Skip to main content
SpringerLink
Log in

The Robustness Continuum

  • Chapter
  • First Online:
Evolutionary Systems Biology

Part of the book series: Advances in Experimental Medicine and Biology ((volume 751))

Abstract

Organisms are subject to random changes in their external environments, as well as in their internal components. A central goal of evolutionary systems biology is to understand how living systems cope with—and in some cases exploit—this variation. Many cellular and developmental processes operate with high fidelity to produce stereotyped, irreversible outcomes despite environmental and genetic perturbation. These processes are said to be robust or insensitive to variation. Robustness can lead to single, invariant phenotypes, or it can take the form of phenotypic plasticity, in which different environmental conditions reproducibly induce distinct phenotypes. Some organisms cope with environmental variation not with robust responses but with stochastic, reversible fate decisions. In those organisms, lower robustness yields heterogeneity among individuals, which in turn serves as a bet-hedging mechanism for the population. Considering high-fidelity and bet-hedging processes together—as a robustness continuum—provides a unifying framework for analyzing and conceptualizing variation in complex evolving systems. This framework can be applied to understanding the architectures and dynamics of the regulatory networks that underlie fate decisions in microbes, plants, animals, and cancer cells.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Masel J, Siegal ML (2009) Robustness: mechanisms and consequences. Trends Genet 25:395–403. doi:10.1016/j.tig.2009.07.005

    PubMed  CAS  Google Scholar 

  2. Wagner A (2007) Robustness and evolvability in living systems, 1st edn. Princeton University Press Princeton

    Google Scholar 

  3. Bergman A, Siegal M (2003) Evolutionary capacitance as a general feature of complex gene networks. Nature 424:549–552. doi:10.1038/nature01765

    PubMed  CAS  Google Scholar 

  4. Draghi JA, Parsons TL, Wagner GP, Plotkin JB (2010) Mutational robustness can facilitate adaptation. Nature 463:353–355. doi:10.1038/nature08694

    PubMed  CAS  Google Scholar 

  5. Bloom JD, Labthavikul ST, Otey CR, Arnold FH (2006) Protein stability promotes evolvability. Proc Natl Acad Sci USA 103:5869–5874. doi:10.1073/pnas.0510098103

    PubMed  CAS  Google Scholar 

  6. van Nimwegen E (2006) Epidemiology. Influenza escapes immunity along neutral networks. Science 314:1884–1886. doi:10.1126/science.1137300

    Google Scholar 

  7. Blount ZD, Borland CZ, Lenski RE (2008) Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proc Natl Acad Sci USA 105:7899–7906. doi:10.1073/pnas.0803151105

    PubMed  CAS  Google Scholar 

  8. Cambray G, Mazel D (2008) Synonymous genes explore different evolutionary landscapes. PLoS Genet 4:e1000256. doi:10.1371/journal.pgen.1000256

    PubMed  Google Scholar 

  9. Isalan M, Lemerle C, Michalodimitrakis K, Horn C, Beltrao P, et al. (2008) Evolvability and hierarchy in rewired bacterial gene networks. Nature 452:840–845. doi:10.1038/nature06847

    PubMed  CAS  Google Scholar 

  10. Ogbunugafor CB, McBride RC, Turner PE (2009) Predicting virus evolution: the relationship between genetic robustness and evolvability of thermotolerance. Cold Spring Harb Symp Quant Biol 74:109–118. doi:10.1101/sqb.2009.74.023

    PubMed  CAS  Google Scholar 

  11. McBride RC, Ogbunugafor CB, Turner PE (2008) Robustness promotes evolvability of thermotolerance in an RNA virus. BMC Evol Biol 8:231. doi:10.1186/1471–2148–8–231

    PubMed  Google Scholar 

  12. Wagner A (2011) The molecular origins of evolutionary innovations. Trends Genet 27:397–410. doi:10.1016/j.tig.2011.06.002

    PubMed  CAS  Google Scholar 

  13. Seger J, Brockmann H (1987) What is bet-hedging? In: Harvey PH, Partridge L (eds) Oxford surveys in evolutionary biology. Oxford University Press, USA 4:182–211

    Google Scholar 

  14. Kussell E, Leibler S (2005) Phenotypic diversity, population growth, and information in fluctuating environments. Science 309:2075–2078. doi:10.1126/science.1114383

    PubMed  CAS  Google Scholar 

  15. Kussell E, Kishony R, Balaban NQ, Leibler S (2005) Bacterial persistence: a model of survival in changing environments. Genetics 169:1807–14. doi:10.1534/genetics.104.035352

    PubMed  Google Scholar 

  16. King OD, Masel J (2007) The evolution of bet-hedging adaptations to rare scenarios. Theor Popul Biol 72:560–575. doi:10.1016/j.tpb.2007.08.006

    PubMed  Google Scholar 

  17. Thattai M, van Oudenaarden A (2004) Stochastic gene expression in fluctuating environments. Genetics 167:523–530. doi:10.1534/genetics.167.1.523

    PubMed  Google Scholar 

  18. Wolf DM, Vazirani VV, Arkin AP (2005) Diversity in times of adversity: probabilistic strategies in microbial survival games. J Theor Biol 234:227–253. doi:10.1016/j.jtbi.2004.11.020

    PubMed  Google Scholar 

  19. Cooper WS, Kaplan RH (1982) Adaptive “coin-flipping”: a decision-theoretic examination of natural selection for random individual variation. J Theor Biol 94:135–151. doi:10.1016/0022-5193(82)90336-8

    PubMed  CAS  Google Scholar 

  20. Haccou P, Iwasa Y (1995) Optimal mixed strategies in stochastic environments. Theor Popul Biol 47:212–243. doi:10.1006/tpbi.1995.1009

    Google Scholar 

  21. Cohen D (1966) Optimizing reproduction in a randomly varying environment. J Theor Biol 12:119–129. doi:10.1016/0022-5193(66)90188-3

    PubMed  CAS  Google Scholar 

  22. Novick A, Weiner M (1957) Enzyme induction as an all-or-none phenomenon. Proc Natl Acad Sci USA 43:553–566

    PubMed  CAS  Google Scholar 

  23. Ozbudak EM, Thattai M, Lim HN, Shraiman BI, Van Oudenaarden A (2004) Multistability in the lactose utilization network of Escherichia coli. Nature 427:737–740. doi:10.1038/nature02298

    PubMed  CAS  Google Scholar 

  24. Vilar JMG, Guet CC, Leibler S (2003) Modeling network dynamics: the lac operon, a case study. J Cell Biol 161:471–476. doi:10.1083/jcb.200301125

    PubMed  CAS  Google Scholar 

  25. Choi PJ, Cai L, Frieda K, Xie XS (2008) A stochastic single-molecule event triggers phenotype switching of a bacterial cell. Science 322:442–446. doi:10.1126/science.1161427

    PubMed  CAS  Google Scholar 

  26. Koch AL (1983) The protein burden of lac operon products. J Mol Evol 19:455–462

    PubMed  CAS  Google Scholar 

  27. Jablonka E, Oborny B, Molnár I, Kisdi E, Hofbauer J, et al. (1995) The adaptive advantage of phenotypic memory in changing environments. Philos Trans R Soc Lond B Biol Sci 350:133–141. doi:10.1098/rstb.1995.0147

    PubMed  CAS  Google Scholar 

  28. Huang S (2009) Non-genetic heterogeneity of cells in development: more than just noise. Development 136:3853–3862. doi:10.1242/dev.035139

    PubMed  CAS  Google Scholar 

  29. Kitano H (2004) Biological robustness. Nat Rev Genet 5:826–837. doi:10.1038/nrg1471

    PubMed  CAS  Google Scholar 

  30. Johnsen PJ, Dubnau D, Levin BR (2009) Episodic selection and the maintenance of competence and natural transformation in Bacillus subtilis. Genetics 181:1521–1533. doi:10.1534/genetics.108.099523

    PubMed  CAS  Google Scholar 

  31. Cahn FH, Fox MS (1968) Fractionation of transformable bacteria from competent cultures of Bacillus subtilis on renografin gradients. J Bacteriol 95:867–875

    PubMed  CAS  Google Scholar 

  32. Hadden C, Nester EW (1968) Purification of competent cells in the Bacillus subtilis transformation system. J Bacteriol 95:876–885

    PubMed  CAS  Google Scholar 

  33. Nester EW, Stocker BA (1963) Biosynthetic latency in early stages of deoxyribonucleic acid transformation in Bacillus subtilis. J Bacteriol 86:785–796

    PubMed  CAS  Google Scholar 

  34. Haijema BJ, Hahn J, Haynes J, Dubnau D (2001) A ComGA-dependent checkpoint limits growth during the escape from competence. Mol Microbiol 40:52–64. doi:10.1046/j.1365–2958.2001.02363.x

    PubMed  CAS  Google Scholar 

  35. Hamoen LW, Venema G, Kuipers OP (2003) Controlling competence in Bacillus subtilis: shared use of regulators. Microbiology (Reading, Engl.) 149:9–17

    Google Scholar 

  36. Hamoen LW, Van Werkhoven AF, Bijlsma JJ, Dubnau D, Venema G (1998) The competence transcription factor of Bacillus subtilis recognizes short A/T-rich sequences arranged in a unique, flexible pattern along the DNA helix. Genes Dev 12:1539–1550. doi:10.1101/gad.12.10.1539

    PubMed  CAS  Google Scholar 

  37. Maamar H, Dubnau D (2005) Bistability in the Bacillus subtilis K-state (competence) system requires a positive feedback loop. Mol Microbiol 56:615–624. doi:10.1111/j.1365–2958.2005.04592.x

    PubMed  CAS  Google Scholar 

  38. Smits WK, Eschevins CC, Susanna KA, Bron S, Kuipers OP, et al (2005) Stripping Bacillus: ComK auto-stimulation is responsible for the bistable response in competence development. Mol Microbiol 56:604–614. doi:10.1111/j.1365–2958.2005.04488.x

    PubMed  CAS  Google Scholar 

  39. Avery SV (2005) Cell individuality: the bistability of competence development. Trends Microbiol 13:459–462. doi:10.1016/j.tim.2005.08.006

    PubMed  CAS  Google Scholar 

  40. Dubnau D, Roggiani M (1990) Growth medium-independent genetic competence mutants of Bacillus subtilis. J Bacteriol 172:4048–4055

    PubMed  CAS  Google Scholar 

  41. Sinderen D, Luttinger A, Kong L, Dubnau D, Venema G, et al (1995) comK encodes the competence transcription factor, the key regulatory protein for competence development in Bacillus subtilis. Mol Microbiol 15: 455–462. doi:10.1111/j.1365–2958.1995.tb02259.x

    PubMed  Google Scholar 

  42. Turgay K, Hahn J, Burghoorn J, Dubnau D (1998) Competence in Bacillus subtilis is controlled by regulated proteolysis of a transcription factor. EMBO J 17:6730–6738. doi:10.1093/emboj/17.22.6730

    PubMed  CAS  Google Scholar 

  43. D’Souza C, Nakano MM, Zuber P (1994) Identification of comS, a gene of the srfA operon that regulates the establishment of genetic competence in Bacillus subtilis. Proc Natl Acad Sci USA 91:9397–9401

    PubMed  Google Scholar 

  44. Hamoen LW, Eshuis H, Jongbloed J, Venema G, van Sinderen D (1995) A small gene, designated comS, located within the coding region of the fourth amino acid-activation domain of srfA, is required for competence development in Bacillus subtilis. Mol Microbiol 15:55–63. doi:10.1111/j.1365-2958.1995.tb02220.x

    PubMed  CAS  Google Scholar 

  45. Solomon JM, Magnuson R, Srivastava A, Grossman AD (1995) Convergent sensing pathways mediate response to two extracellular competence factors in Bacillus subtilis. Genes Dev 9:547–558. doi:10.1101/gad.9.5.547

    PubMed  CAS  Google Scholar 

  46. van Sinderen D, Venema G (1994) comK acts as an autoregulatory control switch in the signal transduction route to competence in Bacillus subtilis. J Bacteriol 176: 5762–5770

    PubMed  CAS  Google Scholar 

  47. Maamar H, Raj A, Dubnau D (2007) Noise in gene expression determines cell fate in Bacillus subtilis. Science 317:526–529. doi:10.1126/science.1140818

    PubMed  CAS  Google Scholar 

  48. Nester EW (1964) Penicillin resistance of competent cells in deoxyribonucleic acid transformation of Bacillus subtilis. J Bacteriol. 87:867–875

    PubMed  CAS  Google Scholar 

  49. Dubnau D, Losick R (2006) Bistability in bacteria. Mol Microbiol 61:564–572. doi:10.1111/j.1365–2958.2006.05249.x

    PubMed  CAS  Google Scholar 

  50. Leisner M, Stingl K, Rädler JO, Maier B (2007) Basal expression rate of comK sets a “switching-window” into the K-state of Bacillus subtilis. Mol Microbiol 63:1806–1816. doi:10.1111/j.1365–2958.2007.05628.x

    PubMed  CAS  Google Scholar 

  51. Süel GM, Kulkarni RP, Dworkin J, Garcia-Ojalvo J, Elowitz MB (2007) Tunability and noise dependence in differentiation dynamics. Science 315:1716–1719. doi:10.1126/science.1137455

    PubMed  Google Scholar 

  52. Beaumont HJE, Gallie J, Kost C, Ferguson GC, Rainey PB (2009) Experimental evolution of bet hedging. Nature 462:90–93. doi:10.1038/nature08504

    PubMed  CAS  Google Scholar 

  53. Orr HA (2009) Fitness and its role in evolutionary genetics. Nat Rev Genet 10:531–539. doi:10.1038/nrg2603

    PubMed  CAS  Google Scholar 

  54. Waddington C (1957) The strategy of the genes. London: George Allen & Unwin

    Google Scholar 

  55. Bigger J (1944) Treatment of Staphylococcal infections with penicillin by intermittent sterilisation. Lancet 244:497–500. doi:10.1016/S0140–6736(00)74210–3

    Google Scholar 

  56. Balaban NQ, Merrin J, Chait R, Kowalik L, Leibler S (2004) Bacterial persistence as a phenotypic switch. Science 305:1622–1625. doi:10.1126/science.1099390

    PubMed  CAS  Google Scholar 

  57. Gefen O, Balaban NQ (2009) The importance of being persistent: heterogeneity of bacterial populations under antibiotic stress. FEMS Microbiol Rev 33:704–717. doi:10.1111/j.1574–6976.2008.00156.x

    PubMed  CAS  Google Scholar 

  58. Maisonneuve E, Shakespeare LJ, Jørgensen MG, Gerdes K (2011) Bacterial persistence by RNA endonucleases. Proc Natal Acad Sci 108:13206–13211. doi:10.1073/pnas.1100186108

    CAS  Google Scholar 

  59. Crowe JH, Hoekstra FA, Crowe LM (1992) Anhydrobiosis. Ann Rev Physiol 54:579–599. doi:10.1146/annurev.ph.54.030192.003051

    CAS  Google Scholar 

  60. Winderickx J, de Winde JH, Crauwels M, Hino A, Hohmann S, et al. (1996) Regulation of genes encoding subunits of the trehalose synthase complex in Saccharomyces cerevisiae: novel variations of STRE-mediated transcription control? Mol Gen Genet 252:470–482

    PubMed  CAS  Google Scholar 

  61. Singer MA, Lindquist S (1998) Multiple effects of trehalose on protein folding in vitro and in vivo. Mol Cell 1:639–648. doi:10.1016/S1097-2765(00)80064-7

    PubMed  CAS  Google Scholar 

  62. Bandara A, Fraser S, Chambers PJ, Stanley GA (2009) Trehalose promotes the survival of Saccharomyces cerevisiae during lethal ethanol stress, but does not influence growth under sublethal ethanol stress. FEMS Yeast Res 9:1208–1216. doi:10.1111/j.1567–1364.2009.00569.x

    PubMed  CAS  Google Scholar 

  63. Hottiger T, Boller T, Wiemken A (1987) Rapid changes of heat and desiccation tolerance correlated with changes of trehalose content in Saccharomyces cerevisiae cells subjected to temperature shifts. FEBS Lett 220:113–115. doi:10.1016/0014-5793(87)80886-4

    PubMed  CAS  Google Scholar 

  64. Kandror O, Bretschneider N, Kreydin E Cavalieri D, Goldberg AL (2004) Yeast adapt to near-freezing temperatures by STRE/Msn2,4-dependent induction of trehalose synthesis and certain molecular chaperones. Mol Cell 13:771–781. doi:10.1016/S1097-2765(04)00148-0

    PubMed  CAS  Google Scholar 

  65. Levy SF, Ziv N, Siegal ML (2012) Bet hedging in yeast by heterogeneous, age-correlated expression of a stress protectant. PLoS Biol 10:e1001325. doi:10.1371/journal.pbio.1001325

    PubMed  CAS  Google Scholar 

  66. Moreira B, Tormo J, Estrelles E, Pausas JG (2010) Disentangling the role of heat and smoke as germination cues in Mediterranean Basin flora. Ann Bot 105:627–635. doi:10.1093/aob/mcq017

    PubMed  CAS  Google Scholar 

  67. Mahdjoub T, Menu F (2008) Prolonged diapause: a trait increasing invasion speed? J Theor Biol 251:317–330. doi:10.1016/j.jtbi.2007.12.002

    PubMed  Google Scholar 

  68. Bento G, Ogawa A, Sommer RJ (2010) Co-option of the hormone-signalling module dafachronic acid-DAF-12 in nematode evolution. Nature 466:494–497. doi:10.1038/nature 09164

    PubMed  CAS  Google Scholar 

  69. Riddle DL, Albert PS (1997) Genetic and environmental regulation of dauer larva development. In: Riddle DL, Blumenthal T, Meyer B, Priess J (eds) Chapter 26 in C. elegans II, 2nd ed. Cold Spring Harbor Monograph Series, Vol. 33

    Google Scholar 

  70. Viney ME, Gardner MP, Jackson JA (2003) Variation in Caenorhabditis elegans dauer larva formation. Dev Growth Differ 45:389–396 doi:10.1046/j.1440–169X.2003.00703.x

    PubMed  Google Scholar 

  71. Kiontke K, Fitch DHA (2010) Phenotypic plasticity: different teeth for different feasts. Curr Biol. 20:R710–712. doi:10.1016/j.cub.2010.07.009

    PubMed  CAS  Google Scholar 

  72. Moran NA (1992) The evolutionary maintenance of alternative phenotypes. Am Nat 139:971–989

    Google Scholar 

  73. Johnston RJ Jr, Desplan C (2010) Stochastic mechanisms of cell fate specification that yield random or robust outcomes. Ann Rev Cell Dev Biol 26:689–719. doi:10.1146/annurev-cellbio-100109–104113

    CAS  Google Scholar 

  74. Roorda A, Williams DR (1999) The arrangement of the three cone classes in the living human eye. Nature 397:520–522. doi:10.1038/17383

    PubMed  CAS  Google Scholar 

  75. Serizawa S, Miyamichi K, Nakatani H, Suzuki M, Saito M, et al. (2003) Negative feedback regulation ensures the one receptor-one olfactory neuron rule in mouse. Science 302:2088–2094. doi:10.1126/science.1089122

    PubMed  CAS  Google Scholar 

  76. Miller AG, Whitlock JP Jr (1982) Heterogeneity in the rate of benzo[a]pyrene metabolism in single cells: quantitation using flow cytometry. Mol Cell Biol 2:625–632

    PubMed  CAS  Google Scholar 

  77. Telenti A (2005) Adaptation, co-evolution, and human susceptibility to HIV-1 infection. Infect. Genet Evol 5:327–334. doi:10.1016/j.meegid.2004.11.001

    PubMed  CAS  Google Scholar 

  78. Huang S, Ingber DE (2006) A non-genetic basis for cancer progression and metastasis: self-organizing attractors in cell regulatory networks. Breast Dis 26:27–54

    PubMed  CAS  Google Scholar 

  79. Kaneko K (2011) Characterization of stem cells and cancer cells on the basis of gene expression profile stability, plasticity, and robustness: dynamical systems theory of gene expressions under cell-cell interaction explains mutational robustness of differentiated cells and suggests how cancer cells emerge. Bioessays 33:403–413. doi:10.1002/bies.201000153

    PubMed  CAS  Google Scholar 

  80. Miller RW (1970) Neoplasia and Downs syndrome. Ann New York Acad Sci 171:637–644

    Google Scholar 

  81. Denys P, Malvaux P, Van Den Berghe H, Tanghe W, Proesmans W (1967) Association of an anatomo-pathological syndrome of male pseudohermaphroditism, Wilms’ tumor, parenchymatous nephropathy and XX/XY mosaicism. Arch Fr Pediatr 24:729–739

    PubMed  CAS  Google Scholar 

  82. Drash A, Sherman F, Hartmann WH, Blizzard RM (1970) A syndrome of pseudohermaphroditism, Wilms’ tumor, hypertension, and degenerative renal disease. J Pediatr 76:585–593

    PubMed  CAS  Google Scholar 

  83. Narod SA, Hawkins MM, Robertson CM Stiller CA (1997) Congenital anomalies and childhood cancer in Great Britain. Am J Hum Genet 60:474–485

    PubMed  CAS  Google Scholar 

  84. Schumacher R, Mai A, Gutjahr P (1992) Association of rib anomalies and malignancy in childhood. Eur J Pediatr 151:432–434

    PubMed  CAS  Google Scholar 

  85. Elliott M, Maher ER (1994) Beckwith-Wiedemann syndrome. J Med Genet 31:560–564

    PubMed  CAS  Google Scholar 

  86. Feinberg AP, Tycko B (2004) The history of cancer epigenetics. Nat Rev Cancer 4:143–53. doi:10.1038/nrc1279

    PubMed  CAS  Google Scholar 

  87. DeBaun MR, Niemitz EL, Feinberg AP (2003) Association of in vitro fertilization with Beckwith-Wiedemann syndrome and epigenetic alterations of LIT1 and H19. Am J Hum Genet 72:156–160. doi:10.1086/346031

    PubMed  CAS  Google Scholar 

  88. Maher ER, Brueton LA, Bowdin SC, Luharia A, Cooper W, et al. (2003) Beckwith-Wiedemann syndrome and assisted reproduction technology (ART). J Med Genet 40:62–64. doi:10.1136/jmg.40.1.62

    PubMed  CAS  Google Scholar 

  89. Méhes K, Signer E, Plüss HJ, Müller HJ, Stalder G (1985) Increased prevalence of minor anomalies in childhood malignancy. Eur J Pediatr 144:243–254

    PubMed  Google Scholar 

  90. Méhes K, Szüle E, Törzsök F, Meggyessy V (1987) Supernumerary nipples and urologic malignancies. Cancer Genet Cytogenet 24:185–188. doi:10.1016/0165-4608(87)90097-5

    PubMed  Google Scholar 

  91. Goedert JJ, McKeen EA, Fraumeni JF (1981) Polymastia and renal adenocarcinoma. Ann Intern Med 95:182–184

    PubMed  CAS  Google Scholar 

  92. Levy SF, Siegal ML (2008) Network hubs buffer environmental variation in Saccharomyces cerevisiae. PLoS Biol 6:e264. doi:10.1371/journal.pbio.0060264

    PubMed  Google Scholar 

  93. Rambaldi D, Giorgi FM, Capuani F, Ciliberto A, Ciccarelli FD (2008) Low duplicability and network fragility of cancer genes. Trends Genet 24:427–30. doi:10.1016/j.tig.2008.06.003

    PubMed  CAS  Google Scholar 

  94. Heppner GH (1984) Tumor heterogeneity. Cancer Res 44:2259–2265

    PubMed  CAS  Google Scholar 

  95. Dexter DL, Leith JT (1986) Tumor heterogeneity and drug resistance. J Clin Oncol 4:244–257

    PubMed  CAS  Google Scholar 

  96. Gupta PB, Fillmore CM, Jiang G, Shapira SD, Tao K, et al. (2011) Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell 146:633–644. doi:10.1016/j.cell.2011.07.026

    PubMed  CAS  Google Scholar 

  97. Roesch A, Fukunaga-Kalabis M, Schmidt EC, Zabierowski SE, Brafford PA, et al. (2010) A temporarily distinct subpopulation of slow-cycling melanoma cells is required for continuous tumor growth. Cell 141:583–594. doi:10.1016/j.cell.2010.04.020

    PubMed  CAS  Google Scholar 

  98. Slack MD, Martinez ED, Wu LF, Altschuler SJ (2008) Characterizing heterogeneous cellular responses to perturbations. Proc Natl Acad Sci USA 105:19306–19311. doi:10.1073/pnas.0807038105

    PubMed  CAS  Google Scholar 

  99. Cohen AA, Geva-Zatorsky N, Eden E, Frenkel-Morgenstern M, Issaeva I, et al. (2008) Dynamic proteomics of individual cancer cells in response to a drug. Science 322:1511–1516. doi:10.1126/science.1160165

    PubMed  CAS  Google Scholar 

  100. Al-Hajj M, Wicha MS, Benito-Hernandez A, Morrison SJ, Clarke MF (2003) Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci USA 100:3983–3988. doi:10.1073/pnas.0530291100

    PubMed  CAS  Google Scholar 

  101. Li C, Heidt DG, Dalerba P, Burant CF, Zhang L, et al (2007) Identification of pancreatic cancer stem cells. Cancer Res 67:1030–1037. doi:10.1158/0008–5472.CAN-06–2030

    PubMed  CAS  Google Scholar 

  102. Li C, Lee CJ, Simeone DM (2009) Identification of human pancreatic cancer stem cells. Meth Mol Biol 568:161–173. doi:10.1007/978–1–59745–280–9_10

    CAS  Google Scholar 

  103. Lapidot T, Sirard C, Vormoor J, Murdoch B, Hoang T, et al. (1994) A cell initiating human acute myeloid leukaemia after transplantation into SCID mice. Nature 367:645–648. doi:10.1038/367645a0

    PubMed  CAS  Google Scholar 

  104. Singh SK, Hawkins C, Clarke ID, Squire JA, Bayani J, et al. (2004) Identification of human brain tumour initiating cells. Nature 432:396–401. doi:10.1038/nature03128

    PubMed  CAS  Google Scholar 

  105. Ohya Y, Sese J, Yukawa M, Sano F, Nakatani Y, et al (2005) High-dimensional and large-scale phenotyping of yeast mutants. Proc Natl Acad Sci USA 102:19015–19020. doi:10.1073/pnas.0509436102

    PubMed  CAS  Google Scholar 

  106. Bakal C, Aach J, Church G, Perrimon N (2007) Quantitative morphological signatures define local signaling networks regulating cell morphology. Science 316:1753–6. doi:10.1126/science.1140324

    PubMed  CAS  Google Scholar 

  107. Shaner NC, Steinbach PA, Tsien RY (2005) A guide to choosing fluorescent proteins. Nat Method 2:905–909. doi:10.1038/nmeth819

    CAS  Google Scholar 

  108. Charvin G, Cross FR, Siggia ED (2008) A microfluidic device for temporally controlled gene expression and long-term fluorescent imaging in unperturbed dividing yeast cells. PLoS One 3:e1468. doi:10.1371/journal.pone.0001468

    PubMed  Google Scholar 

  109. Agresti JJ, Antipov E, Abate AR, Ahn K, Rowat AC, et al. (2010) Ultrahigh-throughput screening in drop-based microfluidics for directed evolution. Proc Natl Acad Sci USA 107:4004–4009. doi:10.1073/pnas.0910781107

    PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Genetics, Stanford University Medical School, Stanford, California, USA

    Sasha F. Levy

  2. Center for Genomics and Systems Biology, Department of Biology, New York University, New York, USA

    Mark L. Siegal

Authors
  1. Sasha F. Levy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark L. Siegal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mark L. Siegal .

Editor information

Editors and Affiliations

  1. University of Exeter, Prince of Wales Road, Exeter, EX4 4SB, Devon, United Kingdom

    Orkun S. Soyer

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Levy, S.F., Siegal, M.L. (2012). The Robustness Continuum. In: Soyer, O. (eds) Evolutionary Systems Biology. Advances in Experimental Medicine and Biology, vol 751. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3567-9_20

Download citation

Publish with us

Policies and ethics

Search

Navigation