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Osteoporosis and Osteoarthritis pp 141–161Cite as

Quantitative Molecular Models for Biological Processes: Modeling of Signal Transduction Networks with ANIMO

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Part of the Methods in Molecular Biology book series (MIMB,volume 2221)

Abstract

Computational modeling of biological networks is increasing in popularity due to the increased demand for understanding biological processes. This understanding requires integration of a variety of experimental data that allows understanding of complex mechanisms regulating cell and tissue function. However, the mathematical complexity of many modeling tools have thusfar prevented broad adaptation and effective use by molecular biologists. In this chapter, we show by example how one can start building a model in ANIMO and how to adapt the model to experimental data. We show how this model can be used for simulating network activities, testing hypotheses, and how to improve the model using wet-lab data.

Key words

  • Modeling tool
  • Computational model
  • Signal transduction network
  • Signaling pathways
  • Signaling cross talk
  • WNT
  • NFkB
  • TGF beta

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References

  1. Liang Y, Kelemen A (2017) Dynamic modeling and network approaches for omics time course data: overview of computational approaches and applications. Brief Bioinform 19(5):1051–1068. https://doi.org/10.1093/bib/bbx036

    CrossRef  CAS  Google Scholar 

  2. Du Q, Zhang X, Liu Q, Zhang X, Bartels CE, Geller DA (2013) Nitric oxide production upregulates Wnt/β-catenin signaling by inhibiting Dickkopf-1. Cancer Res 73(21):6526–6537. https://doi.org/10.1158/0008-5472.CAN-13-1620

    CrossRef  CAS  PubMed  Google Scholar 

  3. Zhong L, Schivo S, Huang X, Leijten J, Karperien M, Post JN (2017) Nitric oxide mediates crosstalk between interleukin 1β and WNT signaling in primary human chondrocytes by reducing DKK1 and FRZB expression. Int J Mol Sci 18(11):2491. https://doi.org/10.3390/ijms18112491

    CrossRef  CAS  PubMed Central  Google Scholar 

  4. Schivo S, Khurana S, Govindaraj K, Scholma J, Kerkhofs J, Zhong L, Huang X, van de Pol J, Langerak R, van Wijnen AJ, Geris L, Karperien M, Post JN (2019) ECHO, the executable CHOndrocyte: a computational model to study articular chondrocytes in health and disease. Cell Signal 68:109471. https://doi.org/10.1016/j.cellsig.2019.109471

    CrossRef  CAS  PubMed  Google Scholar 

  5. Luechtefeld T, Marsh D, Rowlands C, Hartung T (2018) Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility. Toxicol Sci 165(1):198–212. https://doi.org/10.1093/toxsci/kfy152

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  6. Grimm D (2019) U.S. EPA to eliminate all mammal testing by 2035. https://www.sciencemag.org/news/2019/09/us-epa-eliminate-all-mammal-testing-2035

  7. Grimm D (2019) EPA plan to end animal testing splits scientists. Science 365(6459):1231. https://doi.org/10.1126/science.365.6459.1231

    CrossRef  CAS  PubMed  Google Scholar 

  8. Scholma J, Schivo S, Urquidi RA, Pol JVD, Karperien M, Post JN (2014) Biological networks 101 : Computational modeling for molecular biologists. Gene 533(1):379–384. https://doi.org/10.1016/j.gene.2013.10.010

    CrossRef  CAS  PubMed  Google Scholar 

  9. Brodland GW (2015) How computational models can help unlock biological systems. In: Seminars in cell & developmental biology. Elsevier, pp 62–73. https://doi.org/10.1016/j.semcdb.2015.07.001

  10. Morris MK, Saez-Rodriguez J, Sorger PK, Lauffenburger DA (2010) Logic-based models for the analysis of cell signaling networks. Biochemistry 49(15):3216–3224. https://doi.org/10.1021/bi902202q

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  11. Schivo S, Scholma J, van der Vet PE, Karperien M, Post JN, van de Pol J, Langerak R (2016) Modelling with ANIMO: between fuzzy logic and differential equations. BMC Syst Biol 10(1):56. https://doi.org/10.1186/s12918-016-0286-z

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  12. Terfve C, Cokelaer T, Henriques D, MacNamara A, Goncalves E, Morris MK, Mv I, Lauffenburger DA, Saez-Rodriguez J (2012) CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC Syst Biol 6(1):133. https://doi.org/10.1186/1752-0509-6-133

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  13. Chaouiya C, Remy E, Mossé B, Thieffry D (2004) Qualitative analysis of regulatory graphs: a computational tool based on a discrete formal framework. In: Benvenuti L, De Santis A, Farina L (eds) Positive systems. Lecture Notes in Control and Information Science, vol 294. Springer, Berlin. https://doi.org/10.1007/978-3-540-44928-7_17

    CrossRef  Google Scholar 

  14. Bock M, Scharp T, Talnikar C, Klipp E (2013) BooleSim: an interactive Boolean network simulator. Bioinformatics 30(1):131–132. https://doi.org/10.1093/bioinformatics/btt568

    CrossRef  CAS  PubMed  Google Scholar 

  15. Schivo S, Scholma J, Karperien M, Post JN, Van De Pol J, Langerak R (2014) Setting parameters for biological modelsWith ANIMO. In: Electronic Proceedings in Theoretical Computer Science, EPTCS, pp 35–47. doi:https://doi.org/10.4204/EPTCS.145.5

  16. Krumsiek J, Pölsterl S, Wittmann DM, Theis FJ (2010) Odefy - from discrete to continuous models. BMC Bioinformatics 11(1):233. https://doi.org/10.1186/1471-2105-11-233

    CrossRef  PubMed  PubMed Central  Google Scholar 

  17. Mendes P, Hoops S, Sahle S, Gauges R, Dada J, Kummer U (2009) Computational modeling of biochemical networks using COPASI. In: Maly IV (ed) Methods in molecular biology (methods and protocols)-systems biology, vol 500. Vol systems biology. Humana Press, New York, NY. https://doi.org/10.1007/978-1-59745-525-1_2

    CrossRef  Google Scholar 

  18. Matsuoka Y, Funahashi A, Ghosh S, Kitano H (2014) Modeling and simulation using celldesigner. In: Miyamoto-Sato E, Ohashi H, Sasaki H, Nishikawa J-I, Yanagawa H (eds) Methods in molecular biology (methods and protocols)- Transcription factor regulatory networks., vol 1164. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0805-9_11

    CrossRef  Google Scholar 

  19. Schivo S, Scholma J, Wanders B, Camacho RAU, van der Vet PE, Karperien M, Langerak R, van de Pol J, Post JN (2014) Modeling biological pathway dynamics with timed automata. Ieee J Biomed Health 18(3):832–839. https://doi.org/10.1109/Jbhi.2013.2292880

    CrossRef  Google Scholar 

  20. Eisenmann DM (2005) Wnt signaling. https://doi.org/10.1895/wormbook.1.7.1. Accessed 19 Dec 2019

  21. Murphy AM, Wong AL, Bezuhly M (2015) Modulation of angiotensin II signaling in the prevention of fibrosis. Fibrogenesis Tissue Repair 8:7–7. https://doi.org/10.1186/s13069-015-0023-z

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  22. Morlon A, Munnich A, Smahi A (2005) TAB2, TRAF6 and TAK1 are involved in NF-κB activation induced by the TNF-receptor, Edar and its adaptator Edaradd. Hum Mol Genet 14(23):3751–3757. https://doi.org/10.1093/hmg/ddi405

    CrossRef  CAS  PubMed  Google Scholar 

  23. MacDonald BT, Tamai K, He X (2009) Wnt/beta-catenin signaling: components, mechanisms, and diseases. Dev Cell 17(1):9–26. https://doi.org/10.1016/j.devcel.2009.06.016

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  24. Tran FH, Zheng JJ (2017) Modulating the wnt signaling pathway with small molecules. Protein Sci 26(4):650–661. https://doi.org/10.1002/pro.3122

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  25. Verstrepen L, Bekaert T, Chau TL, Tavernier J, Chariot A, Beyaert R (2008) TLR-4, IL-1R and TNF-R signaling to NF-κB: variations on a common theme. Cell Mol Life Sci 65(19):2964–2978. https://doi.org/10.1007/s00018-008-8064-8

    CrossRef  CAS  PubMed  Google Scholar 

  26. Massagué J (1998) TGF-β signal transduction. Annu Rev Biochem 67(1):753–791. https://doi.org/10.1146/annurev.biochem.67.1.753

    CrossRef  PubMed  Google Scholar 

  27. Cadigan KM, Waterman ML (2012) TCF/LEFs and Wnt signaling in the nucleus. Cold Spring Harb Perspect Biol 4(11):a007906. https://doi.org/10.1101/cshperspect.a007906

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wu B, Crampton SP, Hughes CCW (2007) Wnt signaling induces matrix metalloproteinase expression and regulates T cell transmigration. Immunity 26(2):227–239. https://doi.org/10.1016/j.immuni.2006.12.007

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  29. Heather G, Chun P (2006) Activin receptor-like kinases: structure, function and clinical implications. Endocr Metab Immune Disord Drug Targets 6(1):45–58. https://doi.org/10.2174/187153006776056585

    CrossRef  Google Scholar 

  30. Frederick JP, Liberati NT, Waddell DS, Shi Y, Wang X-F (2004) Transforming growth factor β-mediated transcriptional repression of c-myc is dependent on direct binding of Smad3 to a novel repressive Smad binding element. Mol Cell Biol 24(6):2546. https://doi.org/10.1128/MCB.24.6.2546-2559.2004

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  31. Higuera GA, Hendriks JA, van Dalum J, Wu L, Schotel R, Moreira-Teixeira L, van den Doel M, Leijten JC, Riesle J, Karperien M, van Blitterswijk CA, Moroni L (2013) In vivo screening of extracellular matrix components produced under multiple experimental conditions implanted in one animal. Integr Biol 5(6):889–898. https://doi.org/10.1039/c3ib40023a

    CrossRef  CAS  Google Scholar 

  32. Jonk LJC, Itoh S, Heldin C-H, ten Dijke P, Kruijer W (1998) Identification and functional characterization of a Smad binding element (SBE) in the JunB promoter that acts as a transforming growth factor-β, activin, and bone morphogenetic protein-inducible enhancer. J Biol Chem 273(33):21145–21152. https://doi.org/10.1074/jbc.273.33.21145

    CrossRef  CAS  PubMed  Google Scholar 

  33. Ranganathan P, Agrawal A, Bhushan R, Chavalmane AK, Kalathur RKR, Takahashi T, Kondaiah P (2007) Expression profiling of genes regulated by TGF-beta: differential regulation in normal and tumour cells. BMC Genomics 8:98–98. https://doi.org/10.1186/1471-2164-8-98

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  34. Hyun Hwa C, Hye Joon J, Ji Sun S, Yong Chan B, Jin Sup J (2008) Crossregulation of β-catenin/Tcf pathway by NF-κB is mediated by lzts2 in human adipose tissue-derived mesenchymal stem cells. Biochim Biophys Acta 1783(3):419–428. https://doi.org/10.1016/j.bbamcr.2007.08.005

    CrossRef  CAS  Google Scholar 

  35. Du Q, Geller DA (2010) Cross-regulation between Wnt and NF-κB signaling pathways. For Immunopathol Dis Therap 1(3):155–181. https://doi.org/10.1615/ForumImmunDisTher.v1.i3

    CrossRef  PubMed  PubMed Central  Google Scholar 

  36. Wang J, Zhao J, Chu ESH, Mok MTS, Go MYY, Man K, Heuchel R, Lan HY, Chang Z, Sung JJY, Yu J (2013) Inhibitory role of Smad7 in hepatocarcinogenesis in mice and in vitro. J Pathol 230(4):441–452. https://doi.org/10.1002/path.4206

    CrossRef  CAS  PubMed  Google Scholar 

  37. Guo X, Wang X-F (2009) Signaling cross-talk between TGF-beta/BMP and other pathways. Cell Res 19(1):71–88. https://doi.org/10.1038/cr.2008.302

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  38. Huang N, Li W, Wang X, Qi S (2018) MicroRNA-17-5p aggravates lipopolysaccharide-induced injury in nasal epithelial cells by targeting Smad7. BMC Cell Biol 19(1):1–1. https://doi.org/10.1186/s12860-018-0152-5

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  39. Hayashi H, Abdollah S, Qiu Y, Cai J, Xu Y-Y, Grinnell BW, Richardson MA, Topper JN, Gimbrone MA, Wrana JL, Falb D (1997) The MAD-related protein Smad7 associates with the TGFβ receptor and functions as an antagonist of TGFβ signaling. Cell 89(7):1165–1173. https://doi.org/10.1016/S0092-8674(00)80303-7

    CrossRef  CAS  PubMed  Google Scholar 

  40. Huang X, Zhong L, Hendriks J, Post JN, Karperien M (2018) The effects of the WNT-signaling modulators BIO and PKF118-310 on the chondrogenic differentiation of human mesenchymal stem cells. Int J Mol Sci 19(2):561. https://doi.org/10.3390/ijms19020561

    CrossRef  CAS  PubMed Central  Google Scholar 

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Author information

Authors and Affiliations

  1. Developmental BioEngineering, TechMed Centre, University of Twente, Enschede, The Netherlands

    Sakshi Khurana, Janet Huisman & Janine N. Post

  2. Student BioMedical Engineering, University of Twente, Enschede, The Netherlands

    Janet Huisman

  3. Department of Computer Science, Open University, Heerlen, The Netherlands

    Stefano Schivo

Authors
  1. Sakshi Khurana
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  2. Janet Huisman
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  3. Stefano Schivo
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  4. Janine N. Post
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Corresponding author

Correspondence to Janine N. Post .

Editor information

Editors and Affiliations

  1. Stem Cell Therapy and Skeletal Regeneration Lab, Mayo Clinic, Rochester, MN, USA

    Ph.D. Andre J. van Wijnen

  2. Stem Cell Therapy and Skeletal Regeneration Lab, Mayo Clinic, Rochester, MN, USA

    Marina S. Ganshina

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Khurana, S., Huisman, J., Schivo, S., Post, J.N. (2021). Quantitative Molecular Models for Biological Processes: Modeling of Signal Transduction Networks with ANIMO. In: van Wijnen, A.J., Ganshina, M.S. (eds) Osteoporosis and Osteoarthritis. Methods in Molecular Biology, vol 2221. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0989-7_10

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  • DOI: https://doi.org/10.1007/978-1-0716-0989-7_10

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