EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 10 / No 3 (2015) Math. Model. Nat. Phenom., 10 3 (2015) 186-205 References
Free Access
Issue
Math. Model. Nat. Phenom.
Volume 10, Number 3, 2015
Model Reduction
Page(s) 186 - 205
DOI https://doi.org/10.1051/mmnp/201510314
Published online 22 June 2015
  1. E. Barillot, L. Calzone, P. Hupe, J.P. Vert, A. Zinovyev. Computational systems biology of cancer. Chapman & Hall, CRC Mathematical & Computational Biology, 2012. [Google Scholar]
  2. S.C. Bendall, K.L. Davis, A.D. el Amir, M.D. Tadmor, E.F. Simonds, T.J. Chen, D.K. Shenfeld, G.P. Nolan, D. Pe’er. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell, 157 (3) (2014), 714–325. [CrossRef] [PubMed] [Google Scholar]
  3. D. Bérenguier, C. Chaouiya, P.T. Monteiro, A. Naldi, E. Remy, D. Thieffry, L. Tichit. Dynamical modeling and analysis of large cellular regulatory networks. Chaos, 23 (2) (2013), 025114. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  4. A. Biton, I. Bernard-Pierrot, Y. Lou, C. Krucker, E. Chapeaublanc, P. Rubio, B. Lopez, A. Kamoun, Y. Neuzillet, P. Gestraud, G. Grieco, S. Rebouissou, A. de Reynies, S. Benhamou, T. Lebret, J. Southgate, E. Barillot, Y. Allory, A. Zinovyev, F. Radvanyi. Independent component analysis uncovers the landscape of the bladder tumor transcriptome and reveals insights into luminal and basal subtypes. Cell Reports, 9 (2014), 1–11. [CrossRef] [PubMed] [Google Scholar]
  5. E. Bonnet, L. Calzone, D. Rovera, G. Stoll, E. Barillot, A. Zinovyev. BiNoM 2.0, a Cytoscape plugin for accessing and analyzing pathways using standard systems biology formats. BMC Syst. Biol., 7 (1) (2013), 18. [CrossRef] [PubMed] [Google Scholar]
  6. E. Bonnet, L. Calzone, D. Rovera, G. Stoll, E. Barillot, A. Zinovyev. Practical use of BiNoM: a Biological Network Manager Software. Methods Mol. Biol., 1021 (2013), 127–146. [CrossRef] [PubMed] [Google Scholar]
  7. E. Bonnet, E. Viara, I. Kuperstein, L. Calzone, D.P.A. Cohen, E. Barillot. NaviCell Web Service for network-based data visualization. Nucleic Acids Research (2015), Advanced Access Publication, http://dx.doi.org/10.1093/nar/gkv450. [Google Scholar]
  8. L. Calzone, A. Gelay, A. Zinovyev, F. Radvanyi, E. Barillot. A comprehensive modular map of molecular interactions in RB/E2F pathway. Mol. Syst. Biol., 4 (2008), 174. [CrossRef] [PubMed] [Google Scholar]
  9. L. Calzone, L. Tournier, S. Fourquet, D. Thieffry, B. Zhivotovsky, E. Barillot, A. Zinovyev. Mathematical modelling of cell-fate decision in response to death receptor engagement. PLoS Comput. Biol., 6 (3) (2010), e1000702. [CrossRef] [PubMed] [Google Scholar]
  10. L. Calzone, E. Barillot, A. Zinovyev. Predicting genetic interactions from Boolean models of biological networks. Integrative Biology (2015), Advanced Access Publication, http://pubs.rsc.org/en/Content/ArticleLanding/2015/IB/C5IB00029G. [Google Scholar]
  11. L. Calzone, A. Zinovyev, B. Zhivotovsky. Understanding Different Types of Cell Death Using Systems Biology. In Systems Biology of Apoptosis (ed. by Lavrik, I.). Springer, 2012. [Google Scholar]
  12. A. Carbone, A. Zinovyev, F. Kepes. Codon Adaptation Index as a measure of dominating codon bias. Bioinformatics, 19 (13) (2003), 2005–2015. [CrossRef] [PubMed] [Google Scholar]
  13. E.G. Cerami, B.E. Gross, E. Demir, I. Rodchenkov, O. Babur, N. Anwar, N. Schultz, G.D. Bader, C. Sander. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res., 39 (Database issue) (2011), D685–D690. [Google Scholar]
  14. M. Chacòn, M. Lévano, H. Allende, H. Nowak. Detection of gene expressions in microarrays by applying iteratively elastic neural net. In: B. Beliczynski et al. (Eds.). Lecture Notes in Computer Sciences, Springer: Berlin–Heidelberg, 4432 (2007), 355–363. [Google Scholar]
  15. M. Chanrion, I. Kuperstein, C. Barrière, F. El Marjou, D. Cohen, D. Vignjevic, L. Stimmer, P. Paul-Gilloteaux, I. Bièche, R. Tavares Sdos, G.F. Boccia, W. Cacheux, D. Meseure, S. Fre, L. Martignetti, P. Legoix-Né, E. Girard, L. Fetler, E. Barillot, D. Louvard, A. Zinovyev, S. Robine. Concomitant Notch activation and p53 deletion trigger epithelial-to-mesenchymal transition and metastasis in mouse gut. Nature Communications, 5 (2014), 5005. [Google Scholar]
  16. C. Chaouiya, A. Naldi, D. Thieffry. Logical modelling of gene regulatory networks with GINsim. Methods Mol. Biol., 804 (2012), 463–479. [CrossRef] [PubMed] [Google Scholar]
  17. S. Dagley, D. Nicholson. An introduction to metabolic pathways. Blackwell Scientific Publications, 1970. [Google Scholar]
  18. Y. Drier, M. Sheffer, E. Domany. Pathway-based personalized analysis of cancer. PNAS, 110 (16) (2013), 6388–6393. [CrossRef] [Google Scholar]
  19. H. Failmezger, B. Jaegle, A. Schrader, M. Hülskamp, A. Tresch. Semi-automated 3D leaf reconstruction and analysis of trichome patterning from light microscopic images. PLoS Comp. Biol., 9 (4) (2013), e1003029. [CrossRef] [Google Scholar]
  20. L.A. Flórez, C.R. Lammers, R. Michna, J. Stülke. CellPublisher: a web platform for the intuitive visualization and sharing of metabolic, signalling and regulatory pathways. Bioinformatics, 26 (23) (2010), 2997–2999. [CrossRef] [PubMed] [Google Scholar]
  21. S.H. Friend, T.C. Norman. Metcalfe’s law and the biology information commons. Nat. Biotechnol., 4 (2013), 297–303. [CrossRef] [Google Scholar]
  22. A.N. Gorban. Multigrid Integrators on Multiscale Reaction Networks. Keynote talk given at Algorithms for Approximation VI, Ambleside, the Lake District, UK, 2009. [Google Scholar]
  23. A.N. Gorban, I. Karlin, A. Zinovyev. Constructive Methods of Invariant Manifolds for Kinetic Problems. Physics Reports, 396 (2004), 197–403. [Google Scholar]
  24. A.N. Gorban, I. Karlin, A. Zinovyev. Invariant grids for reaction kinetics. Physica A, 333 (2004), 106–154. [CrossRef] [Google Scholar]
  25. A.N. Gorban, B. Kegl, D. Wunch, A. Zinovyev. (eds.) Principal Manifolds for Data Visualisation and Dimension Reduction. Lecture Notes in Computational Science and Engeneering 58, 2008. [Google Scholar]
  26. A.N. Gorban, N. Morozova, A. Harel-Belan, A. Zinovyev. Basic and simple mathematical model of coupled transcription, translation and degradation. (2013) http://arxiv.org/abs/1204.5941. [Google Scholar]
  27. A.N. Gorban, O. Radulescu, A.Y. Zinovyev. Asymptotology of chemical reaction networks. Chem. Eng. Sci., 65 (2010), 2310–2324. [Google Scholar]
  28. A.N. Gorban, N. Sumner, A. Zinovyev. Topological grammars for data approximation. Appl. Math. Lett., 20 (4) (2007), 382–386. [CrossRef] [Google Scholar]
  29. A.N. Gorban, N. Sumner, A. Zinovyev. Beyond The Concept of Manifolds: Principal Trees, Metro Maps, and Elastic Cubic Complexes. Lecture Notes in Computational Science and Engeneering 58 (2008), 223–240. [Google Scholar]
  30. A.N. Gorban, G.S. Yablonsky. Grasping Complexity. Computers & Mathematics with Applications, 65 (10) (2013), 1421–1426. [CrossRef] [MathSciNet] [Google Scholar]
  31. A.N. Gorban, A.Y. Zinovyev. Method of Elastic Maps and its Applications in Data Visualization and Data Modeling. International Journal of Computing Anticipatory Systems, Chaos 12 (2001), 353–369. [Google Scholar]
  32. A.N. Gorban, A. Zinovyev. Elastic Principal Graphs and Manifolds and their Practical Applications. Computing, 75 (2005), 359–379 [CrossRef] [MathSciNet] [Google Scholar]
  33. A.N. Gorban, A. Zinovyev. Elastic Maps and Nets for Approximating Principal Manifolds and Their Application to Microarray Data Visualization. Lecture Notes in Computational Science and Engeneering, 58 (2008), 97–128. [Google Scholar]
  34. A.N. Gorban, A.Y. Zinovyev. Principal Graphs and Manifolds. In Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods and Techniques (eds. Olivas E.S., Guererro J.D.M., Sober M.M., Benedito J.R.M., Lopes A.J.S.). Information Science Reference, 2009. [Google Scholar]
  35. A.N. Gorban, A. Zinovyev. Principal manifolds and graphs in practice: from molecular biology to dynamical systems. Int. J. Neural Syst., 20 (3) (2010), 219–232. [CrossRef] [PubMed] [Google Scholar]
  36. A.N. Gorban, A.Y. Zinovyev, A.A. Pitenko. Visualization of data using method of elastic maps (in Russian). Informatsionnie technologii, (6) (2000), 26–35. [Google Scholar]
  37. A.N. Gorban, A.Y. Zinovyev, T.G. Popova. Seven clusters in genomic triplet distributions. In Silico Biology, 3 (4) (2003), 471–482. http://arxiv.org/abs/cond-mat/0305681 [cond-mat.dis-nn] [PubMed] [Google Scholar]
  38. A.N. Gorban, A. Zinovyev, D.C. Wunsch. Application of the method of elastic maps in analysis of genetic texts. In: Proceedings of International Joint Conference on Neural Networks (IJCNN2003). Portland, Oregon, Vol. 3, 2003, 1826–1831. [Google Scholar]
  39. M. Gromov. Metric Structures for Riemannian and Non-Riemannian Spaces. Progress in Mathematics 152. Birkhauser Verlag, 1999. [Google Scholar]
  40. M. Gromov. Allure of Quotations and Enchantment of Ideas, 2013. http://www.ihes.fr/~gromov/PDF/quotationsideas.pdf. [Google Scholar]
  41. W.C. Hahn, R.A. Weinberg. A subway of cancer pathways. Nature Reviews Cancer Poster, (2002). [Google Scholar]
  42. D. Hanahan, R.A. Weinberg. The hallmarks of cancer. Cell, 100 (1) (2000), 57–70. [CrossRef] [PubMed] [Google Scholar]
  43. D. Hanahan, R.A. Weinberg. Hallmarks of cancer: the next generation. Cell, 144 (5) (2011), 646–674. [CrossRef] [PubMed] [Google Scholar]
  44. W.S. Hlavacek. How to deal with large models? Mol. Syst. Biol., 5 (2009), 240. [CrossRef] [PubMed] [Google Scholar]
  45. E.M. Izhikevich, G.M. Edelman. Large-scale model of mammalian thalamocortical systems. PNAS, 105 (2008), 3593–3598. [Google Scholar]
  46. H. Kitano, A. Funahashi, Y. Matsuoka, K. Oda. Using process diagrams for the graphical representation of biological networks. Nat. Biotechnol., 23 (8) (2005), 961–966. [CrossRef] [PubMed] [Google Scholar]
  47. K.W. Kohn. Molecular interaction map of the mammalian cell cycle control and DNA repair systems. Mol. Biol. Cell., 10 (8) (1999), 2703–2734. [CrossRef] [PubMed] [Google Scholar]
  48. M.D. Kruskal. Asymptotology. In: Dobrot, S. (Ed.), Mathematical Models in Physical Sciences. Prentice-Hall, Englewood Cliffs, NJ, (1963), 17–48. [Google Scholar]
  49. I. Kuperstein, D.P. Cohen, S. Pook, E. Viara, L. Calzone, E. Barillot, A. Zinovyev. NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps. BMC Syst. Biol., 7 (2013), 100. [CrossRef] [PubMed] [Google Scholar]
  50. I. Kuperstein, L. Grieco, D.P.A. Cohen, D. Thieffry, A. Zinovyev, E. Barillot. The shortest path is not the one you know: application of biological network resources in precision oncology research. Mutagenesis 30 (2015), 191–204. [CrossRef] [PubMed] [Google Scholar]
  51. I. Kuperstein, S. Robine, A. Zinovyev. Computational biology helps finding genetic determinants of metastatic colon cancer. Cell Cycle (2015), In press. [Google Scholar]
  52. M. Latendresse, P.D. Karp. Web-based metabolic network visualization with a zooming user interface. BMC Bioinformatics, 12 (2011), 176. [CrossRef] [PubMed] [Google Scholar]
  53. G. Mathonnet et al.. MicroRNA inhibition of translation initiation in vitro by targeting the cap-binding complex eIF4F. Science, 317 (2007), 1764–1767. [CrossRef] [PubMed] [Google Scholar]
  54. J.H. Miller, S.E. Page. Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press, 2007. [Google Scholar]
  55. N. Morozova, A. Zinovyev, N. Nonne, L.L. Pritchard, A.N. Gorban, A. Harel-Bellan. Kinetic signatures of microRNA modes of action. RNA, 18 (9) (2012), 032284. [CrossRef] [Google Scholar]
  56. T. Nissan, R. Parker. Computational analysis of miRNA-mediated repression of translation: implications for models of translation initiation inhibition. RNA, 4 (8) (2008), 1480–1491. [CrossRef] [Google Scholar]
  57. N. Le Novere, M. Hucka, H. Mi, S. Moodie, F. Schreiber, A. Sorokin, E. Demir, K. Wegner, M.I. Aladjem, S.M. Wimalaratne, F.T. Bergman, R. Gauges, P. Ghazal, H. Kawaji, L. Li, Y. Matsuoka, A. Villéger, S.E. Boyd, L. Calzone, M. Courtot, U. Dogrusoz, T.C. Freeman, A. Funahashi, S. Ghosh, A. Jouraku, S. Kim, F. Kolpakov, A. Luna, S. Sahle, E. Schmidt, S. Watterson, G. Wu, I. Goryanin, D.B. Kell, C. Sander, H. Sauro, J.L. Snoep, K. Kohn, H. Kitano. The Systems Biology Graphical Notation. Nat. Biotechnol., 27 (8) (2009), 735–741. [CrossRef] [PubMed] [Google Scholar]
  58. F. Palladino, H.L. Klein. Analysis of mitotic and meiotic defects in Saccharomyces cerevisiae SRS2 DNA helicase mutants. Genetics, 132 (1992), 23–37. [PubMed] [Google Scholar]
  59. K. Pearson. On lines and planes of closest to systems of points in space. Philos. Mag., 2 (1901), 559–572. [Google Scholar]
  60. S. Pook, G. Vaysseix, E. Barillot. Zomit: biological data visualization and browsing. Bioinformatics, 14 (9) (1998), 807–814. [CrossRef] [PubMed] [Google Scholar]
  61. O. Radulescu, A.N. Gorban, S. Vakulenko, A. Zinovyev. Hierarchies and modules in complex biological systems. In: Proceedings of European Conference on Complex Systems. Oxford, UK, 2006. [Google Scholar]
  62. O. Radulescu, A.N. Gorban, A. Zinovyev, A. Lilienbaum. Robust simplifications of multiscale biochemical networks. BMC Syst. Biol., 2 (2008), 86. [Google Scholar]
  63. O. Radulescu, A.N. Gorban, A. Zinovyev. Reduction of dynamical biochemical reactions networks in computational biology. Frontiers in Genetics, 3 (2012), 00131. [CrossRef] [PubMed] [Google Scholar]
  64. O. Radulescu, A. Zinovyev, A. Lilienbaum. Model reduction and model comparison for NFkB signalling. In Proceedings of Foundations of Systems Biology in Engineering, Stuttgart, Germany, (2007). [Google Scholar]
  65. P. Vera-Licona, E. Bonnet, E. Barillot, A. Zinovyev. OCSANA: Optimal Combinations of Interventions from Network Analysis. Bioinformatics, 15 (29) (2013), 1571–1573. [CrossRef] [Google Scholar]
  66. B. Vogelstein, N. Papadopoulos, V.E. Velculescu, S. Zhou, L.A. Diaz, K.W. Kinzler. Cancer genome landscapes. Science, 339 (2013), 1546–1558. [Google Scholar]
  67. A. Wagner. Robustness and Evolvability in Living Systems. Princeton Univ. Press, 2005. [Google Scholar]
  68. A. Zinovyev. Dealing with complexity of biological systems: from data to models. HDR synthesis text, (2014). http://arxiv.org/abs/1404.1626. [Google Scholar]
  69. A. Zinovyev. Visualization of Multidimensional Data (in Russian). KGTU Publ., Krasnoyasrk, 2000. [Google Scholar]
  70. A. Zinovyev, S. Fourquet, L. Tournier, L. Calzone, E. Barillot. Cell death and life in cancer: mathematical modeling of cell fate decisions. In Advances in Experimental Medicine and Biology (Goryanin, I. and Goryachev A, eds.), Springer, 736 (2012), 682. [Google Scholar]
  71. A. Zinovyev, U. Kairov, T. Karpenyuk, E. Ramanculov. Blind Source Separation Methods For Deconvolution Of Complex Signals In Cancer Biology. Biochemical and Biophysical Research Communications, 430 (3) (2013), 1182–1187. [CrossRef] [PubMed] [Google Scholar]
  72. A. Zinovyev, I. Kuperstein, E. Barillot, W.-.D. Heyer. Synthetic Lethality between Gene Defects Affecting a Single Non-essential Molecular Pathway with Reversible Steps. PLoS Comput. Biol., 9 (4) (2013), e1003016. [CrossRef] [PubMed] [Google Scholar]
  73. A. Zinovyev, E. Mirkes. Data complexity measured by principal graphs. Computers and Mathematics with Applications, 65 (2013), 1471–1482. [CrossRef] [MathSciNet] [Google Scholar]
  74. A. Zinovyev, N. Morozova, A.N. Gorban, A. Harel-Belan. Mathematical modeling of microRNA-mediated mechanisms of translation repression. In MiRNA Cancer Regulation: Advanced Concepts, Bioinformatics and Systems Biology Tools (Schmitz U, Wolkenhauer O, Vera J, eds.), Springer, (2013), 189–224. [Google Scholar]
  75. A. Zinovyev, N. Morozova, N. Nonne, E. Barillot, A. Harel-Bellan, A.N. Gorban. Dynamical modeling of microRNA action on the protein translation process. BMC Syst. Biol., 4 (2010), 13. [CrossRef] [PubMed] [Google Scholar]
  76. A. Zinovyev, E. Viara, L. Calzone, E. Barillot. BiNoM: a Cytoscape plugin for using and analyzing biological networks. Bioinformatics, 24 (6) (2008), 876–877. [CrossRef] [PubMed] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.