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Rho GTPases pp 37-46 | Cite as

Modeling Rho GTPase Dynamics Using Boolean Logic

  • Joseph H. R. Hetmanski
  • Jean-Marc Schwartz
  • Patrick T. Caswell
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1821)

Abstract

Rho GTPases such as the canonical Rac1 and RhoA are embedded within complex networks requiring the precise spatiotemporal balance of GEFs, GAPs, upstream regulators, growth factors, and downstream effectors. A modeling approach based on Boolean logical networks is becoming an increasingly relied-upon tool to harness this complexity and elucidate further details regarding Rho GTPase signaling. In this methods chapter we describe how to initially create appropriately sized networks based on literature evidence; formalize these networks with reactions based on Boolean logical operators; implement the network into appropriate simulation software (CellNetAnalyzer); and finally perform simulations and make novel, testable predictions via in silico knockouts. Given this predictive power, the Boolean approach may ultimately help to highlight potential future avenues of experimental research.

Key words

Boolean logic Mathematical modeling Networks RhoA 

References

  1. 1.
    Hetmanski JHR, Zindy E, Schwartz JM, Caswell PT (2016) A MAPK-driven feedback loop suppresses Rac activity to promote RhoA-driven cancer cell invasion. PLoS Comput Biol 12:e1004909CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Kim TH, Monsefi N, Song JH, von Kriegsheim A, Vandamme D, Pertz O, Kholodenko BN, Kolch W, Cho KH (2015) Network-based identification of feedback modules that control RhoA activity and cell migration. J Mol Cell Biol 7:242–252CrossRefPubMedGoogle Scholar
  3. 3.
    Samaga R, Saez-Rodriguez J, Alexopoulos LG, Sorger PK, Klamt S (2009) The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data. PLoS Comput Biol 5:e1000438CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Byrne KM, Monsefi N, Dawson JC, Degasperi A, Bukowski-Will JC, Volinsky N, Dobrynski M, Birtwistle MR, Tsyganov MA, Kiyatkin A, Kida K, Finch AJ, Carragher NO, Kolch W, Nguyen LK, von Kriegsheim A, Kholodenko BN (2016) Bistability in the Rac1, PAK, and RhoA signaling network drives actin cytoskeleton dynamics and cell motility switches. Cell Syst 2:38–48CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Albert I, Thakar J, Li S, Zhang R, Albert R (2008) Boolean network simulations for life scientists. Source Code Biol Med 3:16CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Hetmanski JHR, Schwartz JM, Caswell PT (2016) Rationalizing Rac1 and RhoA GTPase signaling: a mathematical approach. Small GTPases 1248:1–6Google Scholar
  7. 7.
    Hetmanski JHR, Schwartz JM, Caswell PT (2016) Modelling GTPase dynamics to understand RhoA-driven cancer cell invasion. Biochem Soc Trans 44:1695–1700CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Klamt S, Saez-Rodriguez J, Gilles E (2007) Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Syst Biol 1:2CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Oda K, Matsuoka Y, Funahashi A, Kitano H (2005) A comprehensive pathway map of epidermal growth factor receptor signaling. Mol Syst Biol 1:2005.0010CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Kandasamy K, Mohan S, Raju R, Keerthikumar S, Kumar GS, Venugopal AK, Telikicherla D, Navarro JD, Mathivanan S, Pecquet C, Gollapudi SK, Tattikota SG, Mohan S, Padhukasahasram H, Subbannayya Y, Goel R, Jacob HK, Zhong J, Sekhar R, Nanjappa V, Balakrishnan L, Subbaiah R, Ramachandra YL, Rahiman BA, Prasad TS, Lin JX, Houtman JC, Desiderio S, Renauld JC, Constantinescu SN, Ohara O, Hirano T, Kubo M, Singh S, Khatri P, Draghici S, Bader GD, Sander C, Leonard WJ, Pandey A (2010) NetPath: a public resource of curated signal transduction pathways. Genome Biol 11:R3CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Mi H, Thomas P (2009) PANTHER pathway: an ontology-based pathway database coupled with data analysis tools. Methods Mol Biol 563:123–140CrossRefPubMedGoogle Scholar
  12. 12.
    Krumsiek J, Pölsterl S, Wittmann DM, Theis FJ (2010) Odefy—From discrete to continuous models. BMC Bioinformatics 11:233CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Sible JC, Tyson JJ (2007) Mathematical modeling as a tool for investigating cell cycle control networks. Methods 41:238–247CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, von Mering C (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43:D447–D452CrossRefPubMedGoogle Scholar
  15. 15.
    Scita G, Tenca P, Areces LB, Tocchetti A, Frittoli E, Giardina G, Ponzanelli I, Sini P, Innocenti M, Di Fiore PP (2001) An effector region in Eps8 is responsible for the activation of the Rac-specific GEF activity of Sos-1 and for the proper localization of the Rac-based actin-polymerizing machine. J Cell Biol 154:1031–1044CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Singh A, Nascimento JM, Kowar S, Busch H, Boerries M (2012) Boolean approach to signalling pathway modelling in HGF-induced keratinocyte migration. Bioinformatics 28:i495–i501CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Bock M, Scharp T, Talnikar C, Klipp E (2014) BooleSim: an interactive Boolean network simulator. Bioinformatics 30:131–132CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Biology Medicine and Health, Wellcome Trust Centre for Cell-Matrix Research, School of Biological SciencesThe University of ManchesterManchesterUK

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