Evolution of Bow-Tie Architectures in Biology

PLoS Computational Biology

Volumn 11, Issue 3, 2015, Pages

  Friedlander, Tamar ^a,b   Mayo, Avraham E ^a   Tlusty, Tsvi ^c   Alon, Uri ^a  

^a WEIZMANN INSTITUTE OF SCIENCE   (Israel)

^b INSTITUTE OF SCIENCE AND TECHNOLOGY AUSTRIA   (Austria)

^c INSTITUTE FOR ADVANCED STUDY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

NETWORK ARCHITECTURE; SIGNALING;

ARCHITECTURAL FEATURES; BOW TIE; BUILDING BLOCKES; INPUT AND OUTPUTS; INPUT LAYERS; INTERMEDIATE LAYERS; MULTI-LAYERED STRUCTURE; MULTIPLE INPUTS; MULTIPLE OUTPUTS; OUTPUT LAYER;

BIOLOGICAL SYSTEMS;

ARTICLE; BIOLOGY; BOW TIE ARCHITECTURE; DATA ANALYSIS; EVOLUTION; EVOLUTIONARY GOAL; INPUT OUTPUT RELATION; MATHEMATICAL MODEL; MATHEMATICAL PARAMETERS; MAXIMAL COMPRESSION; MULTI LAYERED NETWORK; NARROW LAYER; NONLINEAR SYSTEM; PRODUCT RULE MUTATION; RANK DEFICIENT MATRIX; SIMULATION; STATISTICAL MODEL; SUM RULE MUTATION; BIOLOGICAL MODEL; COMPUTER SIMULATION; METABOLISM; PROCEDURES; SIGNAL TRANSDUCTION;

BIOLOGICAL EVOLUTION; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; SIGNAL TRANSDUCTION;

EID: 84926370637     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1004055     Document Type: Article

References (84)

1. Csete M, Doyle J, (2004) Bow ties, metabolism and disease. TRENDS in Biotechnology 22: 446–450. 15331224
2. Kitano H, (2004) Biological robustness. Nat Rev Genet 5: 826–837. doi: 10.1038/nrg1471 15520792
3. Akhshabi S, Dovrolis C, (2011) The evolution of layered protocol stacks leads to an hourglass-shaped architecture. SIGCOMM-Computer Communication Review 41: 206.
4. Tieri P, Grignolio A, Zaikin A, Mishto M, Remondini D, et al. (2010) Network, degeneracy and bow tie integrating paradigms and architectures to grasp the complexity of the immune system. Theor Biol Med Model 7: 32. doi: 10.1186/1742-4682-7-32 20701759
5. Stern DL, Orgogozo V, (2009) Is genetic evolution predictable? Science 323: 746–751. doi: 10.1126/science.1158997 19197055
6. Mann RS, Carroll SB, (2002) Molecular mechanisms of selector gene function and evolution. Current Opinion in Genetics & Development 12: 592–600. doi: 10.1016/S0959-437X(02)00344-1
7. Zhao J, Yu H, Luo J-H, Cao Z-W, Li Y-X, (2006) Hierarchical modularity of nested bow-ties in metabolic networks. BMC Bioinformatics 7: 386. doi: 10.1186/1471-2105-7-386 16916470
8. Ma H-W, Zeng A-P, (2003) The connectivity structure, giant strong component and centrality of metabolic networks. Bioinformatics 19: 1423–1430. 12874056
9. Ma H, Sorokin A, Mazein A, Selkov A, Selkov E, et al. (2007) The Edinburgh human metabolic network reconstruction and its functional analysis. Molecular systems biology 3.
10. Alberts B, Bray D, Lewis J, Raff M, Roberts K, et al. (1994) Molecular Biology of the Cell.
11. Natarajan M, Lin K-M, Hsueh RC, Sternweis PC, Ranganathan R, (2006) A global analysis of cross-talk in a mammalian cellular signalling network. Nat Cell Biol 8: 571–580. doi: 10.1038/ncb1418 16699502
12. Behar M, Hoffmann A, (2010) Understanding the temporal codes of intra-cellular signals. Current Opinion in Genetics & Development 20: 684–693. doi: 10.1016/j.gde.2010.09.007
13. Jd J, Em L, R I, (2000) Signaling networks: the origins of cellular multitasking. Cell 103, 103: 193, 193–200. doi: 10.1016/S0092-8674(00)00112-4 11057893
14. Tortora GJ, (2011) Principles of anatomy & physiology. 13th ed., International student version. Hoboken, NJ: Wiley. 2 p.
15. Principles of neural science (2011). New York: McGraw-Hill Medical Publishing Division.
16. Perge JA, Koch K, Miller R, Sterling P, Balasubramanian V, (2009) How the Optic Nerve Allocates Space, Energy Capacity, and Information. J Neurosci 29: 7917–7928. doi: 10.1523/JNEUROSCI.5200-08.2009 19535603
17. Davidson EH, Erwin DH, (2006) Gene Regulatory Networks and the Evolution of Animal Body Plans. Science 311: 796–800. doi: 10.1126/science.1113832 16469913
18. Simpson C, (2012) The evolutionary history of division of labour. Proc R Soc B 279: 116–121. doi: 10.1098/rspb.2011.0766 21561969
19. Coen E, Rolland-Lagan A-G, Matthews M, Bangham JA, Prusinkiewicz P, (2004) The genetics of geometry. PNAS 101: 4728–4735. doi: 10.1073/pnas.0306308101 14960734
20. Prusinkiewicz P, Erasmus Y, Lane B, Harder LD, Coen E, (2007) Evolution and Development of Inflorescence Architectures. Science 316: 1452–1456. doi: 10.1126/science.1140429 17525303
21. Oda K, Matsuoka Y, Funahashi A, Kitano H, (2005) A comprehensive pathway map of epidermal growth factor receptor signaling. Molecular systems biology 1.
22. Polouliakh N, Nock R, Nielsen F, Kitano H, (2009) G-Protein Coupled Receptor Signaling Architecture of Mammalian Immune Cells. PLoS ONE 4: e4189. doi: 10.1371/journal.pone.0004189 19142232
23. Oda K, Kitano H, (2006) A comprehensive map of the toll-like receptor signaling network. Molecular systems biology 2.
24. Beutler B, (2004) Inferences, questions and possibilities in Toll-like receptor signalling. Nature 430: 257–263. doi: 10.1038/nature02761 15241424
25. Kitano H, Oda K, (2006) Robustness trade-offs and host–microbial symbiosis in the immune system. Mol Syst Biol 2.
26. Basalla G, (1988) The Evolution of Technology. Cambridge University Press. 264 p.
27. Adner R, Levinthal D, (2001) Demand heterogeneity and technology evolution: implications for product and process innovation. Management science 47: 611–628.
28. Broder A, Kumar R, Maghoul F, Raghavan P, Rajagopalan S, et al. (2000) Graph structure in the web. Computer networks 33: 309–320.
29. Friedlander T, Mayo AE, Tlusty T, Alon U, (2013) Mutation Rules and the Evolution of Sparseness and Modularity in Biological Systems. PLoS One 8.
30. Geman S, Bienenstock E, Doursat R, (1992) Neural networks and the bias/variance dilemma. Neural computation 4: 1–58.
31. Wagner GP, Pavlicev M, Cheverud JM, (2007) The road to modularity. Nature Reviews Genetics 8: 921–931. doi: 10.1038/nrg2267 18007649
32. Lorenz DM, Jeng A, Deem MW, (2011) The emergence of modularity in biological systems. Physics of Life Reviews 8: 129–160. doi: 10.1016/j.plrev.2011.02.003 21353651
33. Borenstein E, Krakauer DC, (2008) An end to endless forms: epistasis, phenotype distribution bias, and nonuniform evolution. PLoS computational biology 4: e1000202. doi: 10.1371/journal.pcbi.1000202 18949026
34. Pfeiffer T, Soyer OS, Bonhoeffer S, (2005) The evolution of connectivity in metabolic networks. PLoS biology 3: e228. 16000019
35. Soyer OS, Pfeiffer T, Bonhoeffer S, (2006) Simulating the evolution of signal transduction pathways. Journal of theoretical biology 241: 223–232. 16403533
36. Soyer OS, Bonhoeffer S, (2006) Evolution of complexity in signaling pathways. PNAS 103: 16337–16342. doi: 10.1073/pnas.0604449103 17060629
37. Francois P, Hakim V, (2004) Design of genetic networks with specified functions by evolution in silico. Proceedings of the National Academy of Sciences of the United States of America 101: 580–585. 14704282
38. Variano EA, McCoy JH, Lipson H, (2004) Networks, dynamics, and modularity. Physical review letters 92: 188701. 15169539
39. Kashtan N, Alon U, (2005) Spontaneous Evolution of Modularity and Network Motifs. PNAS 102: 13773–13778. doi: 10.1073/pnas.0503610102 16174729
40. Clune J, Mouret J-B, Lipson H, (2013) The evolutionary origins of modularity. Proc R Soc B 280:20122863. doi: 10.1098/rspb.2012.2863 23363632
41. Von Hippel PH, Berg OG, (1986) On the specificity of DNA-protein interactions. Proceedings of the National Academy of Sciences 83: 1608. 3456604
42. Wells JA, (1990) Additivity of mutational effects in proteins. Biochemistry 29: 8509–8517. doi: 10.1021/bi00489a001 2271534
43. Maerkl SJ, Quake SR, (2007) A Systems Approach to Measuring the Binding Energy Landscapes of Transcription Factors. Science 315: 233–237. doi: 10.1126/science.1131007 17218526
44. Maslov S, Ispolatov I, (2007) Propagation of large concentration changes in reversible protein-binding networks. Proceedings of the National Academy of Sciences 104: 13655–13660. 17699619
45. Zhang J, Maslov S, Shakhnovich EI, (2008) Constraints imposed by non-functional protein–protein interactions on gene expression and proteome size. Molecular systems biology 4.
46. Burda Z, Krzywicki A, Martin OC, Zagorski M, (2010) Distribution of essential interactions in model gene regulatory networks under mutation-selection balance. Phys Rev E 82: 011908. doi: 10.1103/PhysRevE.82.011908 20866649
47. Heo M, Maslov S, Shakhnovich E, (2011) Topology of protein interaction network shapes protein abundances and strengths of their functional and nonspecific interactions. Proceedings of the National Academy of Sciences 108: 4258–4263. doi: 10.1073/pnas.1009392108 21368118
48. Wagner A, (1994) Evolution of gene networks by gene duplications: a mathematical model and its implications on genome organization. Proceedings of the National Academy of Sciences 91: 4387–4391. 8183919
49. Wagner A (1996) Does evolutionary plasticity evolve? Evolution: 1008–1023.
50. Siegal ML, Bergman A, (2002) Waddington’s canalization revisited: developmental stability and evolution. Proceedings of the National Academy of Sciences 99: 10528–10532. 12082173
51. Azevedo RBR, Lohaus R, Srinivasan S, Dang KK, Burch CL, (2006) Sexual reproduction selects for robustness and negative epistasis in artificial gene networks. Nature 440: 87–90. doi: 10.1038/nature04488 16511495
52. Ciliberti S, Martin OC, Wagner A, (2007) Innovation and robustness in complex regulatory gene networks. Proceedings of the National Academy of Sciences 104: 13591–13596. 17690244
53. Leclerc RD, (2008) Survival of the sparsest: robust gene networks are parsimonious. Molecular Systems Biology 4: n/a–n/a. doi: 10.1038/msb.2008.52
54. Burda Z, Krzywicki A, Martin OC, Zagorski M, (2011) Motifs emerge from function in model gene regulatory networks. Proceedings of the National Academy of Sciences 108: 17263–17268. doi: 10.1073/pnas.1109435108 21960444
55. Lipson H, Pollack JB, Suh NP, (2007) On the origin of modular variation. Evolution 56: 1549–1556. doi: 10.1111/j.0014-3820.2002.tb01466.x
56. Espinosa-Soto C, Wagner A, (2010) Specialization Can Drive the Evolution of Modularity. PLoS Comput Biol 6: e1000719. doi: 10.1371/journal.pcbi.1000719 20360969
57. Kashtan N, Mayo AE, Kalisky T, Alon U, (2009) An Analytically Solvable Model for Rapid Evolution of Modular Structure. PLoS Comput Biol 5: e1000355. doi: 10.1371/journal.pcbi.1000355 19360090
58. Goldberg DE, (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.
59. Mitchell M, (1998) An introduction to genetic algorithms. Cambridge, Massachusetts: MIT Press.
60. Spall JC, (2003) Introduction to Stochastic Search and Optimization: Estimation, Simulation and Control. Wiley-Blackwell. p. 618.
61. Soskine M, Tawfik DS, (2010) Mutational effects and the evolution of new protein functions. Nature Reviews Genetics 11: 572–582. doi: 10.1038/nrg2808 20634811
62. Silander OK, Tenaillon O, Chao L, (2007) Understanding the evolutionary fate of finite populations: the dynamics of mutational effects. PLoS biology 5: e94. 17407380
63. Azevedo RB, Keightley PD, Laurén-Määttä C, Vassilieva LL, Lynch M, et al. (2002) Spontaneous mutational variation for body size in Caenorhabditis elegans. Genetics 162: 755–765. 12399386
64. Hogben L, (2007) Handbook of linear algebra. Boca Raton: Chapman & Hall/CRC.
65. Haykin SS, (1999) Neural networks: a comprehensive foundation. Prentice Hall. 872 p.
66. Bishop C, (2007) Pattern Recognition and Machine Learning (Information Science and Statistics). Springer.
67. Cover TM, Thomas JA, (2012) Elements of information theory. John Wiley & Sons.
68. Tishby N, Pereira FC, Bialek W (2000) The information bottleneck method. arXiv:physics/0004057. Available: http://arxiv.org/abs/physics/0004057.
69. Strong SP, Koberle R, de Ruyter van Steveninck RR, Bialek W, (1998) Entropy and Information in Neural Spike Trains. Phys Rev Lett 80: 197–200. doi: 10.1103/PhysRevLett.80.197
70. Slonim N, Atwal GS, Tkačik G, Bialek W, (2005) Information-based clustering. PNAS 102: 18297–18302. doi: 10.1073/pnas.0507432102 16352721
71. Tkačik G, Walczak AM, Bialek W, (2009) Optimizing information flow in small genetic networks. Physical Review E 80: 031920. 19905159
72. Walczak AM, Tkavcik G, Bialek W, (2010) Optimizing information flow in small genetic networks. II. Feed-forward interactions. Physical Review E 81: 041905. 20481751
73. Tkačik G, Prentice JS, Balasubramanian V, Schneidman E, (2010) Optimal population coding by noisy spiking neurons. PNAS 107: 14419–14424. doi: 10.1073/pnas.1004906107 20660781
74. Tkačik G, Walczak AM, Bialek W, (2012) Optimizing information flow in small genetic networks. III. A self-interacting gene. Physical Review E 85: 041903. 22680494
75. Hinton GE, Salakhutdinov RR, (2006) Reducing the dimensionality of data with neural networks. Science 313: 504–507. 16873662
76. Webb AR, Lowe D, (1990) The optimised internal representation of multilayer classifier networks performs nonlinear discriminant analysis. Neural Networks 3: 367–375.
77. Gallinari P, Thiria S, Badran F, Fogelman-Soulie F, (1991) On the relations between discriminant analysis and multilayer perceptrons. Neural Networks 4: 349–360.
78. Janes KA, Albeck JG, Gaudet S, Sorger PK, Lauffenburger DA, et al. (2005) A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science 310: 1646–1653. 16339439
79. Akhshabi S, Sarda S, Dovrolis C, Yi S (2014) An explanatory evo-devo model for the developmental hourglass. F1000Research. Available: http://f1000research.com/articles/3-156/v1.
80. Barlow H, (2001) Redundancy reduction revisited. Network: computation in neural systems 12: 241–253. 11563528
81. Sakata A, Hukushima K, Kaneko K, (2009) Funnel Landscape and Mutational Robustness as a Result of Evolution under Thermal Noise. Phys Rev Lett 102: 148101. doi: 10.1103/PhysRevLett.102.148101 19392487
82. Fisher RA, Bennett JH, (1930) The Genetical Theory of Natural Selection. 1st ed. Oxford University Press, USA. 318 p.
83. Lampert A, Tlusty T, (2009) Mutability as an altruistic trait in finite asexual populations. Journal of Theoretical Biology 261: 414–422. doi: 10.1016/j.jtbi.2009.08.027 19716831
84. Ripley BD, (2007) Pattern Recognition and Neural Networks. Cambridge University Press. 420 p.