Systems Toxicology from Genes to Organs

Part of the Methods in Molecular Biology book series (MIMB, volume 930)


This unique overview of systems toxicology methods and techniques begins with a brief account of systems thinking in biology over the last century. We discuss how systems biology and toxicology continue to leverage advances in computational modeling, informatics, large-scale computing, and biotechnology. Next, we chart the genesis of systems toxicology from previous work in physiologically based models, models of early development, and more recently, molecular systems biology. For readers interested in further details this background provides useful linkages to the relevant literature. It also lays the foundations for new ideas in systems toxicology that could translate laboratory measurements of molecular responses from xenobiotic perturbations to adverse organ level effects in humans. By providing innovative solutions across disciplinary boundaries and highlighting key scientific gaps, we believe this chapter provides useful information about the current state, and valuable insight about future directions in systems toxicity.

Key words

Systems toxicology Cellular systems biology Biological network inference Agent-based modeling Virtual tissues Dose–response modeling In vitro to in vivo extrapolation 



The United States Environmental Protection Agency through its Office of Research and Development funded and managed the research described in this paper has been subjected to Agency review and approved for publication. Reference to commercial products or services does not constitute endorsement.


  1. 1.
    Dix DJ, Houck KA, Martin MT et al (2007) The toxcast program for prioritizing toxicity testing of environmental chemicals. Toxicol Sci 95:5–12PubMedCrossRefGoogle Scholar
  2. 2.
    Bertalanffy L (1957) Life, language, law: essays in honor of Arthur F Bentley. Antioch, Yellow Springs, OHGoogle Scholar
  3. 3.
    Bertalanffy L (1968) General systems theory: foundations, development, applications. George Braziller, New York, NYGoogle Scholar
  4. 4.
    Ideker T, Galitski T, Hood L (2001) A new approach to decoding life: systems biology. Annu Rev Genomics Hum Genet 2:343–372PubMedCrossRefGoogle Scholar
  5. 5.
    Kitano H (2002) Systems biology: a brief overview. Science 295:1662–1664PubMedCrossRefGoogle Scholar
  6. 6.
    Kitano H (2002) Computational systems biology. Nature 420:206–210PubMedCrossRefGoogle Scholar
  7. 7.
    Turing AM (1952) The chemical basis of morphogenesis. Phil Trans Royal Soc Lond 237:37–72CrossRefGoogle Scholar
  8. 8.
    Kauffman SA (1969) Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol 22:437–467PubMedCrossRefGoogle Scholar
  9. 9.
    Hill (1910) Proceedings of the physiological society: Jan 22 1910. J Physiol 40:i–viiGoogle Scholar
  10. 10.
    Michaelis L, Menten M (1913) Die kinetik der invertinwirkung. Biochem Z 49:333–369Google Scholar
  11. 11.
    Venter JC, Adams MD, Myers EW et al (2001) The sequence of the human genome. Science 291:1304–1351PubMedCrossRefGoogle Scholar
  12. 12.
    Costanzo M, Baryshnikova A, Bellay J et al (2010) The genetic landscape of a cell. Science 327:425–431PubMedCrossRefGoogle Scholar
  13. 13.
    Teutsch HF, Schuerfeld D, Groezinger E (1999) Three-dimensional reconstruction of parenchymal units in the liver of the rat. Hepatology 29:494–505PubMedCrossRefGoogle Scholar
  14. 14.
    Crawford AR, Lin X-Z, Crawford JM (1998) The normal adult human liver biopsy: a quantitative reference standard. Hepatology 28:323–331PubMedCrossRefGoogle Scholar
  15. 15.
    Motta P, Porter KR (1974) Structure of rat liver sinusoids and associated tissue spaces as revealed by scanning electron microscopy. Cell Tissue Res 148:111–125PubMedCrossRefGoogle Scholar
  16. 16.
    Taub R (2004) Liver regeneration: from myth to mechanism. Nat Rev Mol Cell Biol 5:836–847PubMedCrossRefGoogle Scholar
  17. 17.
    Fausto N, Campbell JS, Riehle KJ (2006) Liver regeneration. Hepatology 43:S45–S53PubMedCrossRefGoogle Scholar
  18. 18.
    Michalopoulos GK (2010) Liver regeneration after partial hepatectomy: critical analysis of mechanistic dilemmas. Am J Pathol 176:2–13PubMedCrossRefGoogle Scholar
  19. 19.
    Katz NR (1992) Metabolic heterogeneity of hepatocytes across the liver acinus. J Nutr 122:843–849PubMedGoogle Scholar
  20. 20.
    Gumucio JJ (1989) Hepatocyte heterogeneity: the coming of age from the description of a biological curiosity to a partial understanding of its physiological meaning and regulation. Hepatology 9:154–160PubMedCrossRefGoogle Scholar
  21. 21.
    Athelogou M, Schmidt G, Schäpe A et al (2007) Cognition network technology—a novel multimodal image analysis technique for automatic identification and quantification of biological image contents. In: Shorte SL, Frischknecht F (eds) Imaging cellular and molecular biological functions. Springer, Berlin, Heidelberg, pp 407–422CrossRefGoogle Scholar
  22. 22.
    Roysam B, Ancin H, Bhattacharjya AK et al (1994) Algorithms for automated characterization of cell populations in thick specimens from 3-d confocal fluorescence microscopy data. J Microsc 173:115–126PubMedCrossRefGoogle Scholar
  23. 23.
    Turner JN, Ancin H, Becker DE et al (1997) Automated image analysis technologies for biological 3d light microscopy. Int J Imag Sys Technol 8:240–254CrossRefGoogle Scholar
  24. 24.
    Karacali B, Vamvakidou A, Tozeren A (2007) Automated recognition of cell phenotypes in histology images based on membrane- and nuclei-targeting biomarkers. BMC Med Imaging 7:7PubMedCrossRefGoogle Scholar
  25. 25.
    Andersen ME, Clewell HJ, Frederick CB (1995) Applying simulation modeling to problems in toxicology and risk assessment: a short perspective. Toxicol Appl Pharmacol 133:181–187PubMedCrossRefGoogle Scholar
  26. 26.
    Clark LH, Woodrow Setzer R, Barton HA (2004) Framework for evaluation of physiologically-based pharmacokinetic models for use in safety or risk assessment. Risk Anal 24:1697–1717PubMedCrossRefGoogle Scholar
  27. 27.
    Clewell HJ III, Andersen ME, Barton HA (2002) A consistent approach for the application of pharmacokinetic modeling in cancer and noncancer risk assessment. Environ Health Perspect 110(1):85–93PubMedCrossRefGoogle Scholar
  28. 28.
    von Neuman J (1966) Theory of self-reproducing automata. Univeristy Illinois Press, Champaign, ILGoogle Scholar
  29. 29.
    Wiener N, Rosenblueth A (1946) The mathematical formulation of the problem of conduction of impluses in a network of connected excitable eements spcifically in cardiac muscle. Arch Inst Cardiol Mex 16:205–265PubMedGoogle Scholar
  30. 30.
    Wolfram S, Gad-el-Hak M (2003) A new kind of science. Appl Mech Rev 56:B18–B19CrossRefGoogle Scholar
  31. 31.
    Silva HS, Martins ML (2003) A cellular automata model for cell differentiation. Physica A 322:555–566CrossRefGoogle Scholar
  32. 32.
    de Sales JA, Martins ML, Stariolo DA (1997) Cellular automata model for gene networks. Phys Rev E 55:3262CrossRefGoogle Scholar
  33. 33.
    Markus M, Böhm D, Schmick M (1999) Simulation of vessel morphogenesis using cellular automata. Math Biosci 156:191–206PubMedCrossRefGoogle Scholar
  34. 34.
    Savill NJ, Hogeweg P (1997) Modelling morphogenesis: from single cells to crawling slugs. J Theor Biol 184:229–235CrossRefGoogle Scholar
  35. 35.
    Glazier JA, Graner F, ccedil et al (1993) Simulation of the differential adhesion driven rearrangement of biological cells. Phys Rev E 47:2128CrossRefGoogle Scholar
  36. 36.
    Gillespie D (1976) A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J Comput Phys 22:403–434CrossRefGoogle Scholar
  37. 37.
    Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81:2340–2361CrossRefGoogle Scholar
  38. 38.
    Gibson MA, Bruck J (2000) Efficient exact stochastic simulation of chemical systems with many species and many channels. J Phys Chem A 104:1876–1889CrossRefGoogle Scholar
  39. 39.
    Shmulevich I, Dougherty ER, Zhang W (2002) Gene perturbation and intervention in probabilistic boolean networks. Bioinformatics 18:1319–1331PubMedCrossRefGoogle Scholar
  40. 40.
    Shmulevich I, Dougherty ER, Kim S et al (2002) Probabilistic boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18:261–274PubMedCrossRefGoogle Scholar
  41. 41.
    Biggar SR, Crabtree GR (2001) Cell signaling can direct either binary or graded transcriptional responses. 20:3167–3176Google Scholar
  42. 42.
    Saez-Rodriguez J, Alexopoulos LG, Epperlein J et al (2009) Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Mol Syst Biol 5:331PubMedCrossRefGoogle Scholar
  43. 43.
    Klamt S, Saez-Rodriguez J, Lindquist J et al (2006) A methodology for the structural and functional analysis of signaling and regulatory networks. BMC Bioinformatics 7:56PubMedCrossRefGoogle Scholar
  44. 44.
    Jack J, Wambaugh J, Shah I (2011) Simulating quantiative cellular responses using asynchronous threshold boolean network ensembles. BMC Systems Biology Accepted.Google Scholar
  45. 45.
    Hucka M, Finney A, Sauro HM et al (2003) The systems biology markup language (sbml): a medium for representation and exchange of biochemical network models. Bioinformatics 19:524–531PubMedCrossRefGoogle Scholar
  46. 46.
    Hucka M, Bergmann F, Hoops S et al (2010) The systems biology markup language (sbml): language specification for level 3 version 1 core (release 1 candidate). Nature PrecedingsGoogle Scholar
  47. 47.
    Cuellar A, Lloyd C, Nielsen P et al (2003) An overview of cellml 1.1, a biological model description language. Simulation 79:740–747CrossRefGoogle Scholar
  48. 48.
    Lloyd C, Halstead M, Nielsen P (2004) Cellml: its future, present and past. Model Cell Tissue Funct 85:433–450Google Scholar
  49. 49.
    Bergmann FT, Sauro HM (2006) Sbw—a modular framework for systems biology. In: Proceedings of the 38th conference on winter simulation. Winter Simulation Conference, Monterey, California, pp 1637–1645CrossRefGoogle Scholar
  50. 50.
    Hoops S, Sahle S, Gauges R et al (2006) Copasi—a complex pathway simulator. Bioinformatics 22:3067–3074PubMedCrossRefGoogle Scholar
  51. 51.
    Păun A, Pérez-Jiménez M, Romero-Campero F (2006) Modeling signal transduction using p systems. In: Hoogeboom H, Paun G, Rozenberg G, Salomaa A (eds) Membrane computing. Springer, Berlin, Heidelberg, pp 100–122CrossRefGoogle Scholar
  52. 52.
    Manca V (2008) The metabolic algorithm for p systems: principles and applications. Theor Comput Sci 404:142–155CrossRefGoogle Scholar
  53. 53.
    Jack J, Păun A (2009) Discrete modeling of biochemical signaling with memory enhancement. In: Priami C, Back RJ, Petre I (eds) Transactions on computational systems biology xi. Springer, Berlin, Heidelberg, pp 200–215CrossRefGoogle Scholar
  54. 54.
    Jack J, Păun A, Rodríguez-Patón A (2010) A review of the nondeterministic waiting time algorithm. Nat Comput 1–11Google Scholar
  55. 55.
    Priami C, Regev A, Shapiro E et al (2001) Application of a stochastic name-passing calculus to representation and simulation of molecular processes. Inf Process Lett 80:25–31CrossRefGoogle Scholar
  56. 56.
    Curti M, Degano P, Priami C et al (2004) Modelling biochemical pathways through enhanced [pi]-calculus. Theor Comput Sci 325:111–140CrossRefGoogle Scholar
  57. 57.
    Nickerson DP, Hunter PJ (2005) The noble cardiac ventricular electrophysiology models in cellml. Prog Biophys Mol Biol 90:346–359PubMedCrossRefGoogle Scholar
  58. 58.
    Bassingthwaighte J, Hunter P, Noble D (2009) The cardiac physiome: perspectives for the future. Exp Physiol 94:597–605PubMedCrossRefGoogle Scholar
  59. 59.
    Hunt CA, Yan L, Ropella G et al (2007) The multiscale in silico liver. J Crit Care 22:348–349CrossRefGoogle Scholar
  60. 60.
    Höhme S, Hengstler JG, Brulport M et al (2007) Mathematical modelling of liver regeneration after intoxication with ccl4. Chem Biol Interact 168:74–93PubMedCrossRefGoogle Scholar
  61. 61.
    Ohno H, Naito Y, Nakajima H et al (2008) Construction of a biological tissue model based on a single-cell model: a computer simulation of metabolic heterogeneity in the liver lobule. Artif Life 14:3–28PubMedCrossRefGoogle Scholar
  62. 62.
    Sheikh-Bahaei S, Maher JJ, Anthony Hunt C (2010) Computational experiments reveal plausible mechanisms for changing patterns of hepatic zonation of xenobiotic clearance and hepatotoxicity. J Theor Biol 265:718–733PubMedCrossRefGoogle Scholar
  63. 63.
    Wambaugh J, Shah I (2010) Simulating microdosimetry in a virtual hepatic lobule. PLoS Comput Biol 6:e1000756PubMedCrossRefGoogle Scholar
  64. 64.
    Shah I, Wambaugh J (2010) Virtual tissues in toxicology. J Toxicol Environ Health 13:314–328Google Scholar
  65. 65.
    Lerapetritou MG, Georgopoulos PG, Roth CM et al (2009) Tissue-level modeling of xenobiotic metabolism in liver: an emerging tool for enabling clinical translational research. Clin Transl Sci 2:228–237PubMedCrossRefGoogle Scholar
  66. 66.
    Rowland M, Benet LZ, Graham GG (1973) Clearance concepts in pharmacokinetics. J Pharmacokinet Pharmacodyn 1:123–136CrossRefGoogle Scholar
  67. 67.
    Rani HP, Sheu TWH, Chang TM et al (2006) Numerical investigation of non-newtonian microcirculatory blood flow in hepatic lobule. J Biomech 39:551–563PubMedCrossRefGoogle Scholar
  68. 68.
    West GB, Brown JH, Enquist BJ (1997) A general model for the origin of allometric scaling laws in biology. Science 276:122–126PubMedCrossRefGoogle Scholar
  69. 69.
    West GB, Brown JH, Enquist BJ (1999) The fourth dimension of life: fractal geometry and allometric scaling of organisms. Science 284:1677–1679PubMedCrossRefGoogle Scholar
  70. 70.
    Baish JW, Jain RK (2000) Fractals and cancer. Cancer Res 60:3683–3688PubMedGoogle Scholar
  71. 71.
    Di Ieva A, Grizzi F, Gaetani P et al (2008) Euclidean and fractal geometry of microvascular networks in normal and neoplastic pituitary tissue. Neurosurg Rev 31:271–281PubMedCrossRefGoogle Scholar
  72. 72.
    Cross SS (1997) Fractals in pathology. J Pathol 182:1–8PubMedCrossRefGoogle Scholar
  73. 73.
    Pang KS (1983) The effect of intercellular distribution of drug-metabolizing enzymes on the kinetics of stable metabolite formation and elimination by liver: first-pass effects. Drug Metab Rev 14:61–76PubMedCrossRefGoogle Scholar
  74. 74.
    Pang KS, Stillwell RN (1983) An understanding of the role of enzyme localization of the liver on metabolite kinetics: a computer simulation. J Pharmacokinet Pharmacodyn 11:451–468CrossRefGoogle Scholar
  75. 75.
    Andersen ME, Eklund CR, Mills JJ et al (1997) A multicompartment geometric model of the liver in relation to regional induction of cytochrome p450s. Toxicol Appl Pharmacol 144:135–144PubMedCrossRefGoogle Scholar
  76. 76.
    Abu-Zahra TN, Pang KS (2000) Effect of zonal transport and metabolism on hepatic removal: enalapril hydrolysis in zonal, isolated rat hepatocytes in vitro and correlation with perfusion data. Drug Metab Dispos 28:807–813PubMedGoogle Scholar
  77. 77.
    Liu L, Pang KS (2006) An integrated approach to model hepatic drug clearance. Eur J Pharm Sci 29:215–230PubMedCrossRefGoogle Scholar
  78. 78.
    Basciano C, Kleinstreuer C, Kennedy A et al (2010) Computer modeling of controlled microsphere release and targeting in a representative hepatic artery system. Ann Biomed Eng 38:1862–1879PubMedCrossRefGoogle Scholar
  79. 79.
    Li S, Armstrong CM, Bertin N et al (2004) A map of the interactome network of the metazoan C. elegans. Science 303:540–543PubMedCrossRefGoogle Scholar
  80. 80.
    Bruggeman FJ, Westerhoff HV (2007) The nature of systems biology. Trends Microbiol 15:45–50PubMedCrossRefGoogle Scholar
  81. 81.
    Goh K-I, Cusick ME, Valle D et al (2007) The human disease network. Proc Natl Acad Sci 104:8685–8690PubMedCrossRefGoogle Scholar
  82. 82.
    Meek ME, Bucher JR, Cohen SM et al (2003) A framework for human relevance analysis of information on carcinogenic modes of action. Crit Rev Toxicol 33:591–653PubMedCrossRefGoogle Scholar
  83. 83.
    Karp PD (2001) Pathway databases: a case study in computational symbolic theories. Science 293:2040–2044PubMedCrossRefGoogle Scholar
  84. 84.
    Gruber TR (1995) Toward principles for the design of ontologies used for knowledge sharing. Int J Hum Comput Stud 43:907–928CrossRefGoogle Scholar
  85. 85.
    Demir E, Cary MP, Paley S et al (2010) The biopax community standard for pathway data sharing. Nat Biotechnol 28:935–942PubMedCrossRefGoogle Scholar
  86. 86.
    de Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 9:67–103PubMedCrossRefGoogle Scholar
  87. 87.
    Aldridge BB, Saez-Rodriguez J, Muhlich JL et al (2009) Fuzzy logic analysis of kinase pathway crosstalk in tnf/egf/insulin-induced signaling. PLoS Comput Biol 5:e1000340PubMedCrossRefGoogle Scholar
  88. 88.
    Sachs K, Perez O, Pe’er D et al (2005) Causal protein-signaling networks derived from multiparameter single-cell data. Science 308:523–529PubMedCrossRefGoogle Scholar
  89. 89.
    Noy NF, Shah NH, Whetzel PL et al (2009) Bioportal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res 37:W170–W173PubMedCrossRefGoogle Scholar
  90. 90.
    Kim J-D, Ohta T, Tateisi Y et al (2003) Genia corpus—a semantically annotated corpus for bio-textmining. Bioinformatics 19:i180–i182PubMedCrossRefGoogle Scholar
  91. 91.
    Noble D (2006) The music of life: biology beyond the genome, Oxford University PressGoogle Scholar
  92. 92.
    Merks RMH, Glazier JA (2005) A cell-centered approach to developmental biology. Physica A 352:113–130CrossRefGoogle Scholar
  93. 93.
    Poulin P, Theil F-P (2002) Prediction of pharmacokinetics prior to in vivo studies. Ii. Generic physiologically based pharmacokinetic models of drug disposition. J Pharm Sci 91:1358–1370PubMedCrossRefGoogle Scholar
  94. 94.
    Brian Houston J, Carlile DJ (1997) Prediction of hepatic clearance from microsomes, hepatocytes, and liver slices. Drug Metab Rev 29:891–922CrossRefGoogle Scholar
  95. 95.
    Naritomi Y, Terashita S, Kagayama A et al (2003) Utility of hepatocytes in predicting drug metabolism: comparison of hepatic intrinsic clearance in rats and humans in vivo and in vitro. Drug Metab Dispos 31:580–588PubMedCrossRefGoogle Scholar
  96. 96.
    Santostefano MJ, Richardson VM, Walker NJ et al (1999) Dose-dependent localization of tcdd in isolated centrilobular and periportal hepatocytes. Toxicol Sci 52:9–19PubMedCrossRefGoogle Scholar
  97. 97.
    Collins FS, Gray GM, Bucher JR (2008) Transforming environmental health protection. Science 319:906–907PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  1. 1.U.S. Environmental Protection AgencyResearch Triangle ParkUSA

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