Cell illustrator 4.0: A computational platform for systems biology

In Silico Biology

Volumn 10, Issue 1-2, 2010, Pages 5-26

  Nagasaki, Masao ^a   Saito, Ayumu ^a   Jeong, Euna ^a   Li, Chen ^a   Kojima, Kaname ^a   Ikeda, Emi ^a   Miyano, Satoru ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

Biopathway; Cell Illustrator; CI; CSML; CSO; Java; JWS; Modeling; ODE; Ontology; Pathway database; Petri net; Simulation

Indexed keywords

ANIMAL; ARTICLE; BIOLOGICAL MODEL; CELLULAR, SUBCELLULAR AND MOLECULAR BIOLOGICAL PHENOMENA AND FUNCTIONS; COMPUTER INTERFACE; COMPUTER LANGUAGE; COMPUTER PROGRAM; COMPUTER SIMULATION; GENETIC TRANSCRIPTION; HUMAN; INTERNET; METABOLISM; NUCLEOTIDE SEQUENCE; SYSTEMS BIOLOGY;

ANIMALS; BASE SEQUENCE; CELL PHYSIOLOGICAL PROCESSES; COMPUTER SIMULATION; HUMANS; INTERNET; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; PROGRAMMING LANGUAGES; SOFTWARE; SYSTEMS BIOLOGY; TRANSCRIPTION, GENETIC; USER-COMPUTER INTERFACE;

EID: 77952066951     PISSN: 14343207     EISSN: None     Source Type: Journal    
DOI: 10.3233/ISB-2010-0415     Document Type: Article

References (54)

1. Nagasaki, M., Doi, A., Matsuno, H. and Miyano, S. (2003). Genomic Object Net: I. A platform for modeling and simulating biopathways. Appl. Bioinformatics 2, 181-184.
2. http://www.genomicobject.net/.
3. http://java.sun.com/javase/technologies/desktop/javawebstart/index.jsp.
4. Kato, M., Nagasaki, M., Doi, A. and Miyano, S. (2005). Automatic drawing of biological networks using cross cost and subcomponent data. Genome Inform. 16, 22-31.
5. Kojima, K., Nagasaki, M., Jeong, E., Kato, M. and Miyano, S. (2007). An efficient grid layout algorithm for biological networks utilizing various biological attributes. BMC Bioinformatics 8, 76.
6. Kojima, K., Nagasaki, M. and Miyano, S. (2008). Fast grid layout algorithm for biological networks with sweep calculation. Bioinformatics 24, 1433-1441.
7. Hashimoto, T. B., Nagasaki, M., Kojima, K. and Miyano, S. (2009) BFL: a node and edge betweenness based fast layout algorithm for large scale networks. Bioinformatics 10, 19.
8. http://www.csml.org/.
9. Jeong, E., Nagasaki, M., Saito, A. and Miyano, S. (2007) Cell System Ontology: Representation for modeling, visualizing, and simulating biological pathways. In Silico Biol. 7, 0055.
10. Alla, H. and David, R. (1998). Continuous and Hybrid Petri Nets. Journal of Circuits, Systems, and Computers 8, 159-188.
11. Matsuno, H., Doi, A., Nagasaki, M. and Miyano, S. (2000). Hybrid Petri net representation of gene regulatory network. Pac. Symp. Biocomput. 5, 341-352.
12. Nagasaki, M., Doi, A., Matsuno, H. and Miyano, S. (2004). A versatile Petri net based architecture for modeling and simulation of complex biological processes. Genome Inform. 15, 180-197.
13. Nagasaki, M., Saito, A., Doi, A., Matsuno, H. and Miyano, S. (2009). Foundations of Systems Biology: Using Cell Illustrator and Pathway Databases. Springer, Berlin Heidelberg.
14. Tomita, M. (2001). Whole-cell simulation: a grand challenge of the 21st century. Trends Biotechnol. 19, 205-210.
15. Mendes, P., (1993). GEPASI: a software for modeling the dynamics, steady states and control of biochemical and other systems. Comput. Appl. Biosci. 9, 563-571.
16. http://biospice.sourceforge.net/.
17. Nagasaki, M., Doi, A., Matsuno, H. and Miyano, S. (2005). Computational modeling of biological processes with Petri Net-based architecture. In: Bioinformatics Technologies, Chen, Y.-P. P. (ed.), Springer, pp. 179-242.
18. Troncale, S., Tahi, F., Campard, D.,Vannier, J.-P. and Guespin, J. (2006). Modeling and simulation with Hybrid Functional Petri Nets of the role of interleukin-6 in human early haematopoiesis. Pac. Symp. Biocomput. 11, 427-438.
19. Koh, G., Teong, H. F., Clément, M. V., Hsu, D. and Thiagarajan, P. S. (2006). A decompositional approach to parameter estimation in pathway modeling; a case study of the Akt and MAPK pathways and their crosstalk. Bioinformatics 22, e271-e280.
20. Hardy, S. and Robillard, P. N. (2008). Petri net-based method for the analysis of the dynamics of signal propagation in signaling pathways. Bioinformatics 24, 209-217.
21. Sato, Y., Hashiguchi, Y. and Nishida, M. (2009). Evolution of multiple phosphodiesterase isoforms in stickleback involved in cAMP signal transduction pathway. BMC Syst. Biol. 3, 23.
22. Wu, J. and Voit, E. (2009). Hybrid modeling in biochemical systems theory by means of functional petri nets. J. Bioinform. Comput. Biol. 1, 107-134.
23. Wu, J. and Voit, E. (2009). Integrative biological systems modeling: challenges and opportunities. Front. Comput. Sci. China 3, 92-100.
24. http://www.jgraph.com/jgraph.html.
25. Li, W. and Kurata, H. (2005). A grid layout algorithm for automatic drawing of biochemical networks. Bioinformatics 21, 2036-2042.
26. http://www.biopax.org/.
27. http://www.sbml.org/.
28. http://www.cellml.org/.
29. http://www.kegg.org/.
30. Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H. and Kanehisa, M. (1999). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 27, 29-34.
31. Nagasaki, M., Saito, A., Li, C., Jeong, E. and Miyano, S. (2008). Systematic reconstruction of TRANSPATH data into Cell System Markup Language. BMC Syst. Biol. 2, 53.
32. Jeong, E., Nagasaki, M. and Miyano, S. (2007). Conversion from BioPAXto CSO for System Dynamics and Visualization of Biological Pathway. Genome Inform. 18, 225-236.
33. http://www.csml.org/tools/sbml2csml/.
34. http://www.csml.org/tools/cellml2csml/.
35. Nagasaki, M., Doi, A., Matsuno, H. and Miyano, S. (2003). Recreating biopathway databases towards simulation. In: Computational Methods in Systems Biology, Miyano, S., Wolkenhauer, O., Degano, P., Danos, V., Lincoln, P. and Cho, K.-H. (eds.). Lecture Notes in Computer Science 2602, 168-169. (Pubitemid 36241654)
36. Le Novere N., et al. (2009). The Systems Biology Graphical Notation. Nat. Biotechnol. 27, 735-741.
37. Killcoyne, S., Carter, G. W., Smith, J. and Boyle, J. (2009). Cytoscape: a community-based framework for network modeling. Methods Mol. Biol. 563, 219-239.
38. http://www.w3.org/TR/owl-features/.
39. http://www.reactome.org/.
40. Li, C., Nagasaki, M., Ueno, K. and Miyano, S. (2009). Simulation-based model checking approach to cell fate specification during Caenorhabditis elegans vulval development by hybrid functional Petri net with extension. BMC Syst. Biol. 3, 42.
41. Jeong, E., Nagasaki, M. and Miyano, S. (2008). Rule-based reasoning for system dynamics in cell systems. Genome Inform. 20, 25-36.
42. Hoch, F., Kerr, M. and Griffith, A. (2001). Software as a Service: Strategic Backgrounder, SIIA eBusiness Division, Software & Industry. http://www.siia.net/estore/pubs/SSB-01.pdf.
43. Schacherer, F., Choi, C., Götze, U., Krull, M., Pistor, S. andWingender, E. (2001). The TRANSPATHsignal transduction database: a knowledge base on signal transduction networks, Bioinformatics 17, 1053-1057.
44. http://pnuts.org/.
45. http://www.csml.org/models/csml-models/vulvaldev/.
46. http://java.sun.com/.
47. Nagasaki, M., Yamaguchi, R., Yoshida, R., Imoto, S., Doi, A., Tamada, Y., Matsuno, H., Miyano, S. and Higuchi, T. (2006). Genomic data assimilation for estimating hybrid functional Petri net from time-course gene expression data. Genome Inform. 17, 46-61.
48. Tasaki S., Nagasaki M., Oyama M., Hata H., Ueno K., Yoshida R., Higuchi T., Sugano S. and Miyano S. (2006). Modeling and estimation of dynamic EGFR pathway by data assimilation approach using time series proteomic data. Genome Inform. 17, 226-238.
49. Chen, D., Zebiak, S. E., Busalacchi, A. J. and Cane, M. A. (1995). An improved procedure for El Niño forecasting: Implications for predictability. Science 268, 1699-1702.
50. http://www.w3.org/Graphics/SVG/.
51. http://www.inkscape.org/.
52. http://www.biomodels.net/.
53. http://genome.ib.sci.yamaguchi-u.ac.jp/gon/.
54. http://www.jython.org/.