Aging in complex interdependency networks

Physical Review E - Statistical, Nonlinear, and Soft Matter Physics

Volumn 89, Issue 2, 2014, Pages

  Vural, Dervis C ^a   Morrison, Greg ^a,b   Mahadevan, L ^a,c  

^a HARVARD UNIVERSITY   (United States)

^b IMT SCHOOL FOR ADVANCED STUDIES LUCCA   (Italy)

^c HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CURVE FITTING; REPAIR;

DEPENDENCY STRUCTURES; DIVERSE RANGE; INITIAL CONDITIONS; MANY-BODY EFFECT; NETWORK STRUCTURES; QUANTITATIVE FEATURES; SIMPLE MODELING; SYSTEM SIZE;

COMPLEX NETWORKS;

PROTEOME;

AGING; ANIMAL; BIOLOGICAL MODEL; COMPUTER SIMULATION; GENE EXPRESSION REGULATION; HUMAN; LONGEVITY; METABOLISM; PHYSIOLOGY; SIGNAL TRANSDUCTION; STATISTICAL MODEL;

AGING; ANIMALS; COMPUTER SIMULATION; GENE EXPRESSION REGULATION; HUMANS; LONGEVITY; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PROTEOME; SIGNAL TRANSDUCTION;

EID: 84896926846     PISSN: 15393755     EISSN: 15502376     Source Type: Journal    
DOI: 10.1103/PhysRevE.89.022811     Document Type: Article

References (43)

1. S. Horiuchi, Pop. Dev. Rev. 29, 127 (2003).
2. G. Caughley, Ecology 47, 906 (1966). ECOLAR 0012-9658 10.2307/1935638
3. J. W. Vaupel, J. R. Carey, K. Christensen, T. E. Johnson, A. I. Yashin, Science 280, 855 (1998). SCIEAS 0036-8075 10.1126/science.280.5365.855 (Pubitemid 28215589)
4. K. A. Highes and R. M. Raynolds, Ann. Rev. Entomol. 50, 421 (2005). ARENAA 0066-4170 10.1146/annurev.ento.50.071803.130409
5. B. T. Weinert and P. S. Timiras, J. Appl. Physiol. 95, 1706 (2003).
6. E. H. Blackburn, Nature (London) 408, 53 (2000). NATUAS 0028-0836 10.1038/35040500
7. D. Harman, J. Gerontol. 2, 298 (1957).
8. A. Baudisch, Gerontology 58, 481 (2012). GERNDJ 0304-324X 10.1159/000341861
9. P. B. Medawar, An unsolved problem in biology (H. K. Lewis & Co., London, 1952).
10. G. C. Williams, Evolution 11, 398 (1957). EVOLAO 0014-3820 10.2307/2406060
11. W. D. Hamilton, J. Theor. Biol. 12, 12 (1966). JTBIAP 0022-5193 10.1016/0022-5193(66)90184-6
12. P. B. Medawar, Mod. Quart. 1, 30 (1946).
13. M. R. Rose, Evolutionary Biology of Aging (Oxford University Press, New York, 1991).
14. T. B. L. Kirkwood, Mech. Aging Dev. 123, 737 (2002). 0047-6374 10.1016/S0047-6374(01)00419-5 (Pubitemid 34178486)
15. T. B. L. Kirkwood, Nature (London) 270, 301 (1977). NATUAS 0028-0836 10.1038/270301a0
16. P. A. Abrams and D. Ludwig, Evolution 49, 1055 (1995). EVOLAO 0014-3820 10.2307/2410431
17. J. W. Curtsinger, H. H. Fukui, D. R. Townsend, and J. W. Vaupel, Science 258, 461 (1992). SCIEAS 0036-8075 10.1126/science.1411541
18. J. R. Carey, P. Liedo, D. Orozco, and J. W. Vaupel, Science 258, 457 (1992). SCIEAS 0036-8075 10.1126/science.1411540
19. D. E. L. Promislow, M. Tatar, A. A. Khazaeli, and J. W. Curtsinger, Genetics 143, 839 (1996).
20. D. A. Gray and W. H. Cade, Can. J. Zool. 78, 140 (2000). CJZOAG 0008-4301 10.1139/z99-191
21. R. A. Miller, R. Dysko, C. Chrisp, R. Seguin, L. Linsalata, J. Zool. 250, 95 (2000). JZOOAE 0952-8369 10.1111/j.1469-7998.2000.tb00580.x
22. R. A. Miller, J. M. Harper, R. C. Dysko, S. J. Durkee, and S. N. Austad, Exp. Biol. Med. 227, 500 (2002).
23. D. Reznick, G. Buckwalter, J. Groff, and D. Elder, Exp. Gerontol. 36, 791 (2001). EXGEAB 0531-5565 10.1016/S0531-5565(00)00241-2 (Pubitemid 32273417)
24. M. W. Gray, J. Lukes, J. M. Archibald, P. J. Keeling, and W. Doolittle, Science 330, 920 (2010). SCIEAS 0036-8075 10.1126/science.1198594
25. M. Lynch, Proc. Natl. Acad. Sci. USA 104, 8597 (2007). PNASA6 0027-8424 10.1073/pnas.0702207104 (Pubitemid 47175452)
26. A. Stoltzfus, J. Mol. Evol. 49, 169 (1999). JMEVAU 0022-2844 10.1007/PL00006540 (Pubitemid 29357860)
27. H. J. M. Kiss, Á. Mihalik, T. Nánási, B. Öry, Z. Spiró, C. Söti, and P. Csermely, BioEssays 31, 651 (2009). 10.1002/bies.200800224
28. M. Scheffer, J. Bascompte, W. A. Brock, V. Brokvin, S. R. Carpenter, Nature (London) 461, 53 (2009). NATUAS 0028-0836 10.1038/nature08227
29. R. Albert, H. Jeong, and A. L. Barabasi, Nature (London) 406, 378 (2000). NATUAS 0028-0836 10.1038/35019019 (Pubitemid 30625551)
30. T. Tanizawa, G. Paul, R. Cohen, S. Havlin, and H. E. Stanley, Phys. Rev. E 71, 047101 (2005). PLEEE8 1539-3755 10.1103/PhysRevE.71.047101 (Pubitemid 40619696)
31. R. Cohen, D. ben-Avraham, and S. Havlin, Phys. Rev. E 66, 036113 (2002). 1063-651X 10.1103/PhysRevE.66.036113 (Pubitemid 40606444)
32. S. V. Buldyrev, R. Parshani, G. Paul, H. E. Stanley, and S. Havlin, Nature (London) 464, 1025 (2010). NATUAS 0028-0836 10.1038/nature08932
33. G. I. Simkó, D. Gyurkó, D. V. Veres, T. Nánási, and P. Csermely, Genome Med. 1, 90 (2009). 10.1186/gm90
34. L. A. Gavrilov and N. S. Gavrilova, J. Theor. Biol. 213, 527 (2001). JTBIAP 0022-5193 10.1006/jtbi.2001.2430 (Pubitemid 34044487)
35. R. A. Laird and T. N. Sherratt, J. Evol. Biol. 22, 974 (2009). 1010-061X 10.1111/j.1420-9101.2009.01709.x
36. H. Jeong, B. Tombor, R. Albert, Z. N. Oltvai, and A. L. Barabasi, Nature (London) 407, 651 (2000). NATUAS 0028-0836 10.1038/35036627
37. For two other choices (τ 0, the time at which no nodes are left alive, or τ m, the time at which the largest drop in occurs), we have τ 0 ≈ τ m ≈ τ for large N. We find that in smaller networks (particularly RN) these times may differ. Interestingly τ is not always well defined in nature either; for example, parts of cockroaches are known to survive for weeks after the insect is decapitated [43].
38. In order to determine how sensitive our results are to the choice of threshold η, we have analyzed the lifetimes of both network topologies as a function η. For a network as small as N = 2500, the choice of η changes the lifetime by ∼ 1 % for a SFN and ∼ % 10 for a RN. We find that for N > O [10 6] the η dependence practically vanishes for both network types. We therefore conclude that the precise value of η is not important.
39. B. L. Strehler and A. S. Mildvan, Science 132, 14 (1960). SCIEAS 0036-8075 10.1126/science.132.3418.14
40. The threshold for similarity of the curve characteristics is determined by averaging over the differences in nearest neighbors in the (N, γ 0, γ 1, d) parameter space (i.e., the Σ k = 1 8 | τ ref-τ S k |, with the parameters in the simulation S k being different from the reference simulation in only one position, and a nearest neighbor). Thus, simulation outcomes are considered similar to a reference if they are not nearest neighbors (in parameter space) with the reference, and if the differences in all four characteristics simultaneously fall within the threshold variation.
41. Analogous equations can be written for σ (i) for i > 1; however these equations will depend on P (i; x 1 x 2... x i), the probability that a node with i providers has one provider with x 1 providers, one provider with x 2 providers, and so on. Fortunately we do not need these equations to obtain c and a.
42. M. Ya. Azbel, Phys. Rev. E 66, 016107 (2002). 1063-651X 10.1103/PhysRevE.66.016107 (Pubitemid 40599041)
43. C. Q. Choi, Sci. Am. 297, 116 (2007). SCAMAC 0036-8733 10.1038/scientificamerican1107-118