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Computational modeling of cellular signaling processes embedded into dynamic spatial contexts

Abstract

Cellular signaling processes depend on spatiotemporal distributions of molecular components. Multicolor, high-resolution microscopy permits detailed assessment of such distributions, providing input for fine-grained computational models that explore mechanisms governing dynamic assembly of multimolecular complexes and their role in shaping cellular behavior. However, it is challenging to incorporate into such models both complex molecular reaction cascades and the spatial localization of signaling components in dynamic cellular morphologies. Here we introduce an approach to address these challenges by automatically generating computational representations of complex reaction networks based on simple bimolecular interaction rules embedded into detailed, adaptive models of cellular morphology. Using examples of receptor-mediated cellular adhesion and signal-induced localized mitogen-activated protein kinase (MAPK) activation in yeast, we illustrate the capacity of this simulation technique to provide insights into cell biological processes. The modeling algorithms, implemented in a new version of the Simmune toolset, are accessible through intuitive graphical interfaces and programming libraries.

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Figure 1: Automated spatially resolved generation of reaction networks permitted simulation of cellular signaling in realistic morphologies.
Figure 2: From nonspatial to spatial representations of E-cadherin interactions.
Figure 3: Automatic creation of an E-cadherin trimer for a membrane contact reaction network.
Figure 4: E-cadherin accumulation at static or dynamic cell contacts.
Figure 5: Experimental and simulated patterns of Fus3 phosphorylation.

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References

  1. Lingwood, D. & Simons, K. Lipid rafts as a membrane-organizing principle. Science 327, 46–50 (2010).

    Article  CAS  PubMed  Google Scholar 

  2. Kholodenko, B.N. Four-dimensional organization of protein kinase signaling cascades: the roles of diffusion, endocytosis and molecular motors. J. Exp. Biol. 206, 2073–2082 (2003).

    Article  CAS  PubMed  Google Scholar 

  3. Delon, J. & Germain, R.N. Information transfer at the immunological synapse. Curr. Biol. 10, R923–R933 (2000).

    Article  CAS  PubMed  Google Scholar 

  4. Jones, R.B., Gordus, A., Krall, J.A. & MacBeath, G. A quantitative protein interaction network for the ErbB receptors using protein microarrays. Nature 439, 168–174 (2006).

    Article  CAS  PubMed  Google Scholar 

  5. Brown, M.D. & Sacks, D.B. Protein scaffolds in MAP kinase signalling. Cell. Signal. 21, 462–469 (2009).

    Article  CAS  PubMed  Google Scholar 

  6. Hlavacek, W.S., Faeder, J.R., Blinov, M.L., Perelson, A.S. & Goldstein, B. The complexity of complexes in signal transduction. Biotechnol. Bioeng. 84, 783–794 (2003).

    Article  CAS  PubMed  Google Scholar 

  7. Meier-Schellersheim, M. et al. Key role of local regulation in chemosensing revealed by a new molecular interaction-based modeling method. PLoS Comput. Biol. 2, e82 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Hlavacek, W.S. et al. Rules for modeling signal-transduction systems. Sci. STKE 2006, re6 (2006).

    PubMed  Google Scholar 

  9. Lok, L. & Brent, R. Automatic generation of cellular reaction networks with Moleculizer 1.0. Nat. Biotechnol. 23, 131–136 (2005).

    Article  CAS  PubMed  Google Scholar 

  10. Feret, J., Danos, V., Krivine, J., Harmer, R. & Fontana, W. Internal coarse-graining of molecular systems. Proc. Natl. Acad. Sci. USA 106, 6453–6458 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Koschorreck, M. & Gilles, E.D. ALC: automated reduction of rule-based models. BMC Syst. Biol. 2, 91 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Mallavarapu, A., Thomson, M., Ullian, B. & Gunawardena, J. Programming with models: modularity and abstraction provide powerful capabilities for systems biology. J. R. Soc. Interface 6, 257–270 (2009).

    Article  CAS  PubMed  Google Scholar 

  13. Varma, R., Campi, G., Yokosuka, T., Saito, T. & Dustin, M.L. T cell receptor-proximal signals are sustained in peripheral microclusters and terminated in the central supramolecular activation cluster. Immunity 25, 117–127 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. van Zon, J.S. & ten Wolde, P.R. Green's-function reaction dynamics: a particle-based approach for simulating biochemical networks in time and space. J. Chem. Phys. 123, 234910 (2005).

    Article  PubMed  Google Scholar 

  15. Andrews, S.S., Addy, N.J., Brent, R. & Arkin, A.P. Detailed simulations of cell biology with Smoldyn 2.1. PLoS Comput. Biol. 6, e1000705 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Gumbiner, B.M. Regulation of cadherin-mediated adhesion in morphogenesis. Nat. Rev. Mol. Cell Biol. 6, 622–634 (2005).

    Article  CAS  PubMed  Google Scholar 

  17. Sivasankar, S., Zhang, Y., Nelson, W.J. & Chu, S. Characterizing the initial encounter complex in cadherin adhesion. Structure 17, 1075–1081 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Cavey, M. & Lecuit, T. Molecular bases of cell-cell junctions stability and dynamics. Cold Spring Harb. Perspect. Biol. 1, a002998 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Zhang, Y., Sivasankar, S., Nelson, W.J. & Chu, S. Resolving cadherin interactions and binding cooperativity at the single-molecule level. Proc. Natl. Acad. Sci. USA 106, 109–114 (2009).

    Article  CAS  PubMed  Google Scholar 

  20. Troyanovsky, S. Cadherin dimers in cell-cell adhesion. Eur. J. Cell Biol. 84, 225–233 (2005).

    Article  CAS  PubMed  Google Scholar 

  21. Adams, C.L., Chen, Y.T., Smith, S.J. & Nelson, W.J. Mechanisms of epithelial cell-cell adhesion and cell compaction revealed by high-resolution tracking of E-cadherin-green fluorescent protein. J. Cell Biol. 142, 1105–1119 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Perez, T.D., Tamada, M., Sheetz, M.P. & Nelson, W.J. Immediate-early signaling induced by E-cadherin engagement and adhesion. J. Biol. Chem. 283, 5014–5022 (2008).

    Article  CAS  PubMed  Google Scholar 

  23. Glazier, J.A. & Graner, F. Simulation of the differential adhesion driven rearrangement of biological cells. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics 47, 2128–2154 (1993).

    CAS  PubMed  Google Scholar 

  24. Beltman, J.B., Maree, A.F., Lynch, J.N., Miller, M.J. & de Boer, R.J. Lymph node topology dictates T cell migration behavior. J. Exp. Med. 204, 771–780 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Perez-Moreno, M., Jamora, C. & Fuchs, E. Sticky business: orchestrating cellular signals at adherens junctions. Cell 112, 535–548 (2003).

    Article  CAS  PubMed  Google Scholar 

  26. Hong, S., Troyanovsky, R.B. & Troyanovsky, S.M. Spontaneous assembly and active disassembly balance adherens junction homeostasis. Proc. Natl. Acad. Sci. USA 107, 3528–3533 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Dohlman, H.G. & Thorner, J.W. Regulation of G protein-initiated signal transduction in yeast: paradigms and principles. Annu. Rev. Biochem. 70, 703–754 (2001).

    Article  CAS  PubMed  Google Scholar 

  28. Kofahl, B. & Klipp, E. Modelling the dynamics of the yeast pheromone pathway. Yeast 21, 831–850 (2004).

    Article  CAS  PubMed  Google Scholar 

  29. Shao, D., Zheng, W., Qiu, W., Ouyang, Q. & Tang, C. Dynamic studies of scaffold-dependent mating pathway in yeast. Biophys. J. 91, 3986–4001 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Maeder, C.I. et al. Spatial regulation of Fus3 MAP kinase activity through a reaction-diffusion mechanism in yeast pheromone signalling. Nat. Cell Biol. 9, 1319–1326 (2007).

    Article  CAS  PubMed  Google Scholar 

  31. Good, M., Tang, G., Singleton, J., Remenyi, A. & Lim, W.A. The Ste5 scaffold directs mating signaling by catalytically unlocking the Fus3 MAP kinase for activation. Cell 136, 1085–1097 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bhattacharyya, R.P. et al. The Ste5 scaffold allosterically modulates signaling output of the yeast mating pathway. Science 311, 822–826 (2006).

    Article  CAS  PubMed  Google Scholar 

  33. Evans, R. et al. Integrins in immunity. J. Cell Sci. 122, 215–225 (2009).

    Article  CAS  PubMed  Google Scholar 

  34. Eymard, R., Gallouet, T. & Herbin, R. Finite volume methods. in Handbook of Numerical Analysis (eds. Ciarlet, P.G. & Lions, J.L.) 7, 713–1020 (2000).

    Google Scholar 

  35. Novak, I.L. et al. Diffusion on a curved surface coupled to diffusion in the volume: application to cell biology. J. Comput. Phys. 226, 1271–1290 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank R. Schwartz, R. Varma, A. Nita-Lazar, I. Fraser, J. Tsang and D. Cioffi for helpful comments, members of the Laboratory for Systems Biology at the US National Institute of Allergy and Infectious Diseases for insightful discussions, G. Mack for advice during the early stages of Simmune development and A. Meier-Schellersheim for creating Figure 2 and Supplementary Figure 1. This work was supported by the Intramural Research Program of the US National Institute of Allergy and Infectious Diseases of the National Institutes of Health.

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Authors and Affiliations

Authors

Contributions

B.R.A. wrote the application for designing cellular morphologies. A.D.G. wrote the simulator graphical user interface. M.M.-S. and F.K. developed the volume and membrane spatial discretization. B.R.A. developed the adaptive membrane diffusion discretization. B.R.A. and T.P. performed numerical tests. F.Z. wrote the application for defining molecular interactions. M.M.-S. developed the spatially adaptive network generator. B.R.A. and M.M.-S. developed the integrator drivers. B.R.A., F.K., A.D.G., T.P., F.Z., R.N.G. and M.M.S. discussed the project and wrote the manuscript. M.M.S. conceived and supervised the project.

Corresponding author

Correspondence to Martin Meier-Schellersheim.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Notes 1–6 (PDF 12328 kb)

Supplementary Movie 1

E-cadherin–driven contact formation between two cells. (MOV 1145 kb)

Supplementary Movie 2

E-cadherin–driven contact formation in a segment of a multicellular layer. (MOV 1431 kb)

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Angermann, B., Klauschen, F., Garcia, A. et al. Computational modeling of cellular signaling processes embedded into dynamic spatial contexts. Nat Methods 9, 283–289 (2012). https://doi.org/10.1038/nmeth.1861

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