Radiation and Environmental Biophysics

, Volume 47, Issue 1, pp 5–23 | Cite as

Systems biology and its potential role in radiobiology

  • Ludwig Feinendegen
  • Philip Hahnfeldt
  • Eric E. Schadt
  • Michael Stumpf
  • Eberhard O. Voit


About a century ago, Conrad Röentgen discovered X-rays, and Henri Becquerel discovered a new phenomenon, which Marie and Pierre Curie later coined as radio-activity. Since their seminal work, we have learned much about the physical properties of radiation and its effects on living matter. Alas, the more we discover, the more we appreciate the complexity of the biological processes that are triggered by radiation exposure and eventually lead (or do not lead) to disease. Equipped with modern biological methods of high-throughput experimentation, imaging, and vastly increased computational prowess, we are now entering an era where we can piece some of the multifold aspects of radiation exposure and its sequelae together, and develop a more systemic understanding of radiogenic effects such as radio-carcinogenesis than has been possible in the past. It is evident from the complexity of even the known processes that such an understanding can only be gained if it is supported by mathematical models. At this point, the construction of comprehensive models is hampered both by technical inadequacies and a paucity of appropriate data. Nonetheless, some initial steps have been taken already and the generally increased interest in systems biology may be expected to speed up future progress. In this context, we discuss in this article examples of relatively small, yet very useful models that elucidate selected aspects of the effects of exposure to ionizing radiation and may shine a light on the path before us.


  1. 1.
  2. 2.
  3. 3.
    Müller HJ (1927) Artificial transmutation of the gene. Science 66:84–87ADSGoogle Scholar
  4. 4.
    Timoféeff-Ressovsky NW, Zimmer KG, Delbrück M (1935) Über die Natur der Genmutation und der Genstruktur. Nachr Ges Wiss Göttingen FG VI Biol NF 1:189–245Google Scholar
  5. 5.
    Friedberg EC (2003) DNA damage and repair. Nature 421:436–440ADSGoogle Scholar
  6. 6.
    Friedberg EC (2002) The intersection between the birth of molecular biology and the DNA repair and mutagenesis field. DNA Repair 1:855–867Google Scholar
  7. 7.
    Zimmer KG (1992) The target theory. In: Cairns J, Stent GS, Watson JD (eds) Phage and the origins of molecular biology, Expanded edn. Cold Spring Harbor Laboratory, NYGoogle Scholar
  8. 8.
    Friedland W, Dingfelder M, Jacob P, Paretzke HG (2005) Calculated DNA double-strand break and fragmentation. Radiat Phys Chem 72:279–286ADSGoogle Scholar
  9. 9.
    Ballarini F, Ottolenghi A (2004) A model of chromosome aberration induction and CML incidence at low doses. Radiat Environ Biophys 43:165–171Google Scholar
  10. 10.
    Moolgavkar SH, Dewanji A, Venzon DJ (1988) A stochastic two-stage model for cancer risk assessment. I. The hazard function and the probability of tumor. Risk Anal 8:383–392Google Scholar
  11. 11.
    Luebeck EG, Moolgavkar SH (2002) Multistage carcinogenesis and the incidence of colorectal cancer. Proc Natl Acad Sci USA 99:15095–15100ADSGoogle Scholar
  12. 12.
    Heidenreich WF (2005) Heterogeneity of cancer risk due to stochastic effects. Risk Anal 25:1589–1594Google Scholar
  13. 13.
    Black WC, Welch HG (1993) Advances in diagnostic imaging and overestimations of disease prevalence and the benefits of therapy. N Engl J Med 328:1237–1243Google Scholar
  14. 14.
    Feuer EJ, Wun LM (1999) DEVCAN: probability of developing or dying of cancer. Version 4.0. National Cancer Institute, BethesdaGoogle Scholar
  15. 15.
    Sachs RK, Chan M, Hlatky L, Hahnfeldt P (2005) Modeling intercellular interactions during carcinogenesis. Radiat Res 164:324–331Google Scholar
  16. 16.
    Moolgavkar SH, Luebeck EG (2003) Multistage carcinogenesis and the incidence of human cancer. Genes Chromosomes Cancer 38:302–306Google Scholar
  17. 17.
    Brugmans MJ, Rispens SM, Bijwaard H, Laurier D, Rogel A, Tomásek L, Tirmarche M (2004) Radon-induced lung cancer in French and Czech miner cohorts described with a two-mutation cancer model. Radiat Environ Biophys 43:153–165Google Scholar
  18. 18.
    Heidenreich WF, Brugmans MJ, Little MP, Leenhouts HP, Paretzke HG, Morin M, Lafuma J (2000) Analysis of lung tumour risk in radon-exposed rats: an intercomparison of multi-step modeling. Radiat Environ Biophys 39:253–265Google Scholar
  19. 19.
    Heidenreich WF, Paretzke HG (2004) Interpretation by modelling of observations in radon radiation carcinogenesis. Radiat Prot Dosimetry 112:501–507Google Scholar
  20. 20.
    Bhowmick NA, Chytil A, Plieth D, Gorska AE, Dumont N, Shappell S, Washington MK, Neilson EG, Moses HL (2004) TGF-beta signaling in fibroblasts modulates the oncogenic potential of adjacent epithelia. Science 303:848–851ADSGoogle Scholar
  21. 21.
    Radisky DC, Bissell MJ (2004) Cancer. Respect thy neighbor! Science 303:775–777Google Scholar
  22. 22.
    Reiss M, Barcellos-Hoff MH (1997) Transforming growth factor-beta in breast cancer: a working hypothesis. Breast Cancer Res Treat 45:81–95Google Scholar
  23. 23.
    Gompertz B (1825) On the nature of the function expressive of the law of human mortality and on the new mode of determining the value of life contingencies. Philos Trans R Soc A 115:513–580Google Scholar
  24. 24.
    Hahnfeldt P, Panigrahy D, Folkman J, Hlatky L (1999) Tumor development under angiogenic signaling: a dynamical theory of tumor growth, treatment response, and postvascular dormancy. Cancer Res 59(19):4770–4775Google Scholar
  25. 25.
    Luckey TD (1980) Hormesis with ionizing radiation. CRC, Boca RatonGoogle Scholar
  26. 26.
    Tubiana M, Aurengo A, Averbeck D, Masse R (2006) Recent reports on the effect of low doses of ionizing radiation and its dose–effect relationship. Radiat Environ Biophys 44:245–251Google Scholar
  27. 27.
    Pollycove M, Feinendegen LE (2001) Biologic response to low doses of ionizing radiation: detriment versus hormesis. Part 2: Dose responses of organisms. J Nucl Med 42:26N–32NGoogle Scholar
  28. 28.
    Feinendegen LE, Pollycove M, Neumann RD (2007) Whole body responses to low-level radiation exposure. New concepts in mammalian radiobiology. Exp Hematol 35:37–46Google Scholar
  29. 29.
    Mothersill C, Seymour CB (2006) Radiation-induced bystander effects and the DNA paradigm: an “out of field” perspective. Mutat Res 59:5–10Google Scholar
  30. 30.
    Kadhim MA, Hill MA, Moore SR (2006) Genomic instability and the role of radiation quality. Radiat Prot Dosimetry 122:221–227Google Scholar
  31. 31.
    Morgan WF, Day JP, Kaplan MI, McGhee REM, Limoli CL (1996) Genomic instability induced by ionizing radiation. Radiat Res 146:247–258Google Scholar
  32. 32.
    ICRU, I. C. o. R. U. a. M. (1983) Microdosimetry. ICRU-report 36, BethesdaGoogle Scholar
  33. 33.
    Hall EJ (2000) Radiobiology for the radiologist. Lippincott/Williams & Wilkins, Philadelphia/BaltimoreGoogle Scholar
  34. 34.
    Fliedner TM, Dörr H, Meineke V (2005) Multi-organ involvement as a pathogenetic principle of the radiation syndromes: a study involving 110 case histories documented in SEARCH and classified as the bases of haematopoietic indicators of effect. Br J Radiol Suppl 27:1–8Google Scholar
  35. 35.
    Arthur C, Guyton MD, Hall JE (2000) Textbook of medical physiology WB Saunders, USAGoogle Scholar
  36. 36.
    Feinendegen LE, Loken MK, Booz J, Muehlensiepen H, Sondhaus CA, Bond VP (1995) Cellular mechanisms of protection and repair induced by radiation exposure and their consequences for cell system responses. Stem Cells 13(Suppl 1):7–20Google Scholar
  37. 37.
    Feinendegen LE, Bond VP, Sondhaus CA, Altman KI (1999) Cellular signal adaptationwith damage control at low doses versus the predominance of DNA damage at high doses. C R Acad Sci III, Sci Vie 322:245–251Google Scholar
  38. 38.
    Feinendegen LE, Bond VP, Sondhaus CA (2000) The dual response to low-dose irradiation: induction vs. prevention of DNA damage. In: Yamada T, Mothersill C, Michael BD, Potten CS (eds) Biological effects of low dose radiation. Excerpta Medica. International Congress Serie 1211, Elsevier, Amsterdam, pp 3–17Google Scholar
  39. 39.
    Feinendegen LE, Pollycove M, Sondhaus CA (2004) Responses to low doses of ionizing radiation in biological systems. Nonlinearity Biol Toxicol Med 2:143–171Google Scholar
  40. 40.
    Rothkamm K, Löbrich M (2003) Evidence for a lack of DNA double-strand break repair in human cells exposed to very low X-ray doses. Proc Natl Acad Sci USA 100:5057–5062ADSGoogle Scholar
  41. 41.
    Bond VP, Varma M, Feinendegen LE, Wu CS, Zaider M (1995) Application of the HSEF to assessing radiation risks in the practice of radiation protection. Health Phys 68:627–631Google Scholar
  42. 42.
    Cleaver J (1968) Defective repair replication of DNA in Xeroderma Pigmentosum. Nature 218:652–656ADSGoogle Scholar
  43. 43.
    Amundson SA, Lee RA, Koch-Paiz CA, Bittner ML, Meltzer P, Trent JM, Fornace AJJ (2003) Differential responses of stress genes to low dose-rate gamma irradiation. Mol Cancer Res 1:445–452Google Scholar
  44. 44.
    Chandra J, Samali A, Orrenius S (2000) Triggering and modulation of apoptosis by oxidative stress. Free Radic Biol Med 29:323–333Google Scholar
  45. 45.
    Finkel T, Holbrook NJ (2000) Oxidants, oxidative stress and the biology of aging. Nature 408:239–247ADSGoogle Scholar
  46. 46.
    Zamboglou N, Porschen W, Muehlensiepen H, Booz J, Feinendegen LE (1981) Low dose effect of ionizing radiation on incorporation of iodo-deoxyuridine into bone marrow cells. Int J Radiat Biol 39:83–93Google Scholar
  47. 47.
    Feinendegen LE, Muehlensiepen H, Lindberg C, Marx J, Porschen W, Booz J (1984) Acute and temporary inhibition of thymidine kinase in mouse bone marrow cells after low-dose exposure. Int J Radiat Biol 45:205–215Google Scholar
  48. 48.
    Olivieri G, Bodycote J, Wolff S (1984) Adaptive response of human lymphocytes to low concentration of radioactive thymidine. Science 223:594–597ADSGoogle Scholar
  49. 49.
    Wolff S, Afzal V, Wienke JK, Olivieri G, Michaeli A (1988) Human lymphocytes exposed to low doses of ionizing radiations become refractory to high doses of radiation as well as to chemical mutagens that induce double-strand breaks in DNA. Int J Radiat Biol 53:39–49Google Scholar
  50. 50.
    Kondo S (1988) Altruistic cell suicide in relation to radiation hormesis. Int J Radiat Biol 53:95–102Google Scholar
  51. 51.
    James SJ, Makinodan T (1990) T-cell potentiation by low dose ionizing radiation: possible mechanisms. Health Phys 59:29–34CrossRefGoogle Scholar
  52. 52.
    Feinendegen LE, Neumann RD (2005) Physics must join with biology in better assessing risk from low-dose irradiation. Radiat Prot Dosimetry 117:346–356Google Scholar
  53. 53.
    Feinendegen LE, Bond VP, Sondhaus CA, Muehlensiepen H (1996) Radiation effects induced by low doses in complex tissue and their relation to cellular adaptive responses. Mutat Res 358:199–205Google Scholar
  54. 54.
    Feinendegen LE (2005) Evidence for beneficial low level radiation effects and radiation hormesis. Br J Radiol Suppl 78:3–7Google Scholar
  55. 55.
    Franco N, Lamartine J, Frouin V, Le Minter P, Petat C, Leplat JJ, Libert F, Gidrol X, Martin MT (2005) Low-dose exposure to gamma rays induces specific gene regulations in normal human keratinocytes. Radiat Res 163:623–635Google Scholar
  56. 56.
    Pollycove M, Feinendegen LE (2003) Radiation-induced versus endogenous DNA damage: possible effect of inducible protective responses in mitigating endogenous damage. Hum Exp Toxicol 22:290–306Google Scholar
  57. 57.
    Sen K, Sies H, Baeurle P (eds) (2000) Redox regulation of gene expression. Academic, San DiegoGoogle Scholar
  58. 58.
    Feinendegen LE, Neumann RD (eds) (2000) Cellular responses to low doses of ionizing radiation. In: Workshop of the US Department of Energy (DOE), Washington, DC, and the National Institutes of Health (NIH), Bethesda, MD, 27–30 April 1999, Mary Woodward Lasker Center, Cloister, NIH, DOE report publicationGoogle Scholar
  59. 59.
    Moolgavkar SH, Knudson AGJ (1981) Mutation and cancer: a model for human carcinogenesis. J Natl Cancer Inst 66:1037–1052Google Scholar
  60. 60.
    Heidenreich WF, Hoogenweem R (2001) Limits of applicability for the deterministic approximation of the two-step clonal expansion model. Risk Anal 21:103–105Google Scholar
  61. 61.
    Scott BR (2004) A biological-based model that links genomic instability, bystander effects, and adaptive response. Mutat Res 568:129–143Google Scholar
  62. 62.
    Schöllnberger H, Stewart RD, Mitchel REJ (2005) Low-LET-induced radioprotective mechanisms within a stochastic two-stage cancer model. Dose Response 3:508–518Google Scholar
  63. 63.
    Leonard BE (2007) Adaptive response and human benefit: part I. A microdosimetry dose-dependent model. Int J Radiat Biol 83:115–131Google Scholar
  64. 64.
    Feinendegen LE (2003) Relative implications of protective responses versus damage induction at low-dose and low-dose rate exposures, using the microdose approach. Radiat Prot Dosimetry 104:337–346Google Scholar
  65. 65.
    Azzam EI, de Toledo SM, Raaphorst GP, Mitchel REJ (1996) Low-dose ionizing radiation decreases the frequency of neoplastic transformation to a level below the spontaneous rate in C3H 10T1/2 cells. Radiat Res 146:369–373Google Scholar
  66. 66.
    Redpath JL, Antoniono RJ (1998) Introduction of an adaptive response against spontaneous neoplastic transformation in vitro by low-dose gamma radiation. Radiat Res 149:517–520Google Scholar
  67. 67.
    Mitchel RJE, Jackson JS, Morrison DP, Carlisle SM (2003) Low doses of radiation increase the latency of spontaneous lymphomas and spinal osteosarcomas in cancer-prone radiation-sensitive Trp53 heterozygous mice. Radiat Res 159:320–327Google Scholar
  68. 68.
    Tapio S, Jacob V (2007) Radioadaptive response revisited. Radiat Environ Biophys 46:1–12Google Scholar
  69. 69.
    Feinendegen LE, Bond VP, Booz J (1994) The quantification of physical events within tissue at low levels of exposure to ionizing radiation. ICRU News 2:9–13Google Scholar
  70. 70.
    Feinendegen LE, Booz J, Bond VP, Sondhaus CA (1985) Microdosimetric approach to the analysis of cell responses at low dose and low dose rate. Radiat Prot Dosimetry 13:299–306Google Scholar
  71. 71.
    Feinendegen LE, Graessle D. (2002) Energy deposition in tissue during chronic irradiation and the biological consequences. In: Chronic irradiation: tolerance and failure in complex biological systems. Br J Radiol Suppl, vol 26, British Institute of Radiology, London, pp 6–14Google Scholar
  72. 72.
    Yamamoto O, Seyama T, Ito A, Fujimoto N (1998) Oral administration of tritiated water (THO) in mouse. III: Low dose-rate irradiation and threshold dose-rate for radiation risk. Int J Radiat Biol 73:535–541Google Scholar
  73. 73.
    Zablotska LB, Ashmore JP, Jowe GR (2004) Analysis of mortality among Canadian nuclear power industry workers after chronic low-dose exposure to ionizing radiation. Radiat Res 161:633–641Google Scholar
  74. 74.
    Edwards AO, Ritter R 3rd, Abel KJ, Manning A, Panhuysen C, Farrer LA (2005) Complement factor H polymorphism and age-related macular degeneration. Science 308:421–424ADSGoogle Scholar
  75. 75.
    Haines JL, Hauser MA, Schmidt S, Scott WK, Olson LM, Gallins P, Spencer KL, Kwan SY, Noureddine M, Gilbert JR, Schnetz-Boutaud N, Agarwal A, Postel EA, Pericak-Vance MA (2005) Complement factor H variant increases the risk of age-related macular degeneration. Science 308:419–421ADSGoogle Scholar
  76. 76.
    Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST, Bracken MB, Ferris FL, Ott J, Barnstable C, Hoh J (2005) Complement factor H polymorphism in age-related macular degeneration. Science 308:385–389ADSGoogle Scholar
  77. 77.
    Grant SF, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, Sainz J, Helgason A, Stefansson H, Emilsson V, Helgadottir A, Styrkarsdottir U, Magnusson KP, Walters GB, Palsdottir E, Jonsdottir T, Gudmundsdottir T, Gylfason A, Saemundsdottir J, Wilensky RL, Reilly MP, Rader DJ, Bagger Y, Christiansen C, Gudnason V, Sigurdsson G, Thorsteinsdottir U, Gulcher JR, Kong A, Stefansson K (2006) Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet 38:320–323Google Scholar
  78. 78.
    Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, Boutin P, Vincent D, Belisle A, Hadjadj S, Balkau B, Heude B, Charpentier G, Hudson TJ, Montpetit A, Pshezhetsky AV, Prentki M, Posner BI, Balding DJ, Meyre D, Polychronakos C, Froguel P (2007) A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445:881–885ADSGoogle Scholar
  79. 79.
    Herbert A, Gerry NP, McQueen MB, Heid IM, Pfeufer A, Illig T, Wichmann HE, Meitinger T, Hunter D, Hu FB, Colditz G, Hinney A, Hebebrand J, Koberwitz K, Zhu X, Cooper R, Ardlie K, Lyon H, Hirschhorn JN, Laird NM, Lenburg ME, Lange C, Christman MF (2006) A common genetic variant is associated with adult and childhood obesity. Science 312:279–283ADSGoogle Scholar
  80. 80.
    Easton DF, Pooley KA, Dunning AM, Pharoah PD, Thompson D, Ballinger DG, Struewing JP, Morrison J, Field H, Luben R, Wareham N, Ahmed S, Healey CS, Bowman R, Luccarini C, Conroy D, Shah M, Munday H, Jordan C, Perkins B, West J, Redman K, Meyer KB, Haiman CA, Kolonel LK, Henderson BE, Le Marchand L, Brennan P, Sangrajrang S, Gaborieau V, Odefrey F, Shen CY, Wu PE, Wang HC, Eccles D, Evans DG, Peto J, Fletcher O, Johnson N, Seal S, Stratton MR, Rahman N, Chenevix-Trench G, Bojesen SE, Nordestgaard BG, Axelsson CK, Garcia-Closas M, Brinton L, Chanock S, Lissowska J, Peplonska B, Nevanlinna H, Fagerholm R, Eerola H, Kang D, Yoo KY, Noh DY, Ahn SH, Hunter DJ, Hankinson SE, Cox DG, Hall P, Wedren S, Liu J, Low YL, Bogdanova N, Schurmann P, Dork T, Tollenaar RA, Jacobi CE, Devilee P, Klijn JG, Sigurdson AJ, Doody MM, Alexander BH, Zhang J, Cox A, Brock IW, Macpherson G, Reed MW, Couch FJ, Goode EL, Olson JE, Meijers-Heijboer H, van den Ouweland A, Uitterlinden A, Rivadeneira F, Milne RL, Ribas G, Gonzalez-Neira A, Benitez J, Hopper JL, McCredie M, Southey M, Giles GG, Schroen C, Justenhoven C, Brauch H, Hamann U, Ko YD, Spurdle AB, Beesley J, Chen X, Aghmesheh M, Amor D, Andrews L, Antill Y, Armes J, Armitage S, Arnold L, Balleine R, Begley G, Beilby J, Bennett I, Bennett B, Berry G, Blackburn A, Brennan M, Brown M, Buckley M, Burke J, Butow P, Byron K, Callen D, Campbell I, Clarke C, Colley A, Cotton D, Cui J, Culling B, Cummings M, Dawson SJ, Dixon J, Dobrovic A, Dudding T, Edkins T, Eisenbruch M, Farshid G, Fawcett S, Field M, Firgaira F, Fleming J, Forbes J, Friedlander M, Gaff C, Gardner M, Gattas M, George P, Giles G, Gill G, Goldblatt J, Greening S, Grist S, Haan E, Harris M, Hart S, Hayward N, Hopper J, Humphrey E, Jenkins M, Jones A, Kefford R, Kirk J, Kollias J, Kovalenko S, Lakhani S, Leary J, Lim J, Lindeman G, Lipton L, Lobb L, Maclurcan M, Mann G, Marsh D, McCredie M, McKay M, McLachlan SA, Meiser B, Milne R, Mitchell G, Newman B, O’Loughlin I, Osborne R, Peters L, Phillips K, Price M, Reeve J, Reeve T, Richards R, Rinehart G, Robinson B, Rudzki B, Salisbury E, Sambrook J, Saunders C, Scott C, Scott E, Scott R, Seshadri R, Shelling A, Southey M, Spurdle A, Suthers G, Taylor D, Tennant C, Thorne H, Townshend S, Tucker K, Tyler J, Venter D, Visvader J, Walpole I, Ward R, Waring P, Warner B, Warren G, Watson E, Williams R, Wilson J, Winship I, Young MA, Bowtell D, Green A, Defazio A, Gertig D, Webb P, Mannermaa A, Kosma VM, Kataja V, Hartikainen J, Day NE, Cox DR, Ponder BA (2007) Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 447:1087–1093ADSGoogle Scholar
  81. 81.
    Peacock ML, Warren JT Jr, Roses AD, Fink JK (1993) Novel polymorphism in the A4 region of the amyloid precursor protein gene in a patient without Alzheimer’s disease. Neurology 43:1254–1256Google Scholar
  82. 82.
    Schadt EE, Lum PY (2006) Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes. J Lipid Res 47(12):2601–2613Google Scholar
  83. 83.
    Brem RB, Yvert G, Clinton R, Kruglyak L (2002) Genetic dissection of transcriptional regulation in budding yeast. Science 296:752–755ADSGoogle Scholar
  84. 84.
    DeCook R, Lall S, Nettleton D, Howell SH (2006) Genetic regulation of gene expression during shoot development in Arabidopsis. Genetics 172:1155–1164Google Scholar
  85. 85.
    Hubner N, Wallace CA, Zimdahl H, Petretto E, Schulz H, Maciver F, Mueller M, Hummel O, Monti J, Zidek V, Musilova A, Kren V, Causton H, Game L, Born G, Schmidt S, Muller A, Cook SA, Kurtz TW, Whittaker J, Pravenec M, Aitman TJ (2005) Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat Genet 37:243–253Google Scholar
  86. 86.
    Jin W, Riley RM, Wolfinger RD, White KP, Passador-Gurgel G, Gibson G (2001) The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nat Genet 29:389–395Google Scholar
  87. 87.
    Klose J, Nock C, Herrmann M, Stuhler K, Marcus K, Bluggel M, Krause E, Schalkwyk LC, Rastan S, Brown SD, Bussow K, Himmelbauer H, Lehrach H (2002) Genetic analysis of the mouse brain proteome. Nat Genet 30:385–393Google Scholar
  88. 88.
    Monks SA, Leonardson A, Zhu H, Cundiff P, Pietrusiak P, Edwards S, Phillips JW, Sachs A, Schadt EE (2004) Genetic inheritance of gene expression in human cell lines. Am J Hum Genet 75:1094–1105Google Scholar
  89. 89.
    Morley M, Molony CM, Weber TM, Devlin JL, Ewens KG, Spielman RS, Cheung VG (2004) Genetic analysis of genome-wide variation in human gene expression. Nature 430:743–747ADSGoogle Scholar
  90. 90.
    Oleksiak MF, Churchill GA, Crawford DL (2002) Variation in gene expression within and among natural populations. Nat Genet 32:261–266Google Scholar
  91. 91.
    Schadt EE, Monks SA, Drake TA, Lusis AJ, Che N, Colinayo V, Ruff TG, Milligan SB, Lamb JR, Cavet G, Linsley PS, Mao M, Stoughton RB, Friend SH (2003) Genetics of gene expression surveyed in maize, mouse and man. Nature 422:297–302ADSGoogle Scholar
  92. 92.
    Stranger BE, Forrest MS, Clark AG, Minichiello MJ, Deutsch S, Lyle R, Hunt S, Kahl B, Antonarakis SE, Tavare S, Deloukas P, Dermitzakis ET (2005) Genome-wide associations of gene expression variation in humans. PLoS Genet 1:e78Google Scholar
  93. 93.
    Kulp DC, Jagalur M. (2006) Causal inference of regulator-target pairs by gene mapping of expression phenotypes. BMC Genomics 7:125Google Scholar
  94. 94.
    Lum PY, Chen Y, Zhu J, Lamb J, Melmed S, Wang S, Drake TA, Lusis AJ, Schadt EE (2006) Elucidating the murine brain transcriptional network in a segregating mouse population to identify core functional modules for obesity and diabetes. J Neurochem March 15 (Epub ahead of print)Google Scholar
  95. 95.
    Mehrabian M, Allayee H, Stockton J, Lum PY, Drake TA, Castellani LW, Suh M, Armour C, Edwards S, Lamb J, Lusis AJ, Schadt EE (2005) Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traits. Nat Genet 37:1224–1233Google Scholar
  96. 96.
    Schadt EE, Lamb J, Yang X, Zhu J, Edwards S, Guhathakurta D, Sieberts SK, Monks S, Reitman M, Zhang C, Lum PY, Leonardson A, Thieringer R, Metzger JM, Yang L, Castle J, Zhu H, Kash SF, Drake TA, Sachs A, Lusis AJ (2005) An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet 37:710–717Google Scholar
  97. 97.
    Karp CL, Grupe A, Schadt E, Ewart SL, Keane-Moore M, Cuomo PJ, Kohl J, Wahl L, Kuperman D, Germer S, Aud D, Peltz G, Wills-Karp M (2000) Identification of complement factor 5 as a susceptibility locus for experimental allergic asthma. Nat Immunol 1:221–226Google Scholar
  98. 98.
    Johnson JM, Castle J, Garrett-Engele P, Kan Z, Loerch PM, Armour CD, Santos R, Schadt EE, Stoughton R, Shoemaker DD (2003) Genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays. Science 302:2141–2144ADSGoogle Scholar
  99. 99.
    Mural RJ, Adams MD, Myers EW, Smith HO, Miklos GL, Wides R, Halpern A, Li PW, Sutton GG, Nadeau J, Salzberg SL, Holt RA, Kodira CD, Lu F, Chen L, Deng Z, Evangelista CC, Gan W, Heiman TJ, Li J, Li Z, Merkulov GV, Milshina NV, Naik AK, Qi R, Shue BC, Wang A, Wang J, Wang X, Yan X, Ye J, Yooseph S, Zhao Q, Zheng L, Zhu SC, Biddick K, Bolanos R, Delcher AL, Dew IM, Fasulo D, Flanigan MJ, Huson DH, Kravitz SA, Miller JR, Mobarry CM, Reinert K, Remington KA, Zhang Q, Zheng XH, Nusskern DR, Lai Z, Lei Y, Zhong W, Yao A, Guan P, Ji RR, Gu Z, Wang ZY, Zhong F, Xiao C, Chiang CC, Yandell M, Wortman JR, Amanatides PG, Hladun SL, Pratts EC, Johnson JE, Dodson KL, Woodford KJ, Evans CA, Gropman B, Rusch DB, Venter E, Wang M, Smith TJ, Houck JT, Tompkins DE, Haynes C, Jacob D, Chin SH, Allen DR, Dahlke CE, Sanders R, Li K, Liu X, Levitsky AA, Majoros WH, Chen Q, Xia AC, Lopez JR, Donnelly MT, Newman MH, Glodek A, Kraft CL, Nodell M, Ali F, An HJ, Baldwin-Pitts D, Beeson KY, Cai S, Carnes M, Carver A, Caulk PM, Center A, Chen YH, Cheng ML, Coyne MD, Crowder M, Danaher S, Davenport LB, Desilets R, Dietz SM, Doup L, Dullaghan P, Ferriera S, Fosler CR, Gire HC, Gluecksmann A, Gocayne JD, Gray J, Hart B, Haynes J, Hoover J, Howland T, Ibegwam C, Jalali M, Johns D, Kline L, Ma DS, MacCawley S, Magoon A, Mann F, May D, McIntosh TC, Mehta S, Moy L, Moy MC, Murphy BJ, Murphy SD, Nelson KA, Nuri Z, Parker KA, Prudhomme AC, Puri VN, Qureshi H, Raley JC, Reardon MS, Regier MA, Rogers YH, Romblad DL, Schutz J, Scott JL, Scott R, Sitter CD, Smallwood M, Sprague AC, Stewart E, Strong RV, Suh E, Sylvester K, Thomas R, Tint NN, Tsonis C, Wang G, Wang G, Williams MS, Williams SM, Windsor SM, Wolfe K, Wu MM, Zaveri J, Chaturvedi K, Gabrielian AE, Ke Z, Sun J, Subramanian G, Venter JC, Pfannkoch CM, Barnstead M, Stephenson LD (2002) A comparison of whole-genome shotgun-derived mouse chromosome 16 and the human genome. Science 296:1661–1671ADSGoogle Scholar
  100. 100.
    Schadt EE, Edwards SW, GuhaThakurta D, Holder D, Ying L, Svetnik V, Leonardson A, Hart KW, Russell A, Li G, Cavet G, Castle J, McDonagh P, Kan Z, Chen R, Kasarskis A, Margarint M, Caceres RM, Johnson JM, Armour CD, Garrett-Engele PW, Tsinoremas NF, Shoemaker DD (2004) A comprehensive transcript index of the human genome generated using microarrays and computational approaches. Genome Biol 5:R73Google Scholar
  101. 101.
    Shoemaker DD, Schadt EE, Armour CD, He YD, Garrett-Engele P, McDonagh PD, Loerch PM, Leonardson A, Lum PY, Cavet G, Wu LF, Altschuler SJ, Edwards S, King J, Tsang JS, Schimmack G, Schelter JM, Koch J, Ziman M, Marton MJ, Li B, Cundiff P, Ward T, Castle J, Krolewski M, Meyer MR, Mao M, Burchard J, Kidd MJ, Dai H, Phillips JW, Linsley PS, Stoughton R, Scherer S, Boguski MS (2001) Experimental annotation of the human genome using microarray technology. Nature 409:922–927ADSGoogle Scholar
  102. 102.
    DePrimo SE, Wong LM, Khatry DB, Nicholas SL, Manning WC, Smolich BD, O’Farrell AM, Cherrington JM (2003) Expression profiling of blood samples from an SU5416 phase III metastatic colorectal cancer clinical trial: a novel strategy for biomarker identification. BMC Cancer 3:3Google Scholar
  103. 103.
    Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E, Houstis N, Daly MJ, Patterson N, Mesirov JP, Golub TR, Tamayo P, Spiegelman B, Lander ES, Hirschhorn JN, Altshuler D, Groop LC (2003) PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34:267–273Google Scholar
  104. 104.
    van ‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–536Google Scholar
  105. 105.
    Waring JF, Jolly RA, Ciurlionis R, Lum PY, Praestgaard JT, Morfitt DC, Buratto B, Roberts C, Schadt E, Ulrich RG (2001) Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles. Toxicol Appl Pharmacol 175:28–42Google Scholar
  106. 106.
    Alberts R, Terpstra P, Bystrykh LV, de Haan G, Jansen RC (2005) A statistical multiprobe model for analyzing cis and trans genes in genetical genomics experiments with short-oligonucleotide arrays. Genetics 171:1437–1439Google Scholar
  107. 107.
    Chesler EJ, Lu L, Shou S, Qu Y, Gu J, Wang J, Hsu HC, Mountz JD, Baldwin NE, Langston MA, Threadgill DW, Manly KF, Williams RW (2005) Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nat Genet 37:233–242Google Scholar
  108. 108.
    Cheung VG, Spielman RS, Ewens KG, Weber TM, Morley M, Burdick JT (2005) Mapping determinants of human gene expression by regional and genome-wide association. Nature 437:1365–1369ADSGoogle Scholar
  109. 109.
    Jansen RC, Nap JP (2001) Genetical genomics: the added value from segregation. Trends Genet 17:388–391Google Scholar
  110. 110.
    Petretto E, Mangion J, Dickens NJ, Cook SA, Kumaran MK, Lu H, Fischer J, Maatz H, Kren V, Pravenec M, Hubner N, Aitman TJ (2006) Heritability and tissue specificity of expression quantitative trait loci. PLoS Genet 2:e172Google Scholar
  111. 111.
    Petretto E, Mangion J, Pravanec M, Hubner N, Aitman TJ (2006) Integrated gene expression profiling and linkage analysis in the rat. Mamm Genome 17:480–489Google Scholar
  112. 112.
    Schadt EE (2005) Exploiting naturally occurring DNA variation and molecular profiling data to dissect disease and drug response traits. Curr Opin Biotechnol 16:647–654Google Scholar
  113. 113.
    GuhaThakurta D, Xie T, Anand M, Edwards SW, Li G, Wang SS, Schadt EE (2006) Cis-regulatory variations: a study of SNPs around genes showing cis-linkage in segregating mouse populations. BMC Genomics 7:235Google Scholar
  114. 114.
    Schadt EE, Sachs A, Friend S (2005) Embracing complexity, inching closer to reality. In: Science STKE 2005, p 40Google Scholar
  115. 115.
    Zhu J, Lum PY, Lamb J, GuhaThakurta D, Edwards SW, Thieringer R, Berger JP, Wu MS, Thompson J, Sachs AB, Schadt EE (2004) An integrative genomics approach to the reconstruction of gene networks in segregating populations. Cytogenet Genome Res 105:363–374Google Scholar
  116. 116.
    Kim JK, Gabel HW, Kamath RS, Tewari M, Pasquinelli A, Rual JF, Kennedy S, Dybbs M, Bertin N, Kaplan JM, Vidal M, Ruvkun G (2005) Functional genomic analysis of RNA interference in C. elegans. Science 308:1164–1167ADSGoogle Scholar
  117. 117.
    Palsson BO (2006) Systems biology. Cambridge University Press, CambridgeGoogle Scholar
  118. 118.
    Ross SM (1985) Introduction to probability models. Academic, OrlandoMATHGoogle Scholar
  119. 119.
    Matsuno H, Tanaka Y, Aoshima H, Doi A, Matsui M, Miyano S (2003) Biopath-ways representation and simulation on hybrid functional Petri nets. In Silico Biol 3:32Google Scholar
  120. 120.
    Sachs K, Perez O, Pe’er D, Lauffenburger DA, Nolan G. (2005) Causal protein-signaling networks derived from multiparameter single-cell data. Science 308:523–529ADSGoogle Scholar
  121. 121.
    Zhu J, Wiener MC, Zhang C, Fridman A, Minch E, Lum PY, Sachs JR, Schadt EE (2007) Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations. PLoS Comput Biol 3:e69MathSciNetADSGoogle Scholar
  122. 122.
    Chiellini C, Bertacca A, Novelli SE, Gorgun CZ, Ciccarone A, Giordano A, Xu H, Soukas A, Costa M, Gandini D, Dimitri R, Bottone P, Cecchetti P, Pardini E, Perego L, Navalesi R, Folli F, Benzi L, Cinti S, Friedman JM, Hotamisligil GS, Maffei M. (2002) Obesity modulates the expression of haptoglobin in the white adipose tissue via TNFalpha. J Cell Physiol 190:251–258Google Scholar
  123. 123.
    Friedman N, Linial M, Nachman I, Pe’er D (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7:601–620Google Scholar
  124. 124.
    Peschel M, Mende W. (1986) Do we live in a Volterra world?. Akademie-Verlag, BerlinGoogle Scholar
  125. 125.
    Savageau MA (1969) Biochemical systems analysis. II. The steady-state solutions for an n-pool system using a power-law approximation. J Theor Biol 25:370–379Google Scholar
  126. 126.
    Savageau MA (1969) Biochemical systems analysis. I. Some mathematical properties of the rate law for the component enzymatic reactions. J Theor Biol 25:365–369Google Scholar
  127. 127.
    Savageau MA, Voit EO (1987) Recasting nonlinear differential equations as S-systems: a canonical nonlinear form. Math Biosci 87:83–115MATHMathSciNetGoogle Scholar
  128. 128.
    Savageau MA (1976) Biochemical systems analysis: a study of function and design in molecular biology. Addison-Wesley Pub. Co. Advanced Book Program, ReadingMATHGoogle Scholar
  129. 129.
    Voit EO (1991) Canonical nonlinear modeling: S-system approach to understanding complexity. Van Nostrand Reinhold, New YorkMATHGoogle Scholar
  130. 130.
    Voit EO (2000) Computational analysis of biochemical systems: a practical guide for biochemists and molecular biologists. Cambridge University Press, New YorkGoogle Scholar
  131. 131.
    Voit EO (2000) Canonical modeling: review of concepts with emphasis on environmental health. Environ Health Perspect 108(Suppl 5):895–909Google Scholar
  132. 132.
    Torres NV, Voit EO (2002) Pathway analysis and optimization in metabolic engineering. Cambridge University Press, New YorkGoogle Scholar
  133. 133.
    Goel G, Chou I-C, Voit EO (2006) Biological systems modeling and analysis: a biomolecular technique of the 21st century. J Biomol Tech 17:252–269Google Scholar
  134. 134.
    Voit EO (2004) The dawn of a new era of metabolic systems analysis. Drug Discov Today BioSilico 2:182–189Google Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Ludwig Feinendegen
    • 1
  • Philip Hahnfeldt
    • 2
  • Eric E. Schadt
    • 3
  • Michael Stumpf
    • 4
  • Eberhard O. Voit
    • 5
  1. 1.Department of Nuclear MedicineUniversity Hospital, Heinrich-Heine-UniversityDüsseldorfGermany
  2. 2.Center for Cancer Systems Biology, Caritas St. Elizabeth’s Medical CenterTufts University School of MedicineBostonUSA
  3. 3.Department of Genetics, Rosetta InpharmaticsLLC, Merck & Co., Inc.SeattleUSA
  4. 4.Centre for BioinformaticsImperial College LondonLondonUK
  5. 5.Department of Biomedical EngineeringGeorgia Institute of TechnologyAtlantaUSA

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