Novel recurrent neural network for modelling biological networks: Oscillatory p53 interaction dynamics

BioSystems

Volumn 114, Issue 3, 2013, Pages 191-205

  Ling, Hong ^a,b   Samarasinghe, Sandhya ^b   Kulasiri, Don ^b  

^a GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

^b LINCOLN UNIVERSITY   (New Zealand)

Author keywords

Artificial neural network; Biological robustness; Nonlinear ordinary differential equations; P53 Mdm2 oscillation system; Recurrent neural networks

Indexed keywords

PROTEIN P53; PROTEIN MDM2;

ARTIFICIAL NEURAL NETWORK; ESTIMATION METHOD; GENE; MAPPING; NEUROLOGY; PARAMETERIZATION; PROTEIN; QUANTITATIVE ANALYSIS; SIGNALING;

ARTICLE; ARTIFICIAL NEURAL NETWORK; DNA DAMAGE; FEEDBACK SYSTEM; GENETIC ALGORITHM; KINETICS; MATHEMATICAL ANALYSIS; MOLECULAR INTERACTION; OSCILLATION; PROTEIN PROTEIN INTERACTION; SYSTEMS BIOLOGY; COMPUTER SIMULATION; HUMAN; METABOLISM; PHYSIOLOGY; PROCEDURES; SIGNAL TRANSDUCTION;

COMPUTER SIMULATION; HUMANS; NEURAL NETWORKS (COMPUTER); PROTO-ONCOGENE PROTEINS C-MDM2; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY; TUMOR SUPPRESSOR PROTEIN P53;

EID: 84885742317     PISSN: 03032647     EISSN: 18728324     Source Type: Journal    
DOI: 10.1016/j.biosystems.2013.08.004     Document Type: Article

References (113)

1. Aderem A. Systems biology: its practice and challenges. Cell 2005, 121:511-513.
2. Alanis A.Y., Sanchez E.N., Ricalde R.J., Perez-Cisneros M.A. Discrete-time reduced order neural observers for uncertain nonlinear systems. International Journal of Neural Systems 2010, 20(1):29-38.
3. Albert R., Wang R.S. Discrete dynamic modeling of cellular signaling networks. Methods in Enzymology 2009, 467:281-306.
4. Albert R., Wang R.S., Michael L.J., Ludwig B. Discrete Dynamic Modeling of Cellular Signalling Networks, vol. 467, Methods in Enzymology 2009, Academic Press.
5. Alon U. Biological networks: the tinkerer as an engineer. Science 2003, 301:1866-1877.
6. Alon U. An Introduction to Systems Biology: Design Principles of Biological Circuits 2007, Chapman & Hall/CRC, Boca Raton, FL.
7. Ashyraliyev M., Fomekong-Nanfack Y., Kaandorp J.A., Blom J.G. Systems biology: parameter estimation for biochemical models. FASEB Journal 2009, 276(4):886-902.
8. Ayed A., Hupp T., Levav-Cohen Y., Goldberg Z., Alsheich-Bartok O., Zuckerman V., et al. The p53-Mdm2 Loop: A Critical Juncture of Stress Response 2011, 53. Springer, USA. 1 ed.
9. Bannis S., Moyal L., Shieh S., Taya Y., Anderson C.W., Chessa L., et al. Enhanced phosphorylation of p53 by ATM in response to DNA damage. Science 1998, 281(5383):1674-1677.
10. Batchelor E., Mock C.S., Bhan I., Loewer A., Lahav G. Recurrent initiations: a mechanism for triggering p53 pulses in response to DNA damage. Molecular Cell 2008, 30:277-289.
11. Barak Y., Juven T., Haffner R., Oren M. Mdm2 expression is induced by wild type p53 activity. EMBO Journal 1993, 12:461-468.
12. Baras F. Stochastic analysis of limit cycle behavior. Lectures Notes in Physics: Stochastic Dynamics 1997, LNP 484:167-178. Springer Verlag, Berlin.
13. Baras F., Pearson J.E., Mansour M.M. Microscopic simulation of chemical oscillations in homogeneous systems. Journal of Chemical Physics 1990, 93(8):5747-5750.
14. Bazil J.N., Buzzard G.T., Rundell A.E. Modeling mitochondrial bioenergetics with integrated volume dynamics. PLoS Computational Biology 2010, 6(1):e1000632.
15. Berthoumieux S., Brilli M., de Jong H., Kahn D., Cinquemani E. Identification of metabolic network models from incomplete high-throughput datasets. Bioinformatics 2011, 27(13). i186-i195.
16. Bourdon J.C., Fernandes K., Murray-Zmijewski F., Liu G., Diot A., Xirodimas D.P., Saville M.K., Lane D.P. p53 isoforms can regulate p53 transcriptional activity. Genes and Development 2005, 19:2122-2137.
17. Bray D. Intracellular signalling as a parallel distributed process. Journal of Theoretical Biology 1990, 143:215.
18. Ciliberto A., Novak B., Tyson J. Steady states and oscillations in the p53/Mdm2 network. Cell Cycle 2005, 4(3):488-493.
19. Davidich M.I., Bornholdt S. Boolean network model predicts cell cycle sequence of fission yeast. PLoS ONE 2008, 3(2.).
20. de Jong H. Modeling and simulation of genetic regulatory systems: a literature review. Journal of Computational Biology 2002, 9(1):67-103.
21. Friedman N., Linial M., Nachman I., Pe'er D. Using Bayesian networks to analyze expression data. Journal of Computational Biology 2000, 7:601-620.
22. Freitas A.A., Wieser D.C., Apweiler R. On the importance of comprehensible classification models for protein function prediction. IEEE Transactions on Computational Biology and Bioinformatics 2010, 7(1):172-182.
23. Ge H., Walhout A.J.M., Vidal M. Integrating 'omic' information: a bridge between genomics and systems biology. Trends in Genetics 2003, 19:551-560.
24. Geva-Zatorsky N., Rosenfeld N., Itzkovitz S., Milo R., Sigal A., Dekel E., Yarnitzky T., Liron Y., Polak P., Lahav G., Alon U. Oscillations and variability in the p53 system. Molecular Systems Biology 2006, 2.
25. Ghosh-Dastidar S., Adeli H. Spiking neural networks. International Journal of Neural Systems 2009, 19(4):295-308.
26. Gilbert D., Fuß H., Gu X., Orton R., Robinson S., Vyshemirsky V., Kurth M.J.J., Downes C.S.S., Dubitzky W. Computational methodologies for modelling, analysis and simulation of signalling networks. Briefings in Bioinformatics 2006, 7(4):339-353.
27. Gonzalez-Olvera M.A., Gallardo-Hernandez A.G., Tang Y., Revilla-Monsalve M.C., Islas-Andrade S. A discrete-time recurrent neurofuzzy network for black-box modeling of insulin dynamics in diabetic type-1 patients. International Journal of Neural Systems 2010, 20(2):149-158.
28. Gonze D., Halloy J., Goldbeter A. Robustness of circadian rhythms with respect to molecular noise. Proceedings of the National Academy of Sciences 2002, 99(2):673-678.
29. Granada A.E., Herzel H. How to achieve fast entrainment? The timescale to synchronization. PLoS ONE 2009, 4(9):e7057.
30. Hagan M., Demuth H.B., Beale M. Neural Network Design 2002, Thomson Asia Pty Ltd./China Machine Press, USA/China.
31. Hardy S., Iyengar R., Koch I., Reisig W., Schreiber F. Analysis of dynamical models of signaling networks with petri nets and dynamic graphs modeling. Systems Biology 2011, Springer, London. 16 ed.
32. Harris S.L., Levine A.J. The p53 pathway: positive and negative feedback loops. Oncogene 2005, 24:2899-2908.
33. Hartwell L.H., Hopfield J.J., Leibler S., Murray A.W. From molecular to modular cell biology. Nature 1999, 402. C47-C52.
34. Haupt S., Berger M., Goldberg Z., Haupt Y. Apoptosis - the p53 network. Journal of Cell Science 2003, 116(20):4077-4085.
35. Haupt Y., Maya R., Kazaz A., Oren M. Mdm2 promotes the rapid degradation of p53. Nature 1997, 387:296-299.
36. Heath A.P., Kavraki L.E. Computational challenges in systems biology. Computer Science Reviews 2009, 3:1-17.
37. Hecker M., Lambeck S., Toepfer S., van Someren E., Guthke R. Gene regulatory network inference: data integration in dynamic models - a review. Biosystems 2009, 96(1):86-103.
38. Heiner M., Koch I., Will J. Model validation of biological pathways using Petri nets-demonstrated for apoptosis. Biosystems 2004, 75(1-3):15-28.
39. Hill S.M., Lu Y.L., Molina J., Heiser L.M., Spellman P.T., Speed T.P., Gray J.W., Mills G.B., Mukherjee S. Bayesian inference of signaling network topology in a cancer cell line. Bioinformatics 2012, 28(21):2804-2810.
40. Hirao A., Kong Y.Y., Matsuoka S., Wakeham A., Ruland J., Yoshida H., et al. DNA damage-induced activation of p53 by the checkpoint kinase Chk2. Science 2000, 287:1824-1827.
41. Hopfield J.J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the United States of America 1982, 79:2554.
42. Horn H.F., Vousden K. Coping with stress: multiple ways to activate p53. Oncogene 2007, 26:1306-1316.
43. Hou X., Liu J.E., Liu W., Liu C.Y., Liu Z.Y., Sun Z.Y. A new role of NUAK1: directly phosphorylating p53 and regulating cell proliferation. Oncogene 2011, 30(26):2933-2942.
44. Hübner K., Sahle S., Kummer U. Applications and trends in systems biology in biochemistry. FASEB Journal 2011, 278(16):2767-2857.
45. Hughey J.J., Lee T.K., Covert M.W. Computational modeling of mammalian signaling networks. Wiley Interdisciplinary Reviews: Systems Biology and Medicine 2010, 2(2):194-209.
46. Husmeier D. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 2003, 19:2271-2282.
47. Hunziker A., Jensen M., Krishna S. Stress-specific response of the p53-Mdm2 feedback loop. BMC Systems Biology 2010, 4(1):94.
48. Hyman A., Whither A. Systems biology. Philosophical Transactions of the Royal Society B: Biological Sciences 2011, 366(1584):3635-3637.
49. Ideker T., Galitski T., Hood L. A new approach to decoding life: systems biology. Annual Review of Genomics and Human Genetics 2001, 2:343-372.
50. Ihmels J., Friedlander G., Bergmann S., Sarig O., Ziv Y., Barkai N. Revealing modular organization in the yeast transcriptional network. Nature Genetics 2002, 31(4):370-377.
51. Jin S., Levine A.J. The p53 functional circuit. Journal of Cell Science 2001, 114:4139-4140.
52. Karlebach G., Shamir R. Modelling and analysis of gene regulatory networks. Nature Reviews Molecular Cell Biology 2008, 9:770-780.
53. Kitano H. Systems biology: a brief overview. Science 2002, 295:1662-1664.
54. Kitano H. Towards a theory of biological robustness. Molecular Systems Biology 2007, 3:1-7.
55. Kohl P., Crampin E.J., Quinn T.A., Noble D. Systems biology: an approach. Clinical Pharmacology and Therapeutics 2010, 88(1):25-33.
56. Kohn K.W. Molecular interaction map of the mammalian cell cycle control and DNA repair systems. Molecular Biology of the Cell 1999, 10(8):1065-1075.
57. Kubbutat M.H., Jones S.N., Vousden K.H. Regulation of p53 stability by Mdm2. Nature 1997, 387(6630):299-303.
58. Lahav G., Rosenfeld N., Sigal A., Geva-Zatorsky N., Levine A.J. Dynamics of the p53-Mdm2 feedback loop in individual cells. Nature Genetics 2004, 36:147-150.
59. Lane D.P. Cancer. p53, guardian of the genome. Nature 1992, 358(6381):15-16.
60. Lev Bar-Or R., Maya R., Segel L.A., Alon U., Levine A.J., Oren M. Generation of oscillations by the p53-Mdm2 feedback loop: a theoretical and experimental study. Proceedings of the National Academy of Sciences of the United States of America 2000, 97(21):11250-11255.
61. Li G., Ho V.C. p53-dependent DNA repair and apoptosis respond differently to high- and low-dose ultraviolet radiation. British Journal of Dermatology 1998, 139(1):3-10.
62. Lillacci G., Khammash M. Parameter estimation and model selection in computational biology. PLoS Computational Biology 2010, 6(3):e1000696.
63. Ling H., Kulasiri D., Samarasinghe S. Robustness of G1/S checkpoint pathways in cell cycle regulation based on probability of DNA-damaged cells passing as healthy cells. Biosystems 2010, 101(3):213-221.
64. Ling S., Lin W.C. EDD inhibits ATM-mediated phosphorylation of p53. Journal of Biological Chemistry 2011, 286(17):14972-14982.
65. Ling H., Samarasinghe S., Kulasiri D. Robustness of CDK2 in triggering cellular senescence based on probability of DNA-damaged cells passing G1/S checkpoint. Proceedings of the 5th IEEE International Conference on Systems Biology 2011.
66. Liu B., Yan S., Wang Q. Delay hill dynamics in regulatory biological systems. Molecular Biosystems 2011, 7(2):457-463.
67. Ma L., Wagner J., Rice J.J., Hu W., Levine A.J., Stolovitzky G.A. A plausible model for the digital response of p53 to DNA damage. Proceedings of the National Academy of Sciences USA 2005, 102:14266-14271.
68. Molina F., Dehmer M., Perco P., Graber A., Girolami M., Spasovski G., et al. Systems biology: opening new avenues in clinical research. Nephrology, Dialysis, Transplantation 2010, 25(4):1015-1018.
69. Monk N.A. Oscillatory expression of Hes1, p53 and NF-_B driven by transcriptional time delays. Current Biology 2003, 13:1409-1413.
70. Morris M.K., Saey-Rodriguez J., Sorger P.K., Lauffenburger D.A. Logic-based models for the analysis of cell signaling networks. Biochemistry 2010, 49:3216-3224.
71. Neiman A.B., Yakusheva T.A., Russell D.F. Noise-induced transition to bursting in responses of paddlefish electroreceptor afferents. Journal of Neurophysiology 2007, 98(5):2795-2806.
72. Oltvai Z.N., Barabasi A.L. Systems biology. Life's complexity pyramid. Science 2002, 298(5594):763-764.
73. Papin J.A., Price N.D., Wiback S.J., Fell D.A., Palsson B.O. Metabolic pathways in the post-genome era. Trends in Biochemical Sciences 2003, 28(5):250-258.
74. Papin J.A., Stelling J., Price N.D., Klamt S., Schuster S., Palsson B.O. Comparison of network-based pathway analysis methods. Trends in Biotechnology 2004, 22(8):400-405.
75. Peterson D., Lee J., Lei X.C., Forrest W.F., Davis D.P., Jackson P.K., et al. A chemosensitization screen identifies TP53RK, a kinase that restrains apoptosis after mitotic stress. Cancer Research 2010, 70(15):6325-6335.
76. Petrič T., Gams A., Ijspeert A.J., Žlajpah L. On-line frequency adaptation and movement imitation for rhythmic robotic tasks. The International Journal of Robotics Research 2011.
77. Pinto E.M., Ribeiro R.C., Figueiredo B.C., Zambetti G.P. TP53-associated pediatric malignancies. Genes and Cancer 2011, 2(4):485-490.
78. Poltz R., Naumann M. Dynamics of p53 and NF-kB regulation in response to DNA damage and identification of target proteins suitable for therapeutic intervention. BMC Systems Biology 2012, 6:125.
79. Polynikis A., Hogan S.J., di Bernardo M. Comparing different ODE modelling approaches for gene regulatory networks. Journal of Theoretical Biology 2009, 261(4):511-530.
80. Pozo C., Guillén-Gosálbez G., Sorribas A., Jiménez L. A spatial branch-and-bound framework for the global optimization of kinetic models of metabolic networks. Industrial and Engineering Chemistry Research 2010, 50(9):5225-5238.
81. Proctro C.J., Gray D.A. Explaining oscillations and variability in the p53-Mdm2 system. BMC Systems Biology 2008, 2(75).
82. Price N.D., Papin J.A., Schilling C.H., Palsson B.O. Genome-scale microbial in silico models: the constraints-based approach. Trends in Biotechnology 2003, 21:162-169.
83. Puscasu G., Codres B. Nonlinear system identification based on internal recurrent neural networks. International Journal of Neural Systems 2009, 19(2):115-125.
84. Qi J.P., Ding Y.S., Shao S.H., Zeng X.H., Chou K.C. Cellular responding kinetics based on a model of gene regulatory networks under radiotherapy. Health 2010, 02(02):137-146.
85. Rau A., Jaffrézic F., Foulley J.-L., Doerge R.W. An empirical Bayesian method for estimating biological networks from temporal microarray data. Statistical Applications in Genetics and Molecular Biology 2010, 9(1). Article 9.
86. Ravasz E., Somera A.L., Mongru D.A., Oltvai Z.N., Barabási A.L. Hierarchical organization of modularity in metabolic networks. Science 2002, 297(5586):1551-1555.
87. Reverte C., Dirion J.-L., Cabassud M. Kinetic model identification and parameters estimation from TGA experiments. Journal of Analytical and Applied Pyrolysis 2007, 79(1-2):297-305.
88. Rizk M.L., Liao J.C. Ensemble modeling and related mathematical modeling of metabolic networks. Journal of the Taiwan Institute of Chemical Engineers 2009, 40(6):595-601.
89. Sachs K., Gifford D., Jaakkola T., Sorger P., Lauffenburger D.A. Bayesian network approach to cell signaling pathway modeling. Science's STKE 2002, 2002(148):pe38.
90. Sachs K., Perez O., Pe'er D., Lauffenburger D.A., Nolan G.P. Causal protein-signaling networks derived from multiparameter single-cell data. Science 2005, 308(5721):523-529.
91. Sackmann A., Formanowicz D., Formanowicz P., Koch I., Blazewicz J. An analysis of the Petri net based model of the human body iron homeostasis process. Computational Biology and Chemistry 2007, 31(1):1-10.
92. Saez-Rodriguez J., Simeoni L., Lindquist J.A., Hemenway R., Bommhardt U., Arndt B., Haus U.U., Weismantel R., Gilles E.D., Klamt S., Schraven B. A logical model provides insights into T cell receptor signaling. PLoS Computational Biology 2007, 3(8):e163.
93. Samarasinghe S. Neural Networks for Applied Sciences and Engineering: from Fundamentals to Complex Pattern Recognition 2006, Auerbach, Boca Raton, FL.
94. Samuel B., Basile G. Analysis of a minimal model for p53 oscillations. Journal of Theoretical Biology 2007, 249:235-245.
95. Stelling J., Sauer U., Szallasi Z., Doyle Iii F.J., Doyle J. Robustness of cellular functions. Cell 2004, 118(6):675-685.
96. Sun X., Jin L., Xiong M. Extended Kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks. PLoS ONE 2008, 3(11):e3758.
97. Tsai K., Wang F. Evolutionary optimization with data collocation for reverse engineering of biological networks. Bioinformatics 2005, 21(7):1180-1188.
98. Tang X., Zhu Y., Han L., Kim A.L., Kopelovich L., Bickers D.R., et al. CP-31398 restores mutant p53 tumor suppressor function and inhibits UVB-induced skin carcinogenesis in mice. The Journal of Clinical Investigation 2007, 117(12):3753-3764.
99. Tenazinha N., Vinga S. A survey on methods for modeling and analyzing integrated biological networks. IEEE Transactions on Computational Biology and Bioinformatics 2011, 8(4):943-958.
100. Toledo F., Wahl G.M. Regulating the p53 pathway: in vitro hypothesis, in vivo veritas. Nature Reviews Cancer 2006, 6:909-923.
101. Vermeij R., Leffers N., van der Burg S.H., Melief C.J., Daemen T., Nijman H.W. Immunological and clinical effects of vaccines targeting p53-overexpressing malignancies. Journal of Biomedicine and Biotechnology 2011, 2011.
102. Vogelstein B., Lane D., Levine A.J. Surfing in p53 network. Nature 2000, 408:307-310.
103. Vu B.T., Vassilev L. Small-molecule inhibitors of protein-protein interactions. Current Topics in Microbiology and Immunology 2011, 348:151-172.
104. Wagner J., Ma L., Rice J.J., Hu W., Levine A.J., Stolovitzky G.A. p53-Mdm2 loop controlled by a balance of its feedback strength and effective dampening using ATM and delayed feedback. IEE Systems Biology 2005, 152(3):109-118.
105. Wang T.J., Baek S.W., Kwon H.J., Kim Y.J., Nam I.S., Cha M.S., et al. Kinetic parameter estimation of a commercial Fe-Zeolite SCR. Industrial and Engineering Chemistry Research 2011, 50(5):2850-2864.
106. Wang Z., Sun Y. Targeting p53 for novel anticancer therapy. Translational Oncology 2010, 3(1):1-12.
107. Wittmann D.M., Krumsiek J., Saez-Rodriguez J., Lauffenburger D.A., Klamt S., Theis F.J. Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling. BMC Systems Biology 2009, 3:98.
108. Woolf P.J., Prudhomme W., Daheron L., Daley G.Q., Lauffenburger D.A. Bayesian analysis of signaling networks governing embryonic stem cell fate decisions. Bioinformatics 2005, 21(6):741-753.
109. Wu X., Bayle J.H., Olson D., Levine A.J. The p53-mdm-2 autoregulatory feedback loop. Genes and Development 1993, 7:1126-1132.
110. Wu J., Voit E. Hybrid modeling in biochemical systems theory by means of functional petri nets. Journal of Bioinformatics Computer Biology 2009, 7(1):107-134.
111. Wu J., Voit E. Integrative biological systems modeling: challenges and opportunities. Frontiers Computer Science in China 2009, 3(1):92-100.
112. Xie Z., Kulasiri D., Samarasinghe S., Qian J. An unbiased sensitivity analysis reveals important parameters controlling periodicity of circadian clock. Biotechnology and Bioengineering 2010, 105(2):250-259.
113. Yang Y., Lee K.S., Xiang C., Lin H. Biological mechanisms revealed by a mathematical model for p53-Mdm2 core regulation. IET Systems Biology 2009, 3(4):229-238.