The cell cycle switch computes approximate majority

Scientific Reports

Volumn 2, Issue , 2012, Pages

  Cardelli, Luca ^a   Csikász Nagy, Attila ^b,c  

^a MICROSOFT RESEARCH   (United Kingdom)

^b UNIVERSITY OF TRENTO   (Italy)

^c KING'S COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL; ARTICLE; BIOLOGICAL MODEL; CELL CYCLE CHECKPOINT; COMPUTER SIMULATION; HUMAN; MITOSIS; PROBABILITY; SIGNAL TRANSDUCTION; STATISTICAL MODEL; STATISTICS;

ANIMALS; CELL CYCLE CHECKPOINTS; COMPUTER SIMULATION; HUMANS; MARKOV CHAINS; MITOSIS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; SIGNAL TRANSDUCTION; STOCHASTIC PROCESSES;

EID: 84866987618     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep00656     Document Type: Article

References (65)

1. Novak, B. et al. Irreversible cell-cycle transitions are due to systems-level feedback. Nat Cell Biol 9, 724 (2007).
2. Brandman,O. et al. Interlinked fast and slow positive feedback loops drive reliable cell decisions. Science 310, 496 (2005).
3. Chang, D. E. et al. Building biological memory by linking positive feedback loops. Proc Natl Acad Sci USA 107, 175 (2010).
4. Lindqvist, A. et al. The decision to enter mitosis: feedback and redundancy in the mitotic entry network. J Cell Biol 185, 193 (2009).
5. Nurse, P. Universal control mechanism regulating onset of M-phase. Nature 344, 503 (1990).
6. Domingo-Sananes, M. R. & Novak, B. Different effects of redundant feedback loops on a bistable switch. Chaos 20, 045120 (2010).
7. Ferrell, J. E. Jr. Feedback regulation of opposing enzymes generates robust, all-ornone bistable responses. Curr Biol 18, R244 (2008).
8. O'Farrell, P. H. Triggering the all-or-nothing switch into mitosis. Trends Cell Biol 11, 512 (2001).
9. Csikasz-Nagy, A. Computational systems biology of the cell cycle. Brief Bioinform 10, 424 (2009).
10. Ferrell, J. E. et al.Modeling the Cell Cycle: Why Do Certain CircuitsOscillate? Cell 144, 874 (2011).
11. Csikasz-Nagy, A. et al. Analysis of a generic model of eukaryotic cell-cycle regulation. Biophys J 90, 4361 (2006).
12. Romanel, A. et al. Transcriptional Regulation Is a Major Controller of Cell Cycle Transition Dynamics. PloS one 7, e29716 (2012).
13. Angeli, D. et al. Detection of multistability, bifurcations, and hysteresis in a large class of biological positive-feedback systems. Proc Natl Acad Sci USA 101, 1822 (2004).
14. Griffith, J. S.Mathematics of cellular control processes: II. Positive feedback to one gene. J. Theor. Biol. 20, 209 (1968).
15. Tyson, J. J. et al. Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr Opin Cell Biol 15, 221 (2003).
16. Ferrell, J. E. Jr. Self-perpetuating states in signal transduction: positive feedback, double-negative feedback and bistability. Curr Opin Cell Biol 14, 140 (2002).
17. Tyson, J. J. & Novák, B. Functional motifs in biochemical reaction networks. Annual review of physical chemistry 61, 219 (2010).
18. Kshemkalyani, A. D. & Singhal, M. Distributed computing: principles, algorithms, and systems. (Cambridge Univ Press, 2008).
19. Lynch, N. A. Distributed algorithms. (Morgan Kaufmann, 1996).
20. Angluin, D. et al. A simple population protocol for fast robust approximate majority. Distributed Computing 21, 87 (2008).
21. Aspnes, J. & Ruppert, E. An introduction to population protocols. Middleware for Network Eccentric and Mobile Applications 1, 97 (2009).
22. Barik, D. et al. A model of yeast cell-cycle regulation based on multisite phosphorylation. Mol Syst Biol 6, 405 (2010).
23. Salazar, C. & Höfer, T. Multisite protein phosphorylation-from molecular mechanisms to kinetic models. Febs Journal 276, 3177 (2009).
24. Bazan, J. F. et al. Evolution of a bifunctional enzyme: 6-phosphofructo-2-kinase/fructose-2, 6-bisphosphatase. Proceedings of the National Academy of Sciences 86, 9642 (1989).
25. Fülöp, V. et al. The anatomy of a bifunctional enzyme: Structural basis for reduction of oxygen to water and synthesis of nitric oxide by cytochrome cd1. Cell 81, 369 (1995).
26. Ermentrout, B. Simulating, Analyzing, and Animating Dynamical Systems: A Guide to XPPAUT for Researchers and Students. (SIAM, 2002).
27. Phillips, A. & Cardelli, L. Efficient, correct simulation of biological processes in stochastic pi calculus. in CMSB2007 Vol. 4695 (eds Calder, M. & Gilmore, S.) 184 (Springer-Verlag, 2007).
28. Kwiatkowska, M. et al. PRISM: Probabilistic symbolic model checker. Computer Performance Evaluation: Modelling Techniques and Tools, 113 (2002).
29. Mochida, S.&Hunt, T. Protein phosphatases and their regulation in the control of mitosis. EMBO reports 13, 197 (2012).
30. Gillespie, D. T. Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81, 2340 (1977).
31. Steuer, R. Effects of stochasticity in models of the cell cycle: from quantized cycle times to noise-induced oscillations. Journal of theoretical biology 228, 293 (2004).
32. Thomas, R. & Kaufman,M.Multistationarity, the basis of cell differentiation and memory. I. Structural conditions of multistationarity and other nontrivial behavior. Chaos 11, 170 (2001).
33. Tyson, J. J. &Novak, B. Temporal organization of the cell cycle. Curr Biol 18, R759 (2008).
34. Tsai, T. Y. et al. Robust, tunable biological oscillations from interlinked positive and negative feedback loops. Science 321, 126 (2008).
35. Griffith, J. S. Mathematics of cellular control processes. I. Negative feedback to one gene. J. Theor. Biol. 20, 202 (1968).
36. Novak, B. & Tyson, J. J. Numerical analysis of a comprehensive model ofM-phase control in Xenopus oocyte extracts and intact embryos. J Cell Sci 106 (Pt 4), 1153 (1993).
37. Pomerening, J. R. et al. Building a cell cycle oscillator: hysteresis and bistability in the activation of Cdc2. Nat Cell Biol 5, 346 (2003).
38. Novák, B. & Tyson, J. J. Design principles of biochemical oscillators. Nature Reviews Molecular Cell Biology 9, 981 (2008).
39. Sangwin, C. The wonky trammel of Archimedes. Teaching Mathematics and its Applications 28, 48 (2009).
40. Tyson, J. J. et al. The dynamics of cell cycle regulation. Bioessays 24, 1095 (2002).
41. Vinson, V. et al. Does It Compute? Science 336, 171 (2012).
42. Fisher, J. & Henzinger, T. A. Executable cell biology. Nature biotechnology 25, 1239 (2007).
43. Kwiatkowska, M. Z. & Heath, J. K. Biological pathways as communicating computer systems. Journal of Cell Science 122, 2793 (2009).
44. Navlakha, S. & Bar-Joseph, Z. Algorithms in nature: the convergence of systems biology and computational thinking. Molecular Systems Biology 7, 546 (2011).
45. Nurse, P. Life, logic and information. Nature 454, 424 (2008).
46. Priami, C. Algorithmic systems biology. Commun. ACM 52, 80 (2009).
47. Regev, A. & Shapiro, E. Cellular abstractions: Cells as computation. Nature 419, 343 (2002).
48. Kim, S. Y.&Ferrell, J. E. Substrate competition as a source of ultrasensitivity in the inactivation of Wee1. Cell 128, 1133 (2007).
49. Nash, P. et al. Multisite phosphorylation of a CDK inhibitor sets a threshold for the onset of DNA replication. Nature 414, 514 (2001).
50. Gunawardena, J. Distributivity and processivity in multisite phosphorylation can be distinguished through steady-state invariants. Biophysical journal 93, 3828 (2007).
51. Goldbeter, A. & Koshland, D. E. An amplified sensitivity arising from covalent modification in biological systems. Proceedings of the National Academy of Sciences 78, 6840 (1981).
52. Goldbeter, A. Zero-order switches and developmental thresholds. Mol Syst Biol 1 (2005).
53. Blüthgen, N. et al. Effects of sequestration on signal transduction cascades. Febs Journal 273, 895 (2006).
54. Ciliberto, A. et al. Modeling networks of coupled enzymatic reactions using the total quasi-steady state approximation. PLoS Comput Biol 3, e45 (2007).
55. Kirschner, K. & Bisswanger, H. Multifunctional Proteins. Annual Review of Biochemistry 45, 143 (1976).
56. Kitano, H. Biological robustness. Nature Reviews Genetics 5, 826 (2004).
57. Kitano, H. Towards a theory of biological robustness. Mol Syst Biol 3 (2007).
58. Blüthgen, N. & Herzel, H. How robust are switches in intracellular signaling cascades? Journal of theoretical biology 225, 293 (2003).
59. Del Conte-Zerial, P. et al. Membrane identity and GTPase cascades regulated by toggle and cut-out switches. Mol Syst Biol 4, 206 (2008).
60. Grosshans, B. L. et al. Rabs and their effectors: achieving specificity in membrane traffic. Proceedings of the National Academy of Sciences 103, 11821 (2006).
61. Legewie, S. et al. Mathematical modeling identifies inhibitors of apoptosis as mediators of positive feedback and bistability. PLoS Comput Biol 2, e120 (2006).
62. Jilkine, A. & Edelstein-Keshet, L. A comparison of mathematical models for polarization of single eukaryotic cells in response to guided cues. PLoS computational biology 7, e1001121 (2011).
63. Maree, A. F. et al. Polarization and movement of keratocytes: a multiscale modelling approach. Bull Math Biol 68, 1169 (2006).
64. Wedlich-Soldner, R. & Li, R. Yeast and fungal morphogenesis from an evolutionary perspective. Semin Cell Dev Biol 19, 224 (2008).
65. Glover, D. M. The overlooked greatwall: a new perspective on mitotic control. Open Biology 2, 120023 (2012).