Creation of Three-Dimensional Liver Tissue Models from Experimental Images for Systems Medicine

  • Stefan HoehmeEmail author
  • Adrian Friebel
  • Seddik Hammad
  • Dirk Drasdo
  • Jan G. Hengstler
Part of the Methods in Molecular Biology book series (MIMB, volume 1506)


In this chapter, we illustrate how three-dimensional liver tissue models can be created from experimental image modalities by utilizing a well-established processing chain of experiments, microscopic imaging, image processing, image analysis and model construction. We describe how key features of liver tissue architecture are quantified and translated into model parameterizations, and show how a systematic iteration of experiments and model simulations often leads to a better understanding of biological phenomena in systems biology and systems medicine.

Key words

Systems biology Systems medicine Liver tissue model Hepatocyte transplantation Spatiotemporal model 2D/3D microscopy Liver architecture Confocal scanning microscopy TiQuant software 



The presented work was supported by DFG (HO4772/1-1), BMBF (Virtual Liver Network, Lebersimulator, LiSyM), and ANR (iFlow).


  1. 1.
    Drasdo D, Hoehme S, Hengstler JG (2014) How predictive quantitative modelling of tissue organisation can inform liver disease pathogenesis. J Hepatol 61:951–956CrossRefPubMedGoogle Scholar
  2. 2.
    Zellmer S, Schmidt-Heck W, Godoy P, Wenig H, Meyer C, Lehmann T et al (2010) Transcription factors ETF, E2F, and SP-1 are involved in cytokine-independent proliferation of murine hepatocytes. Hepatology 52:2127–2136CrossRefPubMedGoogle Scholar
  3. 3.
    Friebel A, Neitsch J, Johann T, Hammad S, Hengstler JG, Drasdo D, Hoehme S (2015) TiQuant: software for tissue analysis, quantification and surface reconstruction. Bioinformatics 31(19):3234–3236CrossRefPubMedGoogle Scholar
  4. 4.
    Morales-Navarrete H, Segovia-Miranda F, Klukowski P, Meyer K, Nonaka H, Marsico G, Chernykh M, Kalaidzidis A, Zerial M, Kalaidzidis Y (2015) A versatile pipeline for the multi-scale digital reconstruction and quantitative analysis of 3D tissue architecture. eLife 4:e11214CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Schleicher J, Guthke R, Dahmen U et al (2014) A theoretical study of lipid accumulation in the liver-implications for nonalcoholic fatty liver disease. Biochim Biophys Acta 1841:62–69CrossRefPubMedGoogle Scholar
  6. 6.
    Huard J, Mueller S, Gilles ED et al (2012) An integrative model links multiple inputs and signaling pathways to the onset of DNA synthesis in hepatocytes. FEBS J 279:3290–3313CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Dooley S, Hamzavi J, Ciuclan L et al (2008) Hepatocyte-specific Smad7 expression attenuates TGF-beta-mediated fibrogenesis and protects against liver damage. Gastroenterology 135:642–659CrossRefPubMedGoogle Scholar
  8. 8.
    Vlaic S, Schmidt-Heck W, Linde J et al (2012) Modeling the transcription factor network of murine hepatocytes using the extended TILAR approach. BMC Syst Biol 29:147CrossRefGoogle Scholar
  9. 9.
    Fasbender F, Widera A, Hengstler JG, Watzl C (2016) Natural killer cells and liver fibrosis. Front Immunol 7Google Scholar
  10. 10.
    Hammad S, Hoehme S, Friebel A, von Recklinghausen I, Othman A, Begher-Tibbe B et al (2014) Protocols for staining of bile canalicular and sinusoidal networks of human, mouse and pig livers, three-dimensional reconstruction and quantification of tissue microarchitecture by image processing and analysis. Arch Toxicol 88(5):1161–1183CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Godoy P, Lakkapamu S, Schug M, Bauer A, Stewart JD, Bedawi E et al (2010) Dexamethasone-dependent versus -independent markers of epithelial to mesenchymal transition in primary hepatocytes. Biol Chem 391(1):73–83CrossRefPubMedGoogle Scholar
  12. 12.
    Hoehme S, Brulport M, Bauer A, Bedawy E, Schormann W, Hermes M et al (2010) Prediction and validation of cell alignment along microvessels as order principle to restore tissue architecture in liver regeneration. Proc Natl Acad Sci U S A 107(23):10371–10376CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Rogler CE, Zhou HC, LeVoci L, Rogler LE (2007) Clonal, cultured, murine fetal liver hepatoblasts maintain liver specification in chimeric mice. Hepatology 46(6):1971–1978CrossRefPubMedGoogle Scholar
  14. 14.
    Tarantola E, Bertone V, Milanesi G, Capelli E, Ferrigno A, Neri D et al (2012) Dipeptidylpeptidase-IV, a key enzyme for the degradation of incretins and neuropeptides: activity and expression in the liver of lean and obese rats. Eur J Histochem 56(4):e41CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Braeuning A, Singh Y, Rignall B, Buchmann A, Hammad S, Othman A et al (2010) Phenotype and growth behavior of residual β-catenin-positive hepatocytes in livers of β-catenin-deficient mice. Histochem Cell Biol 134(5):469–481CrossRefPubMedGoogle Scholar
  16. 16.
    Schreiber S, Rignall B, Braeuning A, Marx-Stoelting P, Ott T, Buchmann A et al (2011) Phenotype of single hepatocytes expressing an activated version of β-catenin in liver of transgenic mice. J Mol Histol 42(5):393–400CrossRefPubMedGoogle Scholar
  17. 17.
    Nussler AK, Wildemann B, Freude T, Litzka C, Soldo P, Friess H et al (2014) Chronic CCl4 intoxication causes liver and bone damage similar to the human pathology of hepatic osteodystrophy: a mouse model to analyse the liver-bone axis. Arch Toxicol 88(4):997–1006CrossRefPubMedGoogle Scholar
  18. 18.
    Yoo TS, Ackerman MJ, Lorensen WE, Schroeder W, Chalana V, Aylward S et al (2002) Engineering and algorithm design for an image processing API: a technical report on ITK—the insight toolkit. In: Westwood J (ed) Proc. of medicine meets virtual reality. Ios Press, Amsterdam, pp 586–592Google Scholar
  19. 19.
    Schroeder W, Martin K, Lorensen B (2006) Visualization toolkit: an object-oriented approach to 3D graphics, 4th edn. Kitware, Inc., New JersyGoogle Scholar
  20. 20.
    Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675CrossRefPubMedGoogle Scholar
  21. 21.
    Pizer SM, Amburn EP, Austin JD et al (1987) Adaptive histogram equalization and its variations. Comput Vision Graphics Image Process 39:355–368CrossRefGoogle Scholar
  22. 22.
    Mosaliganti K, Gelas A, Megason S (2009) An adaptive thresholding image filter. Insight J 12(1):1–6Google Scholar
  23. 23.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cyber 9(1):62–66CrossRefGoogle Scholar
  24. 24.
    Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, New JerseyGoogle Scholar
  25. 25.
    Lee TC, Kashyap RL, Chu CN (1994) Building skeleton models via 3-D medial surface axis thinning algorithms. CVGIP Graphical Models Image Process 56(6):462–478CrossRefGoogle Scholar
  26. 26.
    Beare R, Lehmann G (2006) The watershed transform in ITK—discussion and new developments. Insight J 1:1–24Google Scholar
  27. 27.
    Jagiella N, Müller B, Müller M, Vignon-Clementel IE, Drasdo D (2016) Inferring growth control mechanisms in growing multi-cellular spheroids of NSCLC cells from spatial-temporal image data. PLoS Comput Biol 12(2):e1004412CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Remien CH, Adler FR, Waddoups L, Box TD, Sussman NL (2012) Mathematical modeling of liver injury and dysfunction after acetaminophen overdose: early discrimination between survival and death. Hepatology 56(2):727–734CrossRefPubMedGoogle Scholar
  29. 29.
    Eissing T, Kuepfer L, Becker C, Block M, Coboeken K, Gaub T et al (2011) A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks. Front Physiol 2:4CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Ghallab A, Drasdo D, Gebhardt R, Hengstler JG (2015) Model guided identification and therapeutic implications of an ammonia sink mechanism. J Hepat. doi: 10.1016/j.jhep.2015.11.018 Google Scholar
  31. 31.
    Schliess F, Drasdo D, Zellmer S (2014) Integrated metabolic spatial-temporal model for the prediction of ammonia detoxification during liver damage and regeneration. Hepatology 60(6):2040–2051CrossRefPubMedGoogle Scholar
  32. 32.
    Schwen LO, Krauss M, Niederalt C, Gremse F, Kiessling F, Schenk A et al (2014) Spatio-temporal simulation of first pass drug perfusion in the liver. PLoS Comput Biol 10:e1003499CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Ricken T, Dahmen U, Dirsch O (2010) A biphasic model for sinusoidal liver perfusion remodeling after outflow obstruction. Biomech Model Mechanobiol 9(4):435–450CrossRefPubMedGoogle Scholar
  34. 34.
    Ochoa JGD, Bucher J, Péry ARR, Zaldivar Comenges JM, Niklas J, Mauch K (2013) A multi-scale modeling framework for individualized, spatiotemporal prediction of drug effects and toxicological risk. Front Pharmacol 3:204Google Scholar
  35. 35.
    Godoy P, Hewitt NJ, Albrecht U, Anderson ME, Ansari N, Bhattacharya S et al (2013) Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME. Arch Toxicol 87:1315–1530CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Haeussinger D (1983) Hepatocyte heterogeneity in glutamine and ammonia metabolism and the role of an intercellular glutamine cycle during ureogenesis in perfused rat-liver. Eur J Biochem 133:269–275CrossRefGoogle Scholar
  37. 37.
    Gebhardt R, Mecke D (1983) Heterogeneous distribution of glutamine-synthetase among rat-liver parenchymal-cells in situ and in primary culture. EMBO J 2:567–570PubMedPubMedCentralGoogle Scholar
  38. 38.
    Celliere et al (2016) (In preparation)Google Scholar
  39. 39.
    Ramis-Conde I, Drasdo D, Anderson ARA, Chaplain MAJ (2008) Modeling the influence of the E-cadherin-β-catenin pathway in cancer cell invasion: a multiscale approach. Biophys J 95(1):155–165CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    D’Alessandro L, Hoehme S, Drasdo D, Klingmüller U (2015) Unraveling liver complexity from molecular to organ level: challenges and perspectives. Prog Biophys Mol Biol 117(1):78–86CrossRefPubMedGoogle Scholar
  41. 41.
    van Liedekerke P, Palm M, Jagiella N, Drasdo D (2015) Simulating tissue mechanics with Agent Based Models: concepts and perspectives. Comput Particle Mech 2:401–444CrossRefGoogle Scholar
  42. 42.
    Scianna M, Preziosi L (2013) Cellular Potts models. Multiscale extensions and biological applications. Chapman & Hall/CRC, LondonGoogle Scholar
  43. 43.
    Deutsch A, Dormann S (2005) Cellular automaton modeling of biological pattern formation: characterization, applications, and analysis. Birkhaeuser Verlag AG, BostonGoogle Scholar
  44. 44.
    Alber MS, Kiskowski MA, Glazier JA, Jiang Y (2003) On cellular automaton approaches to modeling biological cells. Math Syst Theory Biol Commun Comput Finance 134:1–39CrossRefGoogle Scholar
  45. 45.
    D’Antonio G, Macklin P, Preziosi L (2013) An agent-based model for elasto-plastic mechanical interactions between cells, basement membrane and extracellular matrix. Math Biosci Eng 10:75–101CrossRefPubMedGoogle Scholar
  46. 46.
    Drasdo D, Kree R, McCaskill JS (1995) Monte Carlo approach to tissue-cell populations. Phys Rev E 52:6635–6657CrossRefGoogle Scholar
  47. 47.
    Galle J, Loeffler M, Drasdo D (2005) Modeling the effect of deregulated proliferation and apoptosis on the growth dynamics of epithelial cell populations in vitro. Biophys J 88(1):62–75CrossRefPubMedGoogle Scholar
  48. 48.
    Drasdo D, Hoehme S, Block M (2007) On the role of physics in the growth and pattern formation of multi-cellular systems: what can we learn from individual-cell based models? J Stat Phys 128(1–2):287–345CrossRefGoogle Scholar
  49. 49.
    Montel F, Delarue M, Elgeti J, Vignjevic D, Cappello G, Prost J (2012) Isotropic stress reduces cell proliferation in tumor spheroids. New J Phys 14:055008CrossRefGoogle Scholar
  50. 50.
    Schluter DK, Ramis-Conde I, Chaplain MAJ (2012) Computational modeling of single-cell migration: the leading role of extracellular matrix fibers. Biophys J 103:1141–1151CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Radszuweit M, Block M, Hengstler J, Schöll E, Drasdo D (2009) Comparing the growth kinetics of cell populations in two and three dimensions. Phys Rev E 79:051907CrossRefGoogle Scholar
  52. 52.
    Kansal AR, Torquato S, Harsh GR IV, Chiocca EA, Deisboeck TS (2000) Simulated brain tumor growth dynamics using a three-dimensional cellular automaton. J Theor Biol 203(4):367–382CrossRefPubMedGoogle Scholar
  53. 53.
    Alarcón T, Byrne HM, Maini PK (2003) A cellular automaton model for tumour growth in inhomogeneous environment. J Theor Biol 225(2):257–274CrossRefPubMedGoogle Scholar
  54. 54.
    Graner F, Glazier JA (1992) Simulation of biological cell sorting using a two-dimensional extended Potts model. Phys Rev Lett 69:2013–2016CrossRefPubMedGoogle Scholar
  55. 55.
    Hatzikirou H, Deutsch A (2010) Lattice-gas cellular automaton modeling of emergent behavior in interacting cell populations. Underst Complex Syst 2010:301–331CrossRefGoogle Scholar
  56. 56.
    Hoehme S, Hengstler JG, Brulport M, Schäfer M, Bauer A, Gebhardt R, Drasdo D (2007) Mathematical modelling of liver regeneration after intoxication with CCl4. Chem-Biol Interact 168:74–93CrossRefGoogle Scholar
  57. 57.
    Missal K, Cross M, Drasdo D (2006) Reverse engineering of gene regulatory networks for incomplete expression data: transcriptional control of haemopoietic commitment. Bioinformatics 22(6):731–738CrossRefPubMedGoogle Scholar
  58. 58.
    Steinwart I, Christmann A (2008) Support vector machines. Springer, New York. ISBN 978-0-387-77242-4Google Scholar
  59. 59.
    Bosch A, Zisserman A, Muñoz X (2007) Image classification using random forests and ferns. ICCV 2007. IEEE 11th International Conference on Computer VisionGoogle Scholar
  60. 60.
    Boykov Y, Kolmogorov V (2004) An experimental comparison of Min-Cut/Max-Flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26(9):1124–1137CrossRefPubMedGoogle Scholar
  61. 61.
    Gabriel E, Fagg GE, Bosilca G, Angskun T, Dongarra JJ, Squyres JM (2004) Open MPI: goals, concept, and design of a next generation MPI implementation. In Proceedings, 11th European PVM/MPI Users’ Group Meeting, Budapest, Hungary, September 2004Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Stefan Hoehme
    • 1
    Email author
  • Adrian Friebel
    • 1
  • Seddik Hammad
    • 2
    • 3
  • Dirk Drasdo
    • 4
  • Jan G. Hengstler
    • 5
  1. 1.Institute for Computer ScienceUniversity of LeipzigLeipzigGermany
  2. 2.Molecular Hepatology, Department of Medicine II, Medical Faculty MannheimUniversity of HeidelbergMannheimGermany
  3. 3.Department of Forensic Medicine and Veterinary Toxicology, Faculty of Veterinary MedicineSouth Valley UniversityQenaEgypt
  4. 4.INRIA, Unit Rocquencourt B.P.105, 78153 Le Chesnay Cedex-France, Laboratoire Jacques-Louis LionsFrance Université of Paris 06, CNRS UMR 7598ParisFrance
  5. 5.Leibniz Research Centre for Working Environment and Human Factors (IfADo)TU Dortmund UniversityDortmundGermany

Personalised recommendations