Advertisement

Systems Biology in Aging Research

Chapter
First Online:
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1086)

Abstract

Systems biology is an approach to collect high-dimensional data and analyze in an integrated manner. As aging is a complicated physiological functional decline in biological system, the methods in systems biology could be utilized in aging studies. Here we reviewed recent advances in systems biology in aging research and divide them into two major parts. One is the data resource, which includes omics data from DNA, RNA, proteins, epigenetic changes, metabolisms, and recently single-cell-level variations. The other is the data analysis methods consisting of network and modeling approaches. With all the data and the tools to analyze them, we could further promote our understanding of the systematic aging.

Keywords

Aging Systems biology Network Omics Single cell 
This is a preview of subscription content, log in to check access.

References

  1. Adamski J, Suhre K (2013) Metabolomics platforms for genome wide association studies – linking the genome to the metabolome. Curr Opin Biotechnol 24:39–47. Published online Epub2013/02//.  https://doi.org/10.1016/j.copbio.2012.10.003 PubMedGoogle Scholar
  2. Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, Rambow F, Marine J-C, Geurts P, Aerts J, J. v. d. Oord, Atak ZK, Wouters J, Aerts S (2017) SCENIC: single-cell regulatory network inference and clustering. Nat Methods 14:1083. Published online Epub2017/11//.  https://doi.org/10.1038/nmeth.4463 PubMedPubMedCentralGoogle Scholar
  3. Alisch RS, Barwick BG, Chopra P, Myrick LK, Satten GA, Conneely KN, Warren ST (2012) Age-associated DNA methylation in pediatric populations. Genome Res 22:623–632. Published online Epub2012/01/04/.  https://doi.org/10.1101/gr.125187.111 PubMedPubMedCentralGoogle Scholar
  4. Anselmi CV, Malovini A, Roncarati R, Novelli V, Villa F, Condorelli G, Bellazzi R, Puca AA (2009) Association of the FOXO3A locus with extreme longevity in a southern Italian centenarian study. Rejuvenation Res 12:95–104. Published online Epub2009/04//.  https://doi.org/10.1089/rej.2008.0827 PubMedGoogle Scholar
  5. Bacalini MG, Friso S, Olivieri F, Pirazzini C, Giuliani C, Capri M, Santoro A, Franceschi C, Garagnani P (2014) Present and future of anti-ageing epigenetic diets. Mech Ageing Dev:136, 101–137, 115. Published online Epub2014/04//Mar- undefined.  https://doi.org/10.1016/j.mad.2013.12.006 PubMedGoogle Scholar
  6. Baylis D, Ntani G, Edwards MH, Syddall HE, Bartlett DB, Dennison EM, Martin-Ruiz C, von Zglinicki T, Kuh D, Lord JM, Aihie Sayer A, Cooper C (2014) Inflammation, telomere length, and grip strength: a 10-year longitudinal study. Calcif Tissue Int 95:54–63. Published online Epub2014/07//.  https://doi.org/10.1007/s00223-014-9862-7 PubMedPubMedCentralGoogle Scholar
  7. Bell R, Hubbard A, Chettier R, Chen D, Miller JP, Kapahi P, Tarnopolsky M, Sahasrabuhde S, Melov S, Hughes RE (2009) A human protein interaction network shows conservation of aging processes between human and invertebrate species. PLoS Genet 5:e1000414. Published online Epub2009/03//.  https://doi.org/10.1371/journal.pgen.1000414 PubMedPubMedCentralGoogle Scholar
  8. Berchtold NC, Cribbs DH, Coleman PD, Rogers J, Head E, Kim R, Beach T, Miller C, Troncoso J, Trojanowski JQ, Zielke HR, Cotman CW (2008) Gene expression changes in the course of normal brain aging are sexually dimorphic. Proc Natl Acad Sci U S A 105:15605–15610. Published online Epub2008/10/07/.  https://doi.org/10.1073/pnas.0806883105 Google Scholar
  9. Bock C, Tomazou EM, Brinkman AB, Müller F, Simmer F, Gu H, Jäger N, Gnirke A, Stunnenberg HG, Meissner A (2010) Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotechnol 28:1106–1114. Published online Epub2010/10//.  https://doi.org/10.1038/nbt.1681 PubMedPubMedCentralGoogle Scholar
  10. Cassman M (2005) Barriers to progress in systems biology. Nature 438:1079. Published online Epub2005/12/22/.  https://doi.org/10.1038/4381079a PubMedGoogle Scholar
  11. Cevenini E, Invidia L, Lescai F, Salvioli S, Tieri P, Castellani G, Franceschi C (2008) Human models of aging and longevity. Expert Opin Biol Ther 8:1393–1405. Published online Epub2008/09//.  https://doi.org/10.1517/14712598.8.9.1393 PubMedGoogle Scholar
  12. Chen W, Qian W, Wu G, Chen W, Xian B, Chen X, Cao Y, Green CD, Zhao F, Tang K, Han JD (2015) Three-dimensional human facial morphologies as robust aging markers. Cell Res 25:574–587. Published online EpubMayPubMedPubMedCentralGoogle Scholar
  13. Chen KL, Crane MM, Kaeberlein M (2017) Microfluidic technologies for yeast replicative lifespan studies. Mech Ageing Dev 161:262–269. Published online Epub2017/01//.  https://doi.org/10.1016/j.mad.2016.03.009 PubMedGoogle Scholar
  14. Claesson MJ, Jeffery IB, Conde S, Power SE, O’Connor EM, Cusack S, Harris HMB, Coakley M, Lakshminarayanan B, O’Sullivan O, Fitzgerald GF, Deane J, O’Connor M, Harnedy N, O’Connor K, O’Mahony D, Sinderen D v, Wallace M, Brennan L, Stanton C, Marchesi JR, Fitzgerald AP, Shanahan F, Hill C, Ross RP, O’Toole PW (2012) Gut microbiota composition correlates with diet and health in the elderly. Nature 488:178. Published online Epub2012/08//.  https://doi.org/10.1038/nature11319 PubMedGoogle Scholar
  15. Deelen J, Beekman M, Uh H-W, Helmer Q, Kuningas M, Christiansen L, Kremer D, van der Breggen R, Suchiman HED, Lakenberg N, van den Akker EB, Passtoors WM, Tiemeier H, van Heemst D, de Craen AJ, Rivadeneira F, de Geus EJ, Perola M, van der Ouderaa FJ, Gunn DA, Boomsma DI, Uitterlinden AG, Christensen K, van Duijn CM, Heijmans BT, Houwing-Duistermaat JJ, Westendorp RGJ, Slagboom PE (2011) Genome-wide association study identifies a single major locus contributing to survival into old age; the APOE locus revisited. Aging Cell 10:686–698. Published online Epub2011/08//.  https://doi.org/10.1111/j.1474-9726.2011.00705.x PubMedPubMedCentralGoogle Scholar
  16. de Magalhães JP, Curado J, Church GM (2009) Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics (Oxford, England) 25:875–881. Published online Epub2009/04/01/.  https://doi.org/10.1093/bioinformatics/btp073 PubMedPubMedCentralGoogle Scholar
  17. Enge M, Arda HE, Mignardi M, Beausang J, Bottino R, Kim SK, Quake SR (2017) Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns. Cell 171:321–330.e314. Published online Epub2017/10/05/.  https://doi.org/10.1016/j.cell.2017.09.004 PubMedPubMedCentralGoogle Scholar
  18. Erikson GA, Bodian DL, Rueda M, Molparia B, Scott ER, Scott-Van Zeeland AA, Topol SE, Wineinger NE, Niederhuber JE, Topol EJ, Torkamani A (2016) Whole-genome sequencing of a healthy aging cohort. Cell 0. Published online Epub2016/04/21/.  https://doi.org/10.1016/j.cell.2016.03.022 PubMedPubMedCentralGoogle Scholar
  19. Ewbank DC (2007) Differences in the association between apolipoprotein E genotype and mortality across populations. J Gerontol A Biol Sci Med Sci 62:899–907. Published online Epub2007/08//Google Scholar
  20. Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, Haw R, Jassal B, Korninger F, May B, Milacic M, Roca CD, Rothfels K, Sevilla C, Shamovsky V, Shorser S, Varusai T, Viteri G, Weiser J, Wu G, Stein L, Hermjakob H, P. D’Eustachio (2017) The Reactome pathway knowledgebase. Nucleic Acids Res. Published online Epub2017/11/14/.  https://doi.org/10.1093/nar/gkx1132 PubMedCentralGoogle Scholar
  21. Faisal FE, Milenković T (2014) Dynamic networks reveal key players in aging. Bioinformatics (Oxford, England) 30:1721–1729. Published online Epub2014/06/15/.  https://doi.org/10.1093/bioinformatics/btu089 PubMedGoogle Scholar
  22. Flachsbart F, Caliebe A, Kleindorp R, Blanché H, von Eller-Eberstein H, Nikolaus S, Schreiber S, Nebel A (2009) Association of FOXO3A variation with human longevity confirmed in German centenarians. Proc Natl Acad Sci U S A 106:2700–2705. Published online Epub2009/02/24/.  https://doi.org/10.1073/pnas.0809594106 Google Scholar
  23. Friedman N (2004) Inferring cellular networks using probabilistic graphical models. Science 303:799–805. Published online Epub2004/02/06/.  https://doi.org/10.1126/science.1094068 PubMedGoogle Scholar
  24. Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28:337–374. Published online Epub2000/04//.  https://doi.org/10.1214/aos/1016218223 Google Scholar
  25. Gerdes LU, Jeune B, Ranberg KA, Nybo H, Vaupel JW (2000) Estimation of apolipoprotein E genotype-specific relative mortality risks from the distribution of genotypes in centenarians and middle-aged men: apolipoprotein E gene is a “frailty gene,” not a “longevity gene”. Genet Epidemiol 19:202–210. Published online Epub2000/10//. https://doi.org/10.1002/1098-2272(200010)19:3<202::AID-GEPI2>3.0.CO;2-QGoogle Scholar
  26. Glass D, Viñuela A, Davies MN, Ramasamy A, Parts L, Knowles D, Brown AA, Hedman AK, Small KS, Buil A, Grundberg E, Nica AC, Di Meglio P, Nestle FO, Ryten M, U. K. B. E. Consortium, Mu TC, Durbin R, McCarthy MI, Deloukas P, Dermitzakis ET, Weale ME, Bataille V, Spector TD (2013) Gene expression changes with age in skin, adipose tissue, blood and brain. Genome Biol 14:R75. Published online Epub2013/07/26/.  https://doi.org/10.1186/gb-2013-14-7-r75 PubMedPubMedCentralGoogle Scholar
  27. Gonzalez-Covarrubias V, Beekman M, Uh H-W, Dane A, Troost J, Paliukhovich I, van der Kloet FM, Houwing-Duistermaat J, Vreeken RJ, Hankemeier T, Slagboom EP (2013) Lipidomics of familial longevity. Aging Cell 12:426–434. Published online Epub2013/06//.  https://doi.org/10.1111/acel.12064 PubMedPubMedCentralGoogle Scholar
  28. Green CD, Huang Y, Dou X, Yang L, Liu Y, Han J-DJ (2017) Impact of dietary interventions on noncoding RNA networks and mRNAs encoding chromatin-related factors. Cell Rep 18:2957–2968. Published online Epub2017/03//.  https://doi.org/10.1016/j.celrep.2017.03.001 PubMedGoogle Scholar
  29. Harris RA, Wang T, Coarfa C, Nagarajan RP, Hong C, Downey SL, Johnson BE, Fouse SD, Delaney A, Zhao Y, Olshen A, Ballinger T, Zhou X, Forsberg KJ, Gu J, Echipare L, O’Geen H, Lister R, Pelizzola M, Xi Y, Epstein CB, Bernstein BE, Hawkins RD, Ren B, Chung W-Y, Gu H, Bock C, Gnirke A, Zhang MQ, Haussler D, Ecker JR, Li W, Farnham PJ, Waterland RA, Meissner A, Marra MA, Hirst M, Milosavljevic A, Costello JF (2010) Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nat Biotechnol 28:1097–1105. Published online Epub2010/10//.  https://doi.org/10.1038/nbt.1682 PubMedPubMedCentralGoogle Scholar
  30. Hjelmborg JV, Iachine I, Skytthe A, Vaupel JW, McGue M, Koskenvuo M, Kaprio J, Pedersen NL, Christensen K (2006) Genetic influence on human lifespan and longevity. Hum Genet 119:312. Published online Epub2006/04/01/.  https://doi.org/10.1007/s00439-006-0144-y Google Scholar
  31. Horvath S, Zhang Y, Langfelder P, Kahn RS, Boks MPM, van Eijk K, van den Berg LH, Ophoff RA (2012) Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol 13:R97. Published online Epub2012/10/03/.  https://doi.org/10.1186/gb-2012-13-10-r97 PubMedPubMedCentralGoogle Scholar
  32. Hou L, Wang D, Chen D, Liu Y, Zhang Y, Cheng H, Xu C, Sun N, McDermott J, Mair WB, Han J-DJ A Systems Approach (2016) To reverse engineer lifespan extension by dietary restriction. Cell Metab 23:529–540. Published online Epub2016/03//.  https://doi.org/10.1016/j.cmet.2016.02.002 PubMedPubMedCentralGoogle Scholar
  33. Joshi PK, Pirastu N, Kentistou KA, Fischer K, Hofer E, Schraut KE, Clark DW, Nutile T, Barnes CLK, Timmers PRHJ, Shen X, Gandin I, McDaid AF, Hansen TF, Gordon SD, Giulianini F, Boutin TS, Abdellaoui A, Zhao W, Medina-Gomez C, Bartz TM, Trompet S, Lange LA, Raffield L, Spek A, Galesloot TE, Proitsi P, Yanek LR, Bielak LF, Payton A, Murgia F, Concas MP, Biino G, Tajuddin SM, Seppälä I, Amin N, Boerwinkle E, Børglum AD, Campbell A, Demerath EW, Demuth I, Faul JD, Ford I, Gialluisi A, Gögele M, Graff M, Hingorani A, Hottenga J-J, Hougaard DM, Hurme MA, Ikram MA, Jylhä M, Kuh D, Ligthart L, Lill CM, Lindenberger U, Lumley T, Mägi R, Marques-Vidal P, Medland SE, Milani L, Nagy R, Ollier WER, Peyser PA, Pramstaller PP, Ridker PM, Rivadeneira F, Ruggiero D, Saba Y, Schmidt R, Schmidt H, Slagboom PE, Smith BH, Smith JA, Sotoodehnia N, Steinhagen-Thiessen E, Rooij FJA, Verbeek AL, Vermeulen SH, Vollenweider P, Wang Y, Werge T, Whitfield JB, Zonderman AB, Lehtimäki T, Evans MK, Pirastu M, Fuchsberger C, Bertram L, Pendleton N, Kardia SLR, Ciullo M, Becker DM, Wong A, Psaty BM, Duijn CM, Wilson JG, Jukema JW, Kiemeney L, Uitterlinden AG, Franceschini N, North KE, Weir DR, Metspalu A, Boomsma DI, Hayward C, Chasman D, Martin NG, Sattar N, Campbell H, Esko T, Kutalik Z, Wilson JF (2017) Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity. Nat Commun 8:910. Published online Epub2017/10/13/.  https://doi.org/10.1038/s41467-017-00934-5
  34. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45:D353–D361. Published online Epub2017/01/04/.  https://doi.org/10.1093/nar/gkw1092 PubMedPubMedCentralGoogle Scholar
  35. Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival B, Assad-Garcia N, Glass JI, Covert MW (2012) A whole-cell computational model predicts phenotype from genotype. Cell 150:389–401. Published online Epub2012/07/20/.  https://doi.org/10.1016/j.cell.2012.05.044 PubMedPubMedCentralGoogle Scholar
  36. Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, Balakrishnan L, Marimuthu A, Banerjee S, Somanathan DS, Sebastian A, Rani S, Ray S, Harrys Kishore CJ, Kanth S, Ahmed M, Kashyap MK, Mohmood R, Ramachandra YL, Krishna V, Rahiman BA, Mohan S, Ranganathan P, Ramabadran S, Chaerkady R, Pandey A (2009) Human protein reference database – 2009 update. Nucleic Acids Res 37:D767–D772. Published online Epub2009/01//.  https://doi.org/10.1093/nar/gkn892 PubMedGoogle Scholar
  37. Kowald A, Jendrach M, Pohl S, Bereiter-Hahn J, Hammerstein P (2005) On the relevance of mitochondrial fusions for the accumulation of mitochondrial deletion mutants: a modelling study. Aging Cell 4:273–283. Published online Epub2005/10//.  https://doi.org/10.1111/j.1474-9726.2005.00169.x PubMedGoogle Scholar
  38. Krištić J, Vučković F, Menni C, Klarić L, Keser T, Beceheli I, Pučić-Baković M, Novokmet M, Mangino M, Thaqi K, Rudan P, Novokmet N, Sarac J, Missoni S, Kolčić I, Polašek O, Rudan I, Campbell H, Hayward C, Aulchenko Y, Valdes A, Wilson JF, Gornik O, Primorac D, Zoldoš V, Spector T, Lauc G (2014) Glycans are a novel biomarker of chronological and biological ages. J Gerontol A Biol Sci Med Sci 69:779–789. Published online Epub2014/07//.  https://doi.org/10.1093/gerona/glt190 Google Scholar
  39. Krumsiek J, Suhre K, Illig T, Adamski J, Theis FJ (2011) Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Syst Biol 5:21. Published online Epub2011/01/31/.  https://doi.org/10.1186/1752-0509-5-21 PubMedPubMedCentralGoogle Scholar
  40. Lawton KA, Berger A, Mitchell M, Milgram KE, Evans AM, Guo L, Hanson RW, Kalhan SC, Ryals JA, Milburn MV (2008) Analysis of the adult human plasma metabolome. Pharmacogenomics 9:383–397. Published online Epub2008/04//.  https://doi.org/10.2217/14622416.9.4.383 PubMedGoogle Scholar
  41. Li JE, Liu Y, Liu M, Han J-DJ (2013) Functional dissection of regulatory models using gene expression data of deletion mutants. PLoS Genet 9:e1003757. Published online Epub2013.  https://doi.org/10.1371/journal.pgen.1003757 PubMedPubMedCentralGoogle Scholar
  42. Li S, Sullivan NL, Rouphael N, Yu T, Banton S, Maddur MS, McCausland M, Chiu C, Canniff J, Dubey S, Liu K, Tran V, Hagan T, Duraisingham S, Wieland A, Mehta AK, Whitaker JA, Subramaniam S, Jones DP, Sette A, Vora K, Weinberg A, Mulligan MJ, Nakaya HI, Levin M, Ahmed R, Pulendran B (2017) Metabolic phenotypes of response to vaccination in humans. Cell 169:862–877.e817. Published online Epub2017/05/18/.  https://doi.org/10.1016/j.cell.2017.04.026 PubMedPubMedCentralGoogle Scholar
  43. Ljungquist B, Berg S, Lanke J, McClearn GE, Pedersen NL (1998) The effect of genetic factors for longevity: a comparison of identical and fraternal twins in the Swedish Twin Registry. J Gerontol A Biol Sci Med Sci 53:M441–M446. Published online Epub1998/11//Google Scholar
  44. Lu T, Pan Y, Kao S-Y, Li C, Kohane I, Chan J, Yankner BA (2004) Gene regulation and DNA damage in the ageing human brain. Nature 429:883–891. Published online Epub2004/06/24/.  https://doi.org/10.1038/nature02661 PubMedGoogle Scholar
  45. Lu Y, Biancotto A, Cheung F, Remmers E, Shah N, McCoy JP, Tsang JS (2016) Systematic analysis of cell-to-cell expression variation of T lymphocytes in a human cohort identifies aging and genetic associations. Immunity 45:1162–1175. Published online Epub2016/11/15/.  https://doi.org/10.1016/j.immuni.2016.10.025 PubMedPubMedCentralGoogle Scholar
  46. Marbach D, Costello JC, Küffner R, Vega N, Prill RJ, Camacho DM, Allison KR, Kellis M, Collins JJ, Stolovitzky G (2012) Wisdom of crowds for robust gene network inference. Nat Methods 9:796–804. Published online Epub2012/07/15/.  https://doi.org/10.1038/nmeth.2016 PubMedPubMedCentralGoogle Scholar
  47. Martens L, Vizcaíno JA (2017) A golden age for working with public proteomics data. Trends Biochem Sci 42:333–341. Published online Epub2017/05/01/.  https://doi.org/10.1016/j.tibs.2017.01.001 PubMedPubMedCentralGoogle Scholar
  48. Martinez-Jimenez CP, Eling N, Chen H-C, Vallejos CA, Kolodziejczyk AA, Connor F, Stojic L, Rayner TF, Stubbington MJT, Teichmann SA, Roche Mdl, Marioni JC, Odom DT (2017) Aging increases cell-to-cell transcriptional variability upon immune stimulation. Science 355:1433–1436. Published online Epub2017/03/31/.  https://doi.org/10.1126/science.aah4115 PubMedPubMedCentralGoogle Scholar
  49. Mayer RL, Schwarzmeier JD, Gerner MC, Bileck A, Mader JC, Meier-Menches SM, Gerner SM, Schmetterer KG, Pukrop T, Reichle A, Slany A, Gerner C (2017) Proteomics and metabolomics identify molecular mechanisms of aging potentially predisposing for chronic lymphocytic leukemia. Mol Cell Proteomics. Published online Epub2017/12/01/.  https://doi.org/10.1074/mcp.RA117.000425 Google Scholar
  50. Menni C, Kastenmüller G, Petersen AK, Bell JT, Psatha M, Tsai P-C, Gieger C, Schulz H, Erte I, John S, Brosnan MJ, Wilson SG, Tsaprouni L, Lim EM, Stuckey B, Deloukas P, Mohney R, Suhre K, Spector TD, Valdes AM (2013) Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int J Epidemiol 42:1111–1119. Published online Epub2013/08//.  https://doi.org/10.1093/ije/dyt094 PubMedPubMedCentralGoogle Scholar
  51. Miller JA, Oldham MC, Geschwind DH (2008) A systems level analysis of transcriptional changes in Alzheimer’s disease and normal aging. J Neurosci: Off J Soc Neurosci 28:1410–1420. Published online Epub2008/02/06/.  https://doi.org/10.1523/JNEUROSCI.4098-07.2008 PubMedGoogle Scholar
  52. Moayyeri A, Hammond CJ, Valdes AM, Spector TD (2013) Cohort profile: Twins UK and healthy ageing twin study. Int J Epidemiol 42:76–85. Published online Epub2013/02//.  https://doi.org/10.1093/ije/dyr207 PubMedPubMedCentralGoogle Scholar
  53. McAuley MT, Kenny RA, Kirkwood TBL, Wilkinson DJ, Jones JJL, Miller VM (2009) A mathematical model of aging-related and cortisol induced hippocampal dysfunction. BMC Neurosci 10:26. Published online Epub2009/03/25/.  https://doi.org/10.1186/1471-2202-10-26
  54. Murphy RA, Moore SC, Playdon M, Meirelles O, Newman AB, Milijkovic I, Kritchevsky SB, Schwartz A, Goodpaster BH, Sampson J, Cawthon P, Simonsick EM, Gerszten RE, Clish CB, Harris TB, A. B. C. S. Health (2017) Metabolites associated with lean mass and adiposity in older black men. J Gerontol A Biol Sci Med Sci 72:1352–1359. Published online Epub2017/10/01/.  https://doi.org/10.1093/gerona/glw245
  55. Pagel P, Kovac S, Oesterheld M, Brauner B, Dunger-Kaltenbach I, Frishman G, Montrone C, Mark P, Stümpflen V, Mewes H-W, Ruepp A, Frishman D (2005) The MIPS mammalian protein-protein interaction database. Bioinformatics (Oxford, England) 21:832–834. Published online Epub2005/03//.  https://doi.org/10.1093/bioinformatics/bti115 PubMedGoogle Scholar
  56. Paneni F, Diaz Cañestro C, Libby P, Lüscher TF, Camici GG (2017) The aging cardiovascular system: understanding it at the cellular and clinical levels. J Am Coll Cardiol 69:1952–1967. Published online Epub2017/04/18/.  https://doi.org/10.1016/j.jacc.2017.01.064 PubMedGoogle Scholar
  57. Pawlikowska L, Hu D, Huntsman S, Sung A, Chu C, Chen J, Joyner AH, Schork NJ, Hsueh W-C, Reiner AP, Psaty BM, Atzmon G, Barzilai N, Cummings SR, Browner WS, Kwok P-Y, Ziv E, F. Study of Osteoporotic (2009) Association of common genetic variation in the insulin/IGF1 signaling pathway with human longevity. Aging Cell 8:460–472. Published online Epub2009/08//.  https://doi.org/10.1111/j.1474-9726.2009.00493.x PubMedPubMedCentralGoogle Scholar
  58. Peters MJ, Joehanes R, Pilling LC, Schurmann C, Conneely KN, Powell J, Reinmaa E, Sutphin GL, Zhernakova A, Schramm K, Wilson YA, Kobes S, Tukiainen T, C. Nabec/Ukbec, Ramos YF, Göring HHH, Fornage M, Liu Y, Gharib SA, Stranger BE, De Jager PL, Aviv A, Levy D, Murabito JM, Munson PJ, Huan T, Hofman A, Uitterlinden AG, Rivadeneira F, van Rooij J, Stolk L, Broer L, Verbiest MMPJ, Jhamai M, Arp P, Metspalu A, Tserel L, Milani L, Samani NJ, Peterson P, Kasela S, Codd V, Peters A, Ward-Caviness CK, Herder C, Waldenberger M, Roden M, Singmann P, Zeilinger S, Illig T, Homuth G, Grabe H-J, Völzke H, Steil L, Kocher T, Murray A, Melzer D, Yaghootkar H, Bandinelli S, Moses EK, Kent JW, Curran JE, Johnson MP, Williams-Blangero S, Westra H-J, McRae AF, Smith JA, Kardia SLR, Hovatta I, Perola M, Ripatti S, Salomaa V, Henders AK, Martin NG, Smith AK, Mehta D, Binder EB, Nylocks KM, Kennedy EM, Klengel T, Ding J, Suchy-Dicey AM, Enquobahrie DA, Brody J, Rotter JI, Chen Y-DI, Houwing-Duistermaat J, Kloppenburg M, Slagboom PE, Helmer Q, den Hollander W, Bean S, Raj T, Bakhshi N, Wang QP, Oyston LJ, Psaty BM, Tracy RP, Montgomery GW, Turner ST, Blangero J, Meulenbelt I, Ressler KJ, Yang J, Franke L, Kettunen J, Visscher PM, Neely GG, Korstanje R, Hanson RL, Prokisch H, Ferrucci L, Esko T, Teumer A, van Meurs JBJ, Johnson AD (2015) The transcriptional landscape of age in human peripheral blood. Nat Commun 6:8570. Published online Epub2015/10/22/.  https://doi.org/10.1038/ncomms9570
  59. Pittman WE, Sinha DB, Zhang WB, Kinser HE, Pincus Z (2017) A simple culture system for long-term imaging of individual C. elegans. Lab Chip 17:3909–3920. Published online Epub2017/11/07/.  https://doi.org/10.1039/C7LC00916J PubMedPubMedCentralGoogle Scholar
  60. Proctor CJ, Soti C, Boys RJ, Gillespie CS, Shanley DP, Wilkinson DJ, Kirkwood TBL (2005) Modelling the actions of chaperones and their role in ageing. Mech Ageing Dev 126:119–131. Published online Epub2005/01//.  https://doi.org/10.1016/j.mad.2004.09.031 PubMedGoogle Scholar
  61. Przybilla J, Rohlf T, Loeffler M, Galle J (2014) Understanding epigenetic changes in aging stem cells – a computational model approach. Aging Cell 13:320–328. Published online Epub2014/04//.  https://doi.org/10.1111/acel.12177 PubMedPubMedCentralGoogle Scholar
  62. Rakyan VK, Down TA, Maslau S, Andrew T, Yang T-P, Beyan H, Whittaker P, McCann OT, Finer S, Valdes AM, Leslie RD, Deloukas P, Spector TD (2010) Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains. Genome Res 20:434–439. Published online Epub2010/04//.  https://doi.org/10.1101/gr.103101.109 PubMedPubMedCentralGoogle Scholar
  63. Rockwood K, Mitnitski A (2007) Frailty in relation to the accumulation of deficits. J Gerontol A Biol Sci Med Sci 62:722–727. Published online Epub2007/07//Google Scholar
  64. Rodwell GEJ, Sonu R, Zahn JM, Lund J, Wilhelmy J, Wang L, Xiao W, Mindrinos M, Crane E, Segal E, Myers BD, Brooks JD, Davis RW, Higgins J, Owen AB, Kim SK (2004) A transcriptional profile of aging in the human kidney. PLoS Biol 2:e427. Published online Epub2004/12//.  https://doi.org/10.1371/journal.pbio.0020427 PubMedPubMedCentralGoogle Scholar
  65. Röst HL, Sachsenberg T, Aiche S, Bielow C, Weisser H, Aicheler F, Andreotti S, Ehrlich H-C, Gutenbrunner P, Kenar E, Liang X, Nahnsen S, Nilse L, Pfeuffer J, Rosenberger G, Rurik M, Schmitt U, Veit J, Walzer M, Wojnar D, Wolski WE, Schilling O, Choudhary JS, Malmström L, Aebersold R, Reinert K, Kohlbacher O (2016) OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods 13:741–748. Published online Epub2016/08/30/.  https://doi.org/10.1038/nmeth.3959 PubMedGoogle Scholar
  66. Ruhaak LR, Uh H-W, Beekman M, Hokke CH, Westendorp RGJ, Houwing-Duistermaat J, Wuhrer M, Deelder AM, Slagboom PE (2011) Plasma protein N-glycan profiles are associated with calendar age, familial longevity and health. J Proteome Res 10:1667–1674. Published online Epub2011/04/01/.  https://doi.org/10.1021/pr1009959 PubMedGoogle Scholar
  67. Sebastiani P, Gurinovich A, Bae H, Andersen S, Malovini A, Atzmon G, Villa F, Kraja AT, Ben-Avraham D, Barzilai N, Puca A, Perls TT (2017) Four genome-wide association studies identify new extreme longevity variants. J Gerontology: Ser A 72:1453–1464. Published online Epub2017/10/12/.  https://doi.org/10.1093/gerona/glx027 Google Scholar
  68. Skytthe A, Pedersen NL, Kaprio J, Stazi MA, Hjelmborg JVB, Iachine I, Vaupel JW, Christensen K (2003) Longevity studies in GenomEUtwin. Twin Res 6:448–454. Published online Epub2003/10//.  https://doi.org/10.1375/136905203770326457 PubMedGoogle Scholar
  69. Sozou PD, Kirkwood TB (2001) A stochastic model of cell replicative senescence based on telomere shortening, oxidative stress, and somatic mutations in nuclear and mitochondrial DNA. J Theor Biol 213:573–586. Published online Epub2001/12/21/.  https://doi.org/10.1006/jtbi.2001.2432 PubMedGoogle Scholar
  70. Suo J, Chen X, Shan S, Gao W, Dai Q (2012) A concatenational graph evolution aging model. IEEE Trans Pattern Anal Mach Intell 34:2083–2096. Published online Epub2012/11//.  https://doi.org/10.1109/TPAMI.2012.22 PubMedGoogle Scholar
  71. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, Jensen LJ, von Mering C (2017) The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 45:D362–D368. Published online Epub2017/01/04/.  https://doi.org/10.1093/nar/gkw937 PubMedPubMedCentralGoogle Scholar
  72. Tain LS, Sehlke R, Jain C, Chokkalingam M, Nagaraj N, Essers P, Rassner M, Grönke S, Froelich J, Dieterich C, Mann M, Alic N, Beyer A, Partridge L (2017) A proteomic atlas of insulin signalling reveals tissue-specific mechanisms of longevity assurance. Mol Syst Biol 13:939. Published online Epub2017/09/15/PubMedPubMedCentralGoogle Scholar
  73. Tvardovskiy A, Schwämmle V, Kempf SJ, Rogowska-Wrzesinska A, Jensen ON (2017) Accumulation of histone variant H3.3 with age is associated with profound changes in the histone methylation landscape. Nucleic Acids Res 45:9272–9289. Published online Epub2017/09/19/.  https://doi.org/10.1093/nar/gkx696 PubMedPubMedCentralGoogle Scholar
  74. Wang J, Zhang S, Wang Y, Chen L, Zhang X-S (2009) Disease-aging network reveals significant roles of aging genes in connecting genetic diseases. PLoS Comput Biol 5:e1000521. Published online Epub2009/09//.  https://doi.org/10.1371/journal.pcbi.1000521 PubMedPubMedCentralGoogle Scholar
  75. Weidner CI, Lin Q, Koch CM, Eisele L, Beier F, Ziegler P, Bauerschlag DO, Jöckel K-H, Erbel R, Mühleisen TW, Zenke M, Brümmendorf TH, Wagner W (2014) Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol 15:R24. Published online Epub2014.  https://doi.org/10.1186/gb-2014-15-2-r24 PubMedPubMedCentralGoogle Scholar
  76. West J, Beck S, Wang X, Teschendorff AE (2013) An integrative network algorithm identifies age-associated differential methylation interactome hotspots targeting stem-cell differentiation pathways. Sci Rep 3:1630. Published online Epub2013/04/09/.  https://doi.org/10.1038/srep01630
  77. Wilhelm M, Schlegl J, Hahne H, Gholami AM, Lieberenz M, Savitski MM, Ziegler E, Butzmann L, Gessulat S, Marx H, Mathieson T, Lemeer S, Schnatbaum K, Reimer U, Wenschuh H, Mollenhauer M, Slotta-Huspenina J, Boese J-H, Bantscheff M, Gerstmair A, Faerber F, Kuster B (2014) Mass-spectrometry-based draft of the human proteome. Nature 509:582. Published online Epub2014/05//.  https://doi.org/10.1038/nature13319 PubMedGoogle Scholar
  78. Willcox BJ, Donlon TA, He Q, Chen R, Grove JS, Yano K, Masaki KH, Willcox DC, Rodriguez B, Curb JD (2008) FOXO3A genotype is strongly associated with human longevity. Proc Natl Acad Sci U S A 105:13987–13992. Published online Epub2008/09/16/.  https://doi.org/10.1073/pnas.0801030105 Google Scholar
  79. Xia K, Xue H, Dong D, Zhu S, Wang J, Zhang Q, Hou L, Chen H, Tao R, Huang Z, Fu Z, Chen Y-G, Han J-DJ (2006) Identification of the proliferation/differentiation switch in the cellular network of multicellular organisms. PLoS Comput Biol 2:e145. Published online Epub2006/11/24/.  https://doi.org/10.1371/journal.pcbi.0020145 PubMedPubMedCentralGoogle Scholar
  80. Xian B, Shen J, Chen W, Sun N, Qiao N, Jiang D, Yu T, Men Y, Han Z, Pang Y, Kaeberlein M, Huang Y, Han J-DJ (2013) WormFarm: a quantitative control and measurement device toward automated Caenorhabditis elegans aging analysis. Aging Cell 12:398–409. Published online Epub2013/06//.  https://doi.org/10.1111/acel.12063 PubMedGoogle Scholar
  81. Yu Z, Zhai G, Singmann P, He Y, Xu T, Prehn C, Römisch-Margl W, Lattka E, Gieger C, Soranzo N, Heinrich J, Standl M, Thiering E, Mittelstraß K, Wichmann H-E, Peters A, Suhre K, Li Y, Adamski J, Spector TD, Illig T, Wang-Sattler R (2012) Human serum metabolic profiles are age dependent. Aging Cell 11:960–967. Published online Epub2012/12//.  https://doi.org/10.1111/j.1474-9726.2012.00865.x PubMedPubMedCentralGoogle Scholar
  82. Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4, Article17. Published online Epub2005.  https://doi.org/10.2202/1544-6115.1128
  83. Zierer J, Menni C, Kastenmüller G, Spector TD (2015) Integration of ‘omics’ data in aging research: from biomarkers to systems biology. Aging Cell 14:933–944. Published online Epub2015/12/01/.  https://doi.org/10.1111/acel.12386 PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.CAS Key Laboratory of Computational BiologyCAS-MPG Partner Institute for Computational BiologyShanghaiChina
  2. 2.CAS Center for Excellence in Molecular Cell ScienceShanghai Institute of Nutrition and HealthShanghaiChina
  3. 3.Shanghai Institutes for Biological SciencesUniversity of Chinese Academy of SciencesShanghaiChina

Personalised recommendations

Cite chapter

Buy options