - Tools for Modeling Discrete Event Systems. Prentice Hall.
Genrich, H. J. (1987). Predicate/Transition Nets. In: Lecture Notes in Computer Science, Vol. 254: Petri Nets: Central
Models and Their Properties, Advances in Petri Nets 1986, Part I, Proceedings of an Advanced Course, Brauer, W., Reisig,
W., Rozenberg, G. (eds.), Springer-Verlag, pp. 207-247.
Genrich, H., Kueffner, R. and Voss, K. (2001). Executable Petri Net Models for the Analysis of Metabolic Pathways.
International Journal on Software Tools for Technology Transfer 3 , 394-404.
Goss, P. J. E. and Peccoud, J. (1999). Analysis of the stabilizing effect of Rom on the genetic network controlling ColE1
plasmid replication. Pac. Symp. Biocomput. 4 , 65-76.
Hochstrasser, M. (1996). Protein degradation or regulation: Ub the judge. Cell 84 , 813-815.
Hofestaedt, R. and Thelen, S. (1998). Quantitative modeling of Biochemical Networks. In Silico Biol. 1 , 39-53.
Hofestaedt, R., Lautenbach, K. and Lange, M. (2000a). Modellierung und Simulation Metabolischer Netzwerke: DFG-
Workshop im Rahmen des DFG-Schwerpunktes Informatikmethoden zur Analyse und Interpretation gro?er genomischer
Datenmengen, Magdeburg, Mai 2000.
Hofestaedt, R., Mischke, U. and Scholz, U. (2000b). Knowledge acquisition, management and representation for the
diagnostic support in human inborn errors of metabolism.
In:
Medical Infobahn for Europe: Proceedings of MIE2000
and GMDS2000, Hasman, A. et al.
(eds.) Amsterdam, IOS Press, pp. 857-862.
Hucka, M., Finney, A., Sauro, H. and Bolouri, H. (2001). Systems Biology Markup Language (SBML) Level 1: Structures
and Facilities for Basic Model Definitions, pp. 30-33.
Kauert, R., Toepel, T., Scholz, U. and Hofestaedt, R. (2001). Information System for the Support of Research, Diagnosis
and Therapy of Inborn Metabolic Diseases.
In : MEDINFO 2001, V.Patel et al.
(eds), Amsterdam: IOS Press, pp. 353-356.
Kohn, M. and Letzkus, W. (1983). A Graph-theoretical Analysis of Metabolic Regulation. J. Theor. Biol.
100 , 293-304.
Kurt, J. (1997). Coloured Petri Nets - Basic Concepts, Analysis Methods and Practical Use, In: EATCS Monographs on
Theoretical Computer Science. 2nd edition, Berlin: Springer-Verlag.
Markram, H., Roth, A. and Helmchen, F. (1998).
Competitive calcium binding:
implications for dendritic calcium
signaling. J. Comput. Neurosci. 5 , 331-348.
Matsuno, H., Doi, A., Drath, R. and Miyano, S. (2001). Genomic Object Net: Basic Architecture for Representing and
Simulating Biopathways. In: RECOMB 2001, April 2001.
Matsuno, H., Doi, A., Nagasaki, M. and Miyano, S. (2000). Hybrid Petri net Representation of Gene Regulatory Network.
Pac. Symp. Biocomput. 5 , 338-349.
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