Computational systems biology of the cell cycle

Briefings in Bioinformatics

Volumn 10, Issue 4, 2009, Pages 424-434

  Csikász Nagy, Attila ^a  

^a UNIVERSITY OF TRENTO   (Italy)

Author keywords

Cell cycle; Computational modeling; Historical review; Perspectives; Systems biology

Indexed keywords

ALGORITHM; BIOINFORMATICS; CELL CYCLE REGULATION; INFORMATION PROCESSING; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; REVIEW; SYSTEMS BIOLOGY;

ANIMALS; CELL CYCLE; CELL CYCLE PROTEINS; COMPUTATIONAL BIOLOGY; HISTORY, 20TH CENTURY; MODELS, BIOLOGICAL; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY;

EUKARYOTA;

EID: 67449152160     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbp005     Document Type: Review

References (204)

1. Brooks RF, Bennett DC, Smith JA. Mammalian cell cycles need two random transitions. Cell 1980;19:493-504.
2. Castor LN. A G1 rate model accounts for cell-cycle kinetics attributed to 'transition probability'. Nature 1980; 287:857-59.
3. Koch AL, Schaechter M. A model for statistics of the cell division process. J Gen Microbiol 1962;29:435-54.
4. Koch AL. Does the variability of the cell cycle result from one or many chance events? Nature 1980;286:80-2.
5. Shields R. Transition probability and the origin of variation in the cell cycle. Nature 1977;267:704-7.
6. Smith JA, Martin L. Do cells cycle? Proc Natl Acad Sci USA 1973; 70:1263-67.
7. Tyson JJ. Unstable activator models for size control of the cell cycle. J Theor Biol 1983;104:617-31.
8. Tyson JJ, Hannsgen KB. Cell growth and division: A deterministic/ probabilistic model of the cell cycle. J Math Biol 1986;23 231-46.
9. Li F, Long T, Lu Y, et al. The yeast cell-cycle network is robustly designed. Proc Natl Acad Sci USA 2004;101: 4781-86.
10. Braunewell S, Bornholdt S. Superstability of the yeast cell-cycle dynamics: Ensuring causality in the presence of biochemical stochasticity. J Theor Biol 2007;245:638-43.
11. Stoll G, Rougemont J, Naef F. Few crucial links assure checkpoint efficiency in the yeast cell-cycle network. Bioinformatics 2006; 22:2539-46.
12. Okabe Y, Sasai M. Stable stochastic dynamics in yeast cell cycle. Biophys J 2007;93:3451-59.
13. Han B, Wang J. Quantifying robustness and dissipation cost of yeast cell cycle network: The funneled energy landscape perspectives. Biophys J 2007;92:3755-63.
14. Davidich MI, Bornholdt S. Boolean network model predicts cell cycle sequence of fission yeast. PLoS ONE 2008;3:e1672.
15. Faure A, Naldi A, Chaouiya C, et al. Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle. Bioinformatics 2006;22:e124-31.
16. Brazhnik P, Tyson JJ. Cell cycle control in bacteria and yeast: A case of convergent evolution? Cell Cycle 2006;5:522-9.
17. Li S, Brazhnik P, Sobral B, et al. A quantitative study of the division cycle of Caulobacter crescentus stalked cells. PLoS Comput Biol 2008;4:e9.
18. Shen X, Collier J, Dill D, et al. Architecture and inherent robustness of a bacterial cell-cycle control system. Proc Natl Acad Sci USA 2008;105:11340-5.
19. Barberis M, Klipp E, Vanoni M, et al. Cell size at S phase initiation: An emergent property of the G1/S network. PLoS Comput Biol 2007;3:e64.
20. Battogtokh D, Tyson JJ. Bifurcation analysis of a model of the budding yeast cell cycle. Chaos 2004;14:653-61.
21. Chen KC, Csikasz-Nagy A, Gyorffy B, et al. Kinetic analysis of a molecular model of the budding yeast cell cycle. Mol Biol Cell 2000;11:369-91.
22. Chen KC, Calzone L, Csikasz-Nagy A, et al. Integrative analysis of cell cycle control in budding yeast. Mol Biol Cell 2004;15 3841-62.
23. Ciliberto A, Novak B, Tyson JJ. Mathematical model of the morphogenesis checkpoint in budding yeast. J Cell Biol 2003;163:1243-54.
24. Cross FR. Two redundant oscillatory mechanisms in the yeast cell cycle. Dev Cell 2003;4:741-52.
25. Lovrics A, Csikasz-Nagy A, Zsely IG, et al. Time scale and dimension analysis of a budding yeast cell cycle model. BMC Bioinformatics 2006;7:494.
26. Sriram K, Bernot G, Kepes F. A minimal mathematical model combining several regulatory cycles from the budding yeast cell cycle. IET Syst Biol 2007;1:326-41.
27. Stelling J, Gilles ED. Mathematical modeling of complex regulatory networks. IEEE Trans Nanobioscience 2004;3: 172-9.
28. Thornton BR, Chen KC, Cross FR, et al. Cycling without the cyclosome: Modeling a yeast strain lacking the APC. Cell Cycle 2004;3:629-33.
29. Lovrics A, Zsély IG, Csikász-Nagy A, et al. Analysis of a budding yeast cell cycle model using the shapes of local sensitivity functions. International Journal of Chemical Kinetics 2008;40 710-20.
30. Csikasz-Nagy A, Kapuy O, Gyorffy B, et al. Modeling the septation initiation network (SIN) in fission yeast cells. Curr Genet 2007;51:245-55.
31. Lygeros J, Koutroumpas K, Dimopoulos S, et al. Stochastic hybrid modeling of DNA replication across a complete genome. Proc Natl Acad Sci USA 2008;105:12295-300.
32. Novak B, Tyson JJ. Quantitative analysis of a molecular model of mitotic control in fission yeast. J Theor Biol 1995; 173:283-305.
33. Novak B, Tyson JJ. Modeling the control of DNA replication in fission yeast. Proc Natl Acad Sci USA 1997;94: 9147-52.
34. Novak B, Csikasz-Nagy A, Gyorffy B, et al. Mathematical model of the fission yeast cell cycle with checkpoint controls at the G1/S, G2/M and metaphase/anaphase transitions. Biophys Chem 1998;72 185-200.
35. Novak B, Pataki Z, Ciliberto A, et al. Mathematical model of the cell division cycle of fission yeast. Chaos 2001;11:277-86.
36. Sveiczer A, Csikasz-Nagy A, Gyorffy B, et al. Modeling the fission yeast cell cycle: Quantized cycle times in wee1-cdc25Delta mutant cells. Proc Natl Acad Sci USA 2000;97: 7865-70.
37. Vasireddy R, Biswas S. Modeling Gene Regulatory Network in Fission Yeast Cell Cycle Using Hybrid Petri Nets. Neural Information Processing 2004;1310-15.
38. Borisuk MT, Tyson JJ. Bifurcation analysis of a model of mitotic control in frog eggs. J Theor Biol 1998;195:69-85.
39. Ciliberto A, Petrus MJ, Tyson JJ, et al. A kinetic model of the cyclin E/Cdk2 developmental timer in Xenopus laevis embryos. Biophys Chem 2003;104:573-89.
40. Ciliberto A, Lukacs A, Toth A, et al. Rewiring the exit from mitosis. Cell Cycle 2005;4:1107-12.
41. Marlovits G, Tyson CJ, Novak B, et al. Modeling M-phase control in Xenopus oocyte extracts: The surveillance mechanism for unreplicated DNA. Biophys Chem 1998;72: 169-84.
42. Norel R, Agur Z. A model for the adjustment of the mitotic clock by cyclin and MPF levels. Science 1991;251: 1076-8.
43. Novak B, Tyson JJ. Numerical analysis of a comprehensive model of M-phase control in Xenopus oocyte extracts and intact embryos. J Cell Sci 1993;106(Pt 4):1153-68.
44. Zwolak JW, Tyson JJ, Watson LT. Parameter estimation for a mathematical model of the cell cycle in frog eggs. J Comput Biol 2005;12 48-63.
45. Calzone L, Thieffry D, Tyson JJ, et al. Dynamical modeling of syncytial mitotic cycles in Drosophila embryos. Mol Syst Biol 2007;3:131.
46. Ciliberto A, Tyson JJ. Mathematical model for early development of the sea urchin embryo. Bull Math Biol 2000;62:37-59.
47. Aguda BD. Instabilities in phosphorylation-dephosphorylation cascades and cell cycle checkpoints. Oncogene 1999;18: 2846-51.
48. Alarcon T, Byrne HM, Maini PK. A mathematical model of the effects of hypoxia on the cell-cycle of normal and cancer cells. J Theor Biol 2004;229:395-411.
49. Bai S, Goodrich D, Thron CD, et al. Theoretical and experimental evidence for hysteresis in cell proliferation. Cell Cycle 2003; 2:46-52.
50. Hatzimanikatis V, Lee KH, Bailey JE. A mathematical description of regulation of the G1-S transition of the mammalian cell cycle. Biotechnol Bioeng 1999;65:631-7.
51. Iwamoto K, Tashima Y, Hamada H, et al. Mathematical modeling and sensitivity analysis of G1/S phase in the cell cycle including the DNA-damage signal transduction pathway. Biosystems 2008;94 109-17.
52. Kohn KW. Functional capabilities of molecular network components controlling the mammalian G1/S cell cycle phase transition. Oncogene 1998;16:1065-75.
53. Novak B, Tyson JJ. A model for restriction point control of the mammalian cell cycle. J Theor Biol 2004;230: 563-79.
54. Obeyesekere MN, Herbert JR, Zimmerman SO. A model of the G1 phase of the cell cycle incorporating cyclinE/cdk2 complex and retinoblastoma protein. Oncogene 1995;11: 1199-205.
55. Obeyesekere MN, Knudsen ES, Wang JY, et al. A mathematical model of the regulation of the G1 phase of Rb+/+ amd Rb-/- mouse embryonic fibroblasts and an osteosarcoma cell line. Cell Prolif 1997;30 171-94.
56. Pfeuty B, David-Pfeuty T, Kaneko K. Underlying principles of cell fate determination during G1 phase of the mammalian cell cycle. Cell Cycle 2008;7:3246-57.
57. Qu Z, Weiss JN, MacLellan WR. Regulation of the mammalian cell cycle: A model of the G1-to-S transition. Am J Physiol Cell Physiol 2003; 284:C349-64.
58. Swat M, Kel A, Herzel H. Bifurcation analysis of the regulatory modules of the mammalian G1/S transition. Bioinformatics 2004;20 1506-11.
59. Yang L, Han Z, Robb MacLellan W, et al. Linking cell division to cell growth in a spatiotemporal model of the cell cycle. J Theor Biol 2006;241:120-33.
60. Chassagnole C, Jackson RC, Hussain N, et al. Using a mammalian cell cycle simulation to interpret differential kinase inhibition in anti-tumour pharmaceutical development. Biosystems 2006;83 91-7.
61. Basse B, Baguley BC, Marshall ES, et al. A mathematical model for analysis of the cell cycle in cell lines derived from human tumors. J Math Biol 2003;47:295-312.
62. Collier JR, McInerney D, Schnell S, etal. A cell cycle model for somitogenesis: Mathematical formulation and numerical simulation. J Theor Biol 2000;207:305-16.
63. Aguda BD. A quantitative analysis of the kinetics of the G2 DNA damage checkpoint system. Proc Natl Acad Sci USA 1999;96:11352-7.
64. Csikasz-Nagy A, Battogtokh D, Chen KC, et al. Analysis of a generic model of eukaryotic cell-cycle regulation. Biophys J 2006;90:4361-79.
65. Goldbeter A. A minimal cascade model for the mitotic oscillator involving cyclin and cdc2 kinase. Proc Natl Acad Sci USA 1991; 88:9107-11.
66. Gonze D, Goldbeter A. A model for a network of phosphorylation-dephosphorylation cycles displaying the dynamics of dominoes and clocks. J Theor Biol 2001;210: 167-86.
67. Novak B, Csikasz-Nagy A, Gyorffy B, et al. Model scenarios for evolution of the eukaryotic cell cycle. Philos Trans R Soc Lond B Biol Sci 1998;353:2063-76.
68. Pfeuty B, Kaneko K. Minimal requirements for robust cell size control in eukaryotic cells. Phys Biol 2007;4:194-204.
69. Qu Z, MacLellan WR, Weiss JN. Dynamics of the cell cycle: Checkpoints, sizers, and timers. Biophys J 2003;85: 3600-11.
70. Qu ZL, Weiss JN, MacLellan WR. Coordination of cell growth and cell division: A mathematical modeling study. J Cell Sci 2004;117 4199-207.
71. Thron CD. Mathematical analysis of a model of the mitotic clock. Science 1991;254:122-3.
72. Thron CD. Bistable biochemical switching and the control of the events of the cell cycle. Oncogene 1997;15: 317-25.
73. Tyson JJ. Modeling the cell division cycle: Cdc2 and cyclin interactions. Proc Natl Acad Sci USA 1991;88:7328-32.
74. Yang L, MacLellan WR, Han Z, et al. Multisite phosphorylation and network dynamics of cyclin-dependent kinase signaling in the eukaryotic cell cycle. Biophys J 2004;86: 3432-43.
75. Gardner TS, Dolnik M, Collins JJ. A theory for controlling cell cycle dynamics using a reversibly binding inhibitor. Proc Natl Acad Sci USA 1998;95:14190-5.
76. Mura I, Csikasz-Nagy A. Stochastic Petri Net extension of a yeast cell cycle model. J Theor Biol 2008;254:850-60.
77. Sabouri-Ghomi M, Ciliberto A, Kar S, et al. Antagonism and bistability in protein interaction networks. J Theor Biol 2008; 250:209-18.
78. Sveiczer A, Tyson JJ, Novak B. A stochastic, molecular model of the fission yeast cell cycle: Role of the nucleocytoplasmic ratio in cycle time reguation. Biophys Chem 2001;92:1-15.
79. Steuer R. Effects of stochasticity in models of the cell cycle: From quantized cycle times to noise-induced oscillations. J Theor Biol 2004;228:293-301.
80. Zamborszky J, Hong CI, Csikasz Nagy A. Computational analysis of mammalian cell division gated by a circadian clock: Quantized cell cycles and cell size control. J Biol Rhythms 2007;22:542-53.
81. Lecca P, Priami C. Cell cycle control in eukaryotes: A BioSpi model. Electron Notes Theoret Comput Sci 2007;180: 51-63.
82. Kitano H. Computational systems biology. Nature 2002;420: 206-10.
83. Nurse P. Universal control mechanism regulating onset of M-phase. Nature 1990;344:503-8.
84. Gilbert D, Fuss H, Gu X, et al. Computational methodologies for modelling, analysis and simulation of signalling networks. Brief Bioinform 2006;7:339-53.
85. Karlebach G, Shamir R. Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 2008;9: 770-80.
86. Morgan DO. The Cell Cycle: Principles of Control. New Science Press: London, 2006.
87. Nurse P. A long twentieth century of the cell cycle and beyond. Cell 2000;100:71-8.
88. Tyers M. Cell cycle goes global. Curr Opin Cell Biol 2004;16 602-13.
89. Csikasz-Nagy A, Novak B, Tyson JJ. Reverse engineering models of cell cycle regulation. Adv Exp Med Biol 2008;641: 88-97.
90. Fuss H, Dubitzky W, Downes CS, et al. Mathematical models of cell cycle regulation. Brief Bioinform 2005;6: 163-77.
91. Ingolia NT, Murray AW. The ups and downs of modeling the cell cycle. Curr Biol 2004;14:R771-7.
92. Sible JC, Tyson JJ. Mathematical modeling as a tool for investigating cell cycle control networks. Methods 2007;41: 238-47.
93. Tyson JJ, Novak B. Temporal organization of the cell cycle. Curr Biol 2008;18:R759-R768.
94. Hartwell LH, Weinert TA. Checkpoints: Controls that ensure the order of cell cycle events. Science 1989;246: 629-34.
95. Prescott DM. Relation between growth rate and cell division. III. Changes in nuclear volume and growth rate and prevention of cell division in Amoeba proteus resulting from cytoplasmic amputations. Exp Cell Res 1956;11:94-8.
96. Pomerening JR, Sontag ED, Ferrell JE Jr. Building a cell cycle oscillator: Hysteresis and bistability in the activation of Cdc2. Nat Cell Biol 2003;5:346-51.
97. Sha W, Moore J, Chen K, et al. Hysteresis drives cell-cycle transitions in Xenopus laevis egg extracts. Proc Natl Acad Sci USA 2003;100:975-80.
98. Cross FR, Archambault V, Miller M, et al. Testing a mathematical model for the yeast cell cycle. Mol Biol Cell 2002;13:52-70.
99. Queralt E, Lehane C, Novak B, et al. Downregulation of PP2A(Cdc55) phosphatase by separase initiates mitotic exit in budding yeast. Cell 2006;125:719-32.
100. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000;100 57-70.
101. Novak B, Tyson JJ, Gyorffy B, et al. Irreversible cell-cycle transitions are due to systems-level feedback. Nat Cell Biol 2007;9:724-8.
102. Holt LJ, Krutchinsky AN, Morgan DO. Positive feedback sharpens the anaphase switch. Nature 2008;454: 353-7.
103. Pomerening JR, Kim SY, Ferrell JE Jr. Systems-level dissection of the cell-cycle oscillator: Bypassing positive feedback produces damped oscillations. Cell 2005;122: 565-78.
104. Skotheim JM, Di Talia S, Siggia ED, et al. Positive feedback of G1 cyclins ensures coherent cell cycle entry. Nature 2008; 454 291-6.
105. Thieffry D. Dynamical roles of biological regulatory circuits. Brief Bioinform 2007;8:220-5.
106. Gillespie DT. Stochastic simulation of chemical kinetics. Annu Rev Phys Chem 2007;58:35-55.
107. Bean JM, Siggia ED, Cross FR. Coherence and timing of cell cycle start examined at single-cell resolution. Mol Cell 2006;21:3-14.
108. Di Talia S, Skotheim JM, Bean JM, et al. The effects of molecular noise and size control on variability in the budding yeast cell cycle. Nature 2007;448:947-51.
109. Flory MR, Lee H, Bonneau R, et al. Quantitative proteomic analysis of the budding yeast cell cycle using acid-cleavable isotope-coded affinity tag reagents. Proteomics 2006;6:6146-57.
110. Ho Y, Gruhler A, Heilbut A, et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 2002;415:180-3.
111. Chi A, Huttenhower C, Geer LY, et al. Analysis of phosphorylation sites on proteins from Saccharomyces cerevisiae by electron transfer dissociation (ETD) mass spectrometry. Proc Natl Acad Sci USA 2007;104:2193-8.
112. Chi Y, Welcker M, Hizli AA, et al. Identification of CDK2 substrates in human cell lysates. Genome Biol 2008;9: R149.
113. Ptacek J, Devgan G, Michaud G, et al. Global analysis of protein phosphorylation in yeast. Nature 2005;438:679-84.
114. Gauthier NP, Larsen ME, Wernersson R, et al. Cyclebase.org - a comprehensive multi-organism online database of cell-cycle experiments. Nucleic Acids Res 2008; 36:D854-9.
115. Breitkreutz BJ, Stark C, Reguly T, et al. The BioGRID Interaction Database: 2008 update. Nucleic Acids Res 2008; 36:D637-40.
116. von Mering C, Jensen LJ, Snel B, et al. STRING: Known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res 2005;33: D433-7.
117. Linding R, Jensen LJ, Ostheimer GJ, et al. Systematic discovery of in vivo phosphorylation networks. Cell 2007; 129:1415-26.
118. Calzone L, Gelay A, Zinovyev A, et al. A comprehensive modular map of molecular interactions in RB/E2F pathway. Mol Syst Biol 2008; 4:173.
119. Kohn KW. Molecular interaction map of the mammalian cell cycle control and DNA repair systems. Mol Biol Cell 1999;10:2703-34.
120. Cho RJ, Campbell MJ, Winzeler EA, et al. A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell 1998;2:65-73.
121. Spellman PT, Sherlock G, Zhang MQ, et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 1998;9 3273-97.
122. Chang WC, Li CW, Chen BS. Quantitative inference of dynamic regulatory pathways via microarray data. BMC Bioinformatics 2005;6:44.
123. Chen HC, Lee HC, Lin TY, et al. Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle. Bioinformatics 2004;20:1914-27.
124. Cokus S, Rose S, Haynor D, et al. Modelling the network of cell cycle transcription factors in the yeast Saccharomyces cerevisiae. BMC Bioinformatics 2006;7:381.
125. Tsai HK, Lu HH, Li WH. Statistical methods for identifying yeast cell cycle transcription factors. Proc Natl Acad Sci USA 2005;102 13532-7.
126. Simon I, Barnett J, Hannett N, et al. Serial regulation of transcriptional regulators in the yeast cell cycle. Cell 2001; 106:697-708.
127. Cheng C, Li LM. Systematic identification of cell cycle regulated transcription factors from microarray time series data. BMC Genomics 2008;9:116.
128. Wu WS, Li WH. Systematic identification of yeast cell cycle transcription factors using multiple data sources. BMC Bioinformatics 2008;9:522.
129. Jensen LJ, Saric J, Bork P. Literature mining for the biologist: From information retrieval to biological discovery. Nat Rev Genet 2006;7:119-29.
130. Roberts PM. Mining literature for systems biology. Brief Bioinform 2006;7:399-406.
131. Hillenmeyer ME, Fung E, Wildenhain J, et al. The chemical genomic portrait of yeast: Uncovering a phenotype for all genes. Science 2008;320:362-5.
132. Tong AH, Evangelista M, Parsons AB, et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 2001;294:2364-8.
133. Tong AH, Lesage G, Bader GD, et al. Global mapping of the yeast genetic interaction network. Science 2004;303: 808-13.
134. Moriya H, Shimizu-Yoshida Y, Kitano H. In vivo robustness analysis of cell division cycle genes in Saccharomyces cerevisiae. PLoS Genet 2006;2:e111.
135. Sopko R, Huang D, Preston N, et al. Mapping pathways and phenotypes by systematic gene overexpression. Mol Cell 2006;21 319-30.
136. Kitano H, Funahashi A, Matsuoka Y, et al. Using process diagrams for the graphical representation of biological networks. Nat Biotechnol 2005;23:961-6.
137. Hucka M, Finney A, Sauro HM, et al. The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models. Bioinformatics 2003;19:524-31.
138. 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. Nucleic Acids Res 2006;34:D689-91.
139. Olivier BG, Snoep JL. Web-based kinetic modelling using JWS Online. Bioinformatics 2004;20:2143-4.
140. Alfieri R, Merelli I, Mosca E, et al. A data integration approach for cell cycle analysis oriented to model simulation in systems biology. BMC Syst Biol 2007;1:35.
141. Dematte L, Priami C, Romanel A. The Beta Workbench: A computational tool to study the dynamics of biological systems. Brief Bioinform 2008;9:437-49.
142. Ermentrout B. Simulating, Analyzing, and Animating Dynamical Systems: A Guide to XPPAUT for Researchers and Students. Philadelphia: SIAM, 2002.
143. Schmidt H, Jirstrand M. Systems biology toolbox for MATLAB: A computational platform for research in systems biology. Bioinformatics 2006;22:514-5.
144. Vass M, Allen N, Shaffer CA, et al. the JigCell model builder and run manager. Bioinformatics 2004;20: 3680-1.
145. Panning T, Watson L, Allen N, et al. Deterministic parallel global parameter estimation for a model of the budding yeast cell cycle. Journal of Global Optimization 2008;40: 719-38.
146. Hoops S, Sahle S, Gauges R, et al. COPASI-a COmplex PAthway SImulator. Bioinformatics 2006;22: 3067-74.
147. Lecca P, Palmisano A, Priami C. A new probabilistic generative model of parameter inference in biochemical networks Proceedings of the 2009 ACM Symposium on Applied Computing. Association for Computing Machinery, New York, 2009.
148. Saez-Rodriguez J, Goldsipe A, Muhlich J, et al. Flexible informatics for linking experimental data to mathematical models via DataRail. Bioinformatics 2008;24:840-7.
149. Zi Z, Klipp E. SBML-PET: A systems biology markup language-based parameter estimation tool. Bioinformatics 2006;22:2704-5.
150. Zwolak JW, Tyson JJ, Watson LT. Globally optimised parameters for a model of mitotic control in frog egg extracts. Syst Biol 2005; 152:81-92.
151. Cline MS, Smoot M, Cerami E, et al. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2007;2:2366-82.
152. Aldridge BB, Burke JM, Lauffenburger DA, et al. Physicochemical modelling of cell signalling pathways. Nat Cell Biol 2006;8 1195-203.
153. Andrecut M, Huang S, Kauffman SA. Heuristic approach to sparse approximation of gene regulatory networks. J Comput Biol 2008; 15:1173-86.
154. Fujita A, Sato JR, Garay-Malpartida HM, et al. Modeling gene expression regulatory networks with the sparse vector autoregressive model. BMC Syst Biol 2007;1:39.
155. Nelander S, Wang W, Nilsson B, et al. Models from experiments: combinatorial drug perturbations of cancer cells. Mol Syst Biol 2008;4:216.
156. Kholodenko BN. Cell-signalling dynamics in time and space. Nat Rev Mol Cell Biol 2006;7:165-76.
157. Slepchenko BM, Schaff JC, Macara I, et al. Quantitative cell biology with the Virtual Cell. Trends Cell Biol 2003;13: 570-6.
158. Borghans JA, de Boer RJ, Segel LA. Extending the quasisteady state approximation by changing variables. Bull Math Biol 1996;58 43-63.
159. Barik D, Paul MR, Baumann WT, et al. Stochastic simulation of enzyme-catalyzed reactions with disparate timescales. Biophys J 2008;95:3563-74.
160. Ciliberto A, Capuani F, Tyson JJ. Modeling networks of coupled enzymatic reactions using the total quasi-steady state approximation. PLoS Comput Biol 2007;3:e45.
161. Turányi T. Sensitivity analysis of complex kinetic systems. Tools and applications. Journal of Mathematical Chemistry 1990;5 203-48.
162. Conradi C, Flockerzi D, Raisch J, et al. Subnetwork analysis reveals dynamic features of complex (bio)chemical networks. Proc Natl Acad Sci USA 2007;104:19175-80.
163. Ballarini P, Mazza T, Palmisano A, et al. Studying irreversible transitions in a model of cell cycle regulation. Electron Notes Theoret Comput Sci 2009; in press.
164. Calzone L, Fages F, Soliman S. BIOCHAM: An environment for modeling biological systems and formalizing experimental knowledge. Bioinformatics 2006;22:1805-7.
165. Heath J, Kwiatkowska M, Norman G, et al. Probabilistic model checking of complex biological pathways. Theoretical Computer Science 2008;391:239-57.
166. Mardare R, Priami C, Quaglia P, et al. Model checking biological systems described using ambient calculus. Comput Methods Syst Biol 2005;3082:85-103.
167. Monteiro PT, Ropers D, Mateescu R, et al. Temporal logic patterns for querying dynamic models of cellular interaction networks. Bioinformatics 2008;24:i227-i233.
168. Hlavacek WS, Faeder JR, Blinov ML, et al. Rules for modeling signal-transduction systems. Sci STKE 2006; 2006:rE6.
169. Regev A, Shapiro E. Cells as computation. Nature 2002; 419 343.
170. Ciocchetta F, Hillston J. Process algebras in systems biology. Lecture Notes Comput Sci 2008;5016: 265-312.
171. Phillips A, Cardelli L. A correct abstract machine for the stochastic pi-calculus. Proceedings of Bioconcur 2004. London: Concurrent Models in Molecular Biology, 2005.
172. Priami C, Quaglia P. Modelling the dynamics of biosystems. Brief Bioinform 2004;5:259-69.
173. Priami C, Quaglia P. Beta binders for biological interactions. Comput Methods Syst Biol 2005;3082:20-33.
174. Kim SY, Song EJ, Lee KJ, et al. Multisite M-phase phosphorylation of Xenopus Wee1A. Mol Cell Biol 2005; 25:10580-90.
175. de Lichtenberg U, Jensen LJ, Brunak S, et al. Dynamic complex formation during the yeast cell cycle. Science 2005; 307 724-7.
176. Dematte L, Priami C, Romanel A. Modelling and simulation of biological processes in BlenX, SIGMETRICS Perform. Eval Rev 2008;35 32-9.
177. Altinok A, Levi F, Goldbeter A. A cell cycle automaton model for probing circadian patterns of anticancer drug delivery. Adv Drug Deliv Rev 2007;59: 1036-53.
178. Bernard S, Herzel H. Why do cells cycle with a 24 hour period? Genome Inform 2006;17:72-9.
179. Obeyesekere MN, Tecarro E, Lozano G. Model predictions of MDM2 mediated cell regulation. Cell Cycle 2004;3: 655-61.
180. Fuss H, Dubitzky W, Downes S, et al. Bistable switching and excitable behaviour in the activation of Src at mitosis. Bioinformatics 2006;22:e158-e165.
181. Klipp E, Nordlander B, Kruger R, et al. Integrative model of the response of yeast to osmotic shock. Nat Biotechnol 2005;23 975-82.
182. Kofahl B, Klipp E. Modelling the dynamics of the yeast pheromone pathway. Yeast 2004;21:831-50.
183. Kuepfer L, Peter M, Sauer U, et al. Ensemble modeling for analysis of cell signaling dynamics. Nat Biotechnol 2007; 25:1001-6.
184. Schaber J, Kofahl B, Kowald A, et al. A modelling approach to quantify dynamic crosstalk between the pheromone and the starvation pathway in baker's yeast. FEBS J 2006;273: 3520-33.
185. Schoeberl 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.
186. Wang X, Hao N, Dohlman HG, et al. Bistability, stochasticity, and oscillations in the mitogen-activated protein kinase cascade. Biophys J 2006;90:1961-78.
187. Hayles J, Nurse P. A journey into space. Nat Rev Mol Cell Biol 2001;2:647-56.
188. Barre B, Perkins ND. A cell cycle regulatory network controlling NF-kappaB subunit activity and function. EMBO J 2007;26 4841-55.
189. Kastan MB, Bartek J. Cell-cycle checkpoints and cancer. Nature 2004;432:316-23.
190. Altschuler SJ, Angenent SB, Wang Y, et al. On the spontaneous emergence of cell polarity. Nature 2008;454: 886-9.
191. Csikasz-Nagy A, Gyorffy B, Alt W, et al. Spatial controls for growth zone formation during the fission yeast cell cycle. Yeast 2008;25:59-69.
192. Ihekwaba AE, Wilkinson SJ, Waithe D, et al. Bridging the gap between in silico and cell-based analysis of the nuclear factor-kappaB signaling pathway by in vitro studies of IKK2. FEBS J 2007;274 1678-90.
193. Bentele M, Lavrik I, Ulrich M, et al. Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis. J Cell Biol 2004;166:839-51.
194. Anderson AR, Rejniak KA, Gerlee P, et al. Microenvironment driven invasion: A multiscale multi-model investigation. J Math Biol 2009;58:579-624.
195. Chauhan A, Legewie S, Westermark PO, et al. A mesoscale model of G1/S phase transition in liver regeneration. J Theor Biol 2008; 252:465-73.
196. Ribba B, Colin T, Schnell S. A multiscale mathematical model of cancer, and its use in analyzing irradiation therapies. Theor Biol Med Model 2006;3:7.
197. Zhang L, Wang Z, Sagotsky JA, et al. Multiscale agent-based cancer modeling. J Math Biol 2009;58:545-559.
198. Anderson AR, Quaranta V. Integrative mathematical oncology. Nat Rev Cancer 2008;8:227-34.
199. Araujo RP, McElwain DL. A history of the study of solid tumour growth: the contribution of mathematical modelling. Bull Math Biol 2004; 66:1039-91.
200. Byrne HM, Alarcon T, Owen MR, et al. Modelling aspects of cancer dynamics: A review. Philos Trans R Soc A: Math Phys Eng Sci 2006;364:1563-78.
201. Preziosi L. Cancer Modelling and Simulation. London: CRC Press, 2003.
202. Byrne H, Drasdo D. Individual-based and continuum models of growing cell populations: A comparison. J Math Biol 2009;58:657-687.
203. Sherratt JA, Chaplain MA. A new mathematical model for avascular tumour growth. J Math Biol 2001;43: 291-312.
204. Duvdevani-Bar S, Segel L. On topological simulations in developmental biology. J Theoret Biol 1994;166:33-50.