Computational methodologies for modelling, analysis and simulation of signalling networks

Briefings in Bioinformatics

Volumn 7, Issue 4, 2006, Pages 339-353

  Gilbert, David ^a   Fuß, Hendrik ^b   Gu, Xu ^b   Orton, Richard ^b   Robinson, Steve ^b   Vyshemirsky, Vladislav ^b   Kurth, Mary Jo ^b   Downes, C Stephen ^b   Dubitzky, Werner ^b  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

^b NONE

Author keywords

Analysis; Biochemical signalling networks; Modelling; Simulation

Indexed keywords

CELL SURFACE RECEPTOR; CYCLIN DEPENDENT KINASE; MITOGEN ACTIVATED PROTEIN KINASE;

BIOLOGICAL MODEL; CELL COMMUNICATION; CELL CYCLE REGULATION; CELL INTERACTION; COMPUTER ANALYSIS; COMPUTER MODEL; COMPUTER PROGRAM; COMPUTER SIMULATION; CONCEPTUAL FRAMEWORK; CYTOTOXICITY; DRUG RESEARCH; GENETIC DATABASE; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MOLECULAR DYNAMICS; PROTEIN FUNCTION; REVIEW; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY; WOUND HEALING;

ALGORITHMS; ANIMALS; CELL COMMUNICATION; CELL CYCLE; CELL PHYSIOLOGY; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; HUMANS; MODELS, BIOLOGICAL; RECEPTOR PROTEIN-TYROSINE KINASES; SIGNAL TRANSDUCTION; SOFTWARE; SYSTEMS BIOLOGY;

EID: 33845466196     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbl043     Document Type: Review

References (86)

1. Orton RJ, Sturm OE, Vyshemirsky V, et al. Computational modelling of the receptor-tyrosine-kinase-activated MAPK pathway. Biochem J 2005;392:249-61.
2. Vaudry D, Stork PJ, Lazarovici P, et al. Signaling pathways for PC12 cell differentiation: Making the right connections. Science 2002;296:1648-9.
3. Butcher JC. Numerical Methods for Ordinary Differential Equations. Wiley, Chichester, UK, 2003.
4. Huang CY, Ferrell JE, Jr. Ultrasensitivity in the mitogen-activated protein kinase cascade. Proc Nat Acad Sci 1996;93:10078-83.
5. Burack WR, Sturgill TW. The activating dual phosphorylation of MAPK by MEK is nonprocessive. Biochemistry 1997;36:5929-33.
6. Ferrell JE, Jr, Bhatt RR. Mechanistic studies of the dual phosphorylation of mitogen-activated protein kinase. J Biol Chem 1997;272:19008-16.
7. Kholodenko BN. Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. Eur J Biochem 2000;267:1583-8.
8. Schöberl B, Eichler-Jonsson C, Gilles ED, et al. Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat Biotechnol 2002;20:370-5.
9. Resat H, Ewald JA, Dixon DA, etal. An integrated model of epidermal growth factor receptor trafficking and signal transduction. BiophysJ 2003;85:730-43.
10. Brightman FA, Fell DA. Differential feedback regulation of the MAPK cascade underlies the quantitative differences in EGF and NGF signalling in PC12 cells. FEBS Lett 2000;482:169-74.
11. Sasagawa S, Ozaki Y, Fujita K, et al. Prediction and validation of the distinct dynamics of transient and sustained ERK activation. Nat Cell Biol 2005;7:365-73.
12. Cho KH, Shin SY, Kim HW, et al. Mathematical modeling of the influence of RKIP on the ERK signaling pathway. Lect Notes Comput Sc 2003;2602:127-41.
13. Pettinen A, Aho T, Smolander OP, et al. Simulation tools for biochemical networks: Evaluation of performance and usability. Bioinformatics 2005;21(3):357-63.
14. Mandel J, Palfreyman NM, Lopez JA, et al. Representing Bioinformatic Causality. Brief Bioinform 2004;5(3):270-283.
15. Mandel J, Palfreyman N, Dubitzky W. Modelling codependence in biological systems. IEE Proc Systems Biol 2007 (in press).
16. Kitano H, Funahashi A, Matsuoka Y, et al. Using process diagrams for the graphical representation of biological networks. Nat Biotechnol 2005;23:961-66.
17. Eungdamrong EJ, Iyengar R. Modeling cell signaling networks. Biol Cell 2004;96(5):355-62.
18. Phillips A, Cardelli LA. Graphical representation for the stochastic Pi-calculus. Proceedings of Concurrent Models in Molecular Biology 2005 (Bioconcur'05).
19. Calder M, Gilmore S, Hillston J. Modelling the influence of RKIP on the ERK signalling pathway using the stochastic process algebra PEPA. Trans Comp Systems Biol VII, Vol. 4230. Springer, 2006, pp. 1-23.
20. Calder M, Gilmore S, Hillston J. Automatically deriving ODEs from process algebra models of signalling pathways. In: Proceedings of Computational Methods in Systems Biology 2005;204-15.
21. Calder M, Vyshemirsky V, Gilbert D, et al. Analysis of Signalling Pathways using Continuous Time Markov Chains. Trans Comp Systems Biol VI, Vol. 4220. Springer, 2006, pp. 44-67.
22. Lee D-Y, Zimmer R, Lee S-Y, et al. Knowledge representation model for systems-level analysis of signal transduction networks. Genome Informatics 2004;15(2):234-43.
23. Gilbert DR, Heiner M. From Petri nets to differential equations - an integrative approach for biochemical network analysis, International Conference on Application and Theory of Petri Nets and other models of concurrency (ATPN06), LNCS 4024/2006. Springer, 2006, pp. 181-200.
24. Belousov BP. A periodic reaction and its mechanism. Sbornik Referatov po Radiatsonno Meditsine (Medgiz, Moscow) 1958;605-13.
25. Zhabotinsky AM. Periodic processes of malonic acid oxidation in a liquid phase. Biofizika 1964;9:306.
26. Murray JD. 1st edn. Mathematical Biology. Springer, London, UK, 1989.
27. Goldbeter A. Computational approaches to cellular rhythms. Nature 2002;420:238-45.
28. Ingolia NT, Murray AW. The ups and downs of modeling the cell cycle. Curr Biol 2004;14:R771-7.
29. Tyson JJ, Csikasz-Nagy A, Novák B. The dynamics of cell cycle regulation. Bioessays 2002;24(12):1095-109.
30. Fuß H, Dubitzky W, Downes CS, et al. Mathematical models of cell cycle regulation. Brief Bioinform 2005;6(2):163-77.
31. Goldbeter A. A minimal cascade model for the mitotic oscillator involving cyclin and cdc2 kinase. Proc Nat Acad Sci 1991;88:9107-11.
32. Tyson JJ. Modeling the cell division cycle: Cdc2 and cyclin interactions. Proc Nat Acad Sci 1991;88(16):7328-32.
33. Ciliberto A, Petrus MJ, Tyson JJ, et al. A kinetic model of the cyclin E/ Cdk2 developmental timer in Xenopus laevis embryos. Biophys Chem 2004;104(3):573-89.
34. Han JD, Bertin N, Hao T, et al. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 2004;430(6995):88-93.
35. Hartwell LH, Hopfield JJ, Leibler S, et al. From molecular to modular cell biology. Nature 1999;402(Suppl 6761):C47-52.
36. Bhalla US, Iyengar R. Emergent properties of networks of biological signaling pathways. Science 1999;283(5400):381-7.
37. Schmidt H, Jacobsen EW. Linear systems approach to analysis of complex dynamic behaviours in biochemical networks. IEE Proc Systems Biol 2004;1(1):149-58.
38. McAdams HH, Arkin A. It's a noisy business! Genetic regulation at the nanomolar scale. Trends Genet 1999;15(2):65-9.
39. Vilar JMG, Kueh HY, Barkai N, et al. Mechanisms of noise-resistance in genetic oscillators. Proc Nat Acad Sci 2002;99(9):5988-92.
40. Han Z, Yang L, MacLellan WR, et al. Hysteresis and cell cycle transitions: How crucial is it? Biophys J 2005;88:1626-34.
41. Pomerening JR, Sonta ED, Ferrell JE, Jr. Building a cell cycle oscillator: Hysteresis and bistability in the activation of Cdc2. Nat Cell Biol 2003;5(4):346-51.
42. Hodgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 1952;117(4):500-44.
43. Fuß H, Dubitzky W, Downes J, et al. Bistable switching and excitable behaviour in the activation of src at mitosis. Bioinformatics 2006;22(14):e158-65.
44. Chassagnole C, Jackson RC, Hussain N, et al. Using a mammalian cell cycle simulation to interpret differential kinase inhibition in antitumour pharmaceutical development. Biosystems 2006;83(2-3):91-7.
45. Nurse P. A long twentieth century of the cell cycle and beyond. Cell 2000;100(1):71-8.
46. Biron D, Alvarez-Lacalle E, Tlusty T, et al. Molecular model of the contractile ring. Phys Rev Lett 2005;95(9):098102.
47. Takahashi K, Kaizu K, Hu B, et al. A multialgorithm, multi-timescale method for cell simulation. Bioinformatics 2004;20(4):538-46.
48. Smallwood RH, Holcombe M. The Epitheliome Project: Multiscale Agent-Based Modeling of Epithelial Cell. 2006 IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2006.
49. McCulloch A, Huber G. 'In Silico' Simulation of Biological Processes. Wiley, 2002;4-20.
50. Noble D. Modeling the heart - from genes to cells to the whole organ. Science 2002;295:1678-82.
51. Wishart DS, Yang R, Arndt D, et al. Dynamic cellular automata: An alternative approach to cellular simulation. In Silico Biol 2005;5(2):139-61.
52. Broderick G, Rúaini M, Chan E, et al. A life-like virtual cell membrane using discrete automata. In Silico Biol 2004;5:0016.
53. Weiss G. Multiagent Systems: A Modern approach to distributed artificial intelligence. MIT Press, Boston, USA, 1999.
54. Walker DC, Hill G, Wood SM, et al. Agent-based computational modelling of wounded epithelial cell monolayers, IEEE Trans Nanobioscience 2004;3:153-63.
55. Walker DC, Southgate JS, Hill G, et al. The Epitheliome: Modeling the social behaviour of cells. Bio Systems 2004;76:89-100.
56. Walker DC, Sun T, Macneil S, et al. Modeling the effect of exogenous calcium on keratinocyte and HaCat cell proliferation and differentiation using an agent-based computational paradigm. Tissue Eng 2006;3:4.
57. Cheng G, Youssef BB, Markenscoff P, et al. Cell population dynamics modulate the rates of tissue growth processes. Biophys J 2006;90:713-24.
58. Chang I, Gilbert E, Elashberg N, et al. A three dimensional stochastic simulation of biofilm growth and transport related factors that affect structure. Microbiology 2003;149:2859-71.
59. Lee Y, Kouvroukoglou S, McIntire LV, et al. A cellular automaton model for the proliferation of migrating contact-inhibited cells. Biophys J 1995;69:1284-98.
60. Krull M, Voss N, Choi C, et al. 'TRANSPATH: An integrated database on signal transduction and a tool for array analysis', Nucl Acids Res 2003;31(1):97-100.
61. Lemer C, Naim A, Zhang Y, et al. AMAZE: A database of molecular function, interactions and biochemical processes, In: Proceedings of molecular informatics on confronting complexity, 13th-16th May, Bozen, Italy, 2002. Available at http://www.beilstein-institut.de/bozen2002/ proceedings/wodak/wodak.html.
62. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes, Nucl Acids Res 2000;28(1):27-30.
63. Schomburg I, Chang A, Hofmann O, et al. BRENDA: A resource for enzyme data and metabolic information. TRENDS Biochem Sci 2002;27(1):54-56.
64. Ji ZL, Chen X, Zhen CJ, et al. KDBI: Kinetic Data of Biomolecular Interactions database. Nucl Acids Res 2003;31(1):255-7.
65. Le Novere N, Bornstein B, Broicher A, et al. BioModels Database: A free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucl Acids Res 2006;34:D689-91.
66. Sivakumaran S, Hariharaputran S, Mishra J, et al. US The database of quantitative cellular signaling: Management and analysis of chemical kinetic model of signaling networks. Bioinformatics 2003;19(3):408-15.
67. Nielsen P, Hunter P, Bullivant D, et al. CellML Model Respository. 2004. Available at http://www.cellml.org/examples/respository/index.html.
68. Ullah M, Schmidt H, Cho KH, et al. Deterministic modeling and stochastic simulation of biochemical pathways using MATLAB, Systems Biology 2006;153:53-60.
69. Eusebius J. Doedel. Auto: A program for the automatic bifurcation analysis of autonomous systems. In: Proc 10th Manitoba Conf on Num Math and Comp, Univ. of Manitoba, Winnipeg, Canada, 1981;30:265-84.
70. Hoops S, Sahle S. Gauges R, et al. COPASI - a COmplex Pathway simulator. Bioinfomatics (advance access date, 10 October 2006).
71. Mendes P. GEPASI: A software package for modelling the dynamics, steady states and control of biochemical and other systems. Comput Appl Biosci 1993;9:563-571.
72. Loew LM, Schaff JC. The Virtual Cell: A software environment for computational cell biology. TRENDS Biotechnology 2001;19(10):401-6.
73. Sauro HM, Hucka M, Finney A, et al. Next generation simulation tools: the Systems Biology Workbench and BioSPICE integration, OMICS 2003;7(4):355-72.
74. Tomita M, Hashimoto K, Takahashi K, et al. E-Cell: Software environment for whole-cell simulation. Bioinformatics 1999;15(1):72-84.
75. Novere NL, Shimizu TS. STOCHSIM: Modelling of stochastic biomolecular processes. Bioinformatics 2001;17(6):575-6.
76. Hucka M, Finney A, Sauro HM, et al. The system biology markup language (SBML): A medium for representation and exchange of biochemical network models'. Bioinformatics 2003;19(4):524-31.
77. Roch S, Starke PH. Integrated Net Analyzer Version 2.2 Manual. 1998. Available at http://www2.informatik.hu-berlin.de/~starke/ina.html
78. Grahlmann B. The PEP Tool. In: Proceedings of the 9th international computer aided verification conference, 1997;440-443.
79. Zimmermann A, Freiheit J, German R, et al. Petri Net modelling and performability evaluation with TimeNET. In: 11th International conference of computer performance evaluation on modelling techniques and tools. Schaumburg, USA 2000;1786:188-202.
80. Rölke H, Heitmann F. Petri Nets Tool Database. Available at http://www.informatik.uni-hamburg.de/TGI/PetriNets/tools/db.html 2006.
81. Phillips A. The Stochastic Pi Machine (SPiM). 2006. Available at http://www.doc.ic.ac.uk/~anp/spim/.
82. Gillespie DT. Exact stochastic simulation of coupled chemical reactions. J Phys Chem-Us 1977;18(25):2340-62.
83. Calzone L, Fages F, Soliman S. 'BIOCHAM: An environment for modeling biological systems and formalizing experimental knowledge. Bioinformatics 2006;22:1805-07.
84. Baral C, Chancellor K, Tran NL, et al. A knowledge based approach for representing and reasoning about signaling networks. Bioinformatics 2004;20:i15-22.
85. Kwiatkowska MZ, Norman G, Parker DA. Probabilistic symbolic model checking with prism: A hybrid approach. University of Birmingham, School of Computer Science, CSR-01-10, October 2001.
86. Gilmore S, Hillston J. The PEPA workbench: A tool to support a process algebra-based approach to performance modelling. Lecture Notes in Computer Science 1994;794:353-68.