Discrete Dynamic Modeling with Asynchronous Update, or How to Model Complex Systems in the Absence of Quantitative Information

  • Sarah M. Assmann
  • Réka Albert
Protocol
Part of the Methods in Molecular Biology™ book series (MIMB, volume 553)

Abstract

A major aim of systems biology is the study of the inter-relationships found within and between large biological data sets. Here we describe one systems biology method, in which the tools of network analysis and discrete dynamic (Boolean) modeling are used to develop predictive models of cellular signaling in cases where detailed temporal and kinetic information regarding the propagation of the signal through the system is lacking. This approach is also applicable to data sets derived from some other types of biological systems, such as transcription factor-mediated regulation of gene expression during the control of developmental fate, or host defense responses following pathogen attack, and is equally applicable to plant and non-plant systems. The method also allows prediction of how elimination of one or more individual signaling components will affect the ultimate outcome, thus allowing the researcher to model the effects of genetic knockout or pharmacological block. The method also serves as a starting point from which more quantitative models can be developed as additional information becomes available.

Key words

Boolean model computational biology dynamic modeling discrete model network analysis signal transduction systems biology 

References

  1. 1.
    Figeys, D., McBroom, L.D., and Moran, M.F. (2001) Mass spectrometry for the study of protein–protein interactions. Methods 24(3), 230–239.PubMedCrossRefGoogle Scholar
  2. 2.
    Berggard, T., Linse, S., and James, P. (2007) Methods for the detection and analysis of protein–protein interactions. Proteomics 7(16), 2833–2842.PubMedCrossRefGoogle Scholar
  3. 3.
    Walhout, A.J. and Vidal, M. (2001) High-throughput yeast two-hybrid assays for large-scale protein interaction mapping. Methods 24(3), 297–306.PubMedCrossRefGoogle Scholar
  4. 4.
    Legrain, P. and Selig, L. (2000) Genome-wide protein interaction maps using two-hybrid systems. FEBS Lett. 480(1), 32–36.PubMedCrossRefGoogle Scholar
  5. 5.
    Fields, S. (2005) High-throughput two-hybrid analysis. The promise and the peril. FEBS J. 272(21), 5391–5399.PubMedCrossRefGoogle Scholar
  6. 6.
    Obrdlik, P., El-Bakkoury, M., Hamacher, T., et al. (2004) K+ channel interactions detected by a genetic system optimized for systematic studies of membrane protein interactions. Proc. Natl. Acad. Sci. USA 101(33), 12242–12247.PubMedCrossRefGoogle Scholar
  7. 7.
    Uetz, P., Giot, L., Cagney, G., et al. (2000) A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature 403(6770), 623–627.PubMedCrossRefGoogle Scholar
  8. 8.
    Schwikowski, B., Uetz, P., and Fields, S. (2000) A network of protein–protein interactions in yeast. Nat. Biotechnol. 18(12), 1257–1261.PubMedCrossRefGoogle Scholar
  9. 9.
    Rain, J.C., Selig, L., De Reuse, H., et al. (2001) The protein–protein interaction map of Helicobacter pylori. Nature 409(6817), 211–215.PubMedCrossRefGoogle Scholar
  10. 10.
    Buck, M.J. and Lieb, J.D. (2004) ChIP-chip: considerations for the design, analysis, and application of genome-wide chromatin immunoprecipitation experiments. Genomics 83(3), 349–360.PubMedCrossRefGoogle Scholar
  11. 11.
    Haring, M., Offermann, S., Danker, T., Horst, I., Peterhansel, C., and Stam, M. (2007) Chromatin immunoprecipitation: optimization, quantitative analysis and data normalization. Plant Methods 3, 11.PubMedCrossRefGoogle Scholar
  12. 12.
    Hudson, M.E. and Snyder, M. (2006) High-throughput methods of regulatory element discovery. Biotechniques 41(6), 673, 5, 7 passim.PubMedCrossRefGoogle Scholar
  13. 13.
    Mockler, T.C., Chan, S., Sundaresan, A., Chen, H., Jacobsen, S.E. and Ecker, J.R. (2005) Applications of DNA tiling arrays for whole-genome analysis. Genomics 85(1), 1–15.PubMedCrossRefGoogle Scholar
  14. 14.
    de Folter, S., Urbanus, S.L., van Zuijlen, L.G., Kaufmann, K., and Angenent, G.C. (2007) Tagging of MADS domain proteins for chromatin immunoprecipitation. BMC Plant Biol. 7, 47.PubMedCrossRefGoogle Scholar
  15. 15.
    Lee, J., He, K., Stolc, V., et al. (2007) Analysis of transcription factor HY5 genomic binding sites revealed its hierarchical role in light regulation of development. Plant Cell 19(3), 731–749.PubMedCrossRefGoogle Scholar
  16. 16.
    Peck, S.C. (2006) Phosphoproteomics in Arabidopsis: moving from empirical to predictive science. J. Exp. Bot. 57(7), 1523–1527.PubMedCrossRefGoogle Scholar
  17. 17.
    Blom, N., Sicheritz-Ponten, T., Gupta, R., Gammeltoft, S., and Brunak, S. (2004) Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence. Proteomics 4(6), 1633–1649.PubMedCrossRefGoogle Scholar
  18. 18.
    de la Fuente van Bentem, S. and Hirt, H. (2007) Using phosphoproteomics to reveal signalling dynamics in plants. Trends Plant Sci. 12(9), 404–411.Google Scholar
  19. 19.
    Li, S., Assmann, S.M., and Albert, R. (2006) Predicting essential components of signal transduction networks: a dynamic model of guard cell abscisic acid signaling. PLoS Biol. 4(10), e312.PubMedCrossRefGoogle Scholar
  20. 20.
    Albert, R., DasGupta, B., Dondi, R., et al. (2007) A novel method for signal transduction network inference from indirect experimental evidence. J. Comput. Biol. 14(7), 927–949.PubMedCrossRefGoogle Scholar
  21. 21.
    Voit, E.O. (2000) Computational Analysis of Biochemical Systems. Cambridge: Cambridge University Press.Google Scholar
  22. 22.
    Tyson, J.J., Chen, K.C., and Novak, B. (2003) Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr. Opin. Cell Biol. 15(2), 221–231.PubMedCrossRefGoogle Scholar
  23. 23.
    Friedman, C., Kra, P., Yu, H., Krauthammer, M., and Rzhetsky, A. (2001) GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles. Bioinformatics 17 Suppl 1, S74–S82.PubMedGoogle Scholar
  24. 24.
    Marcotte, E.M., Xenarios, I., and Eisenberg, D. (2001) Mining literature for protein–protein interactions. Bioinformatics 17(4), 359–363.PubMedCrossRefGoogle Scholar
  25. 25.
    Jensen, L.J., Saric, J., and Bork, P. (2006) Literature mining for the biologist: from information retrieval to biological discovery. Nat. Rev. Genet. 7(2), 119–129.PubMedCrossRefGoogle Scholar
  26. 26.
    Chaves, M., Albert, R., and Sontag, E.D. (2005) Robustness and fragility of Boolean models for genetic regulatory networks. J. Theor. Biol. 235(3), 431–449.PubMedCrossRefGoogle Scholar
  27. 27.
    Gazzarrini, S. and McCourt, P. (2003) Cross-talk in plant hormone signalling: what Arabidopsis mutants are telling us. Ann. Bot. (Lond) 91(6), 605–612.CrossRefGoogle Scholar
  28. 28.
    Fujita, M., Fujita, Y., Noutoshi, Y., et al. (2006) Crosstalk between abiotic and biotic stress responses: a current view from the points of convergence in the stress signaling networks. Curr. Opin. Plant Biol. 9(4), 436–442.PubMedCrossRefGoogle Scholar
  29. 29.
    Ingolia, N.T. (2004) Topology and robustness in the Drosophila segment polarity network. PLoS Biol. 2(6), e123.PubMedCrossRefGoogle Scholar
  30. 30.
    Barkai, N. and Leibler, S. (1997) Robustness in simple biochemical networks. Nature 387(6636), 913–917.PubMedCrossRefGoogle Scholar
  31. 31.
    von Dassow, G., Meir, E., Munro, E.M., and Odell, G.M. (2000) The segment polarity network is a robust developmental module. Nature 406(6792), 188–192.CrossRefGoogle Scholar
  32. 32.
    Li, F., Long, T., Lu, Y., Ouyang, Q., and Tang, C. (2004) The yeast cell-cycle network is robustly designed. Proc. Natl. Acad. Sci. USA 101(14), 4781–4786.PubMedCrossRefGoogle Scholar
  33. 33.
    Thakar, J., Pilione, M., Kirimanjeswara, G., Harvill, E.T., and Albert, R. (2007) Modeling systems-level regulation of host immune responses. PLoS Comput. Biol. 3(6), e109.PubMedCrossRefGoogle Scholar
  34. 34.
    Ghysen, A. and Thomas, R. (2003) The formation of sense organs in Drosophila: a logical approach. Bioessays 25(8), 802–807.PubMedCrossRefGoogle Scholar
  35. 35.
    Mendoza, L. and Alvarez-Buylla, E.R. (1998) Dynamics of the genetic regulatory network for Arabidopsis thaliana flower morphogenesis. J. Theor. Biol. 193(2), 307–319.PubMedCrossRefGoogle Scholar
  36. 36.
    Espinosa-Soto, C., Padilla-Longoria, P., and Alvarez-Buylla, E.R. (2004) A gene regulatory network model for cell-fate determination during Arabidopsis thaliana flower development that is robust and recovers experimental gene expression profiles. Plant Cell 16(11), 2923–2939.PubMedCrossRefGoogle Scholar
  37. 37.
    Sanchez, L. and Thieffry, D. (2003) Segmenting the fly embryo: a logical analysis of the pair-rule cross-regulatory module. J. Theor. Biol. 224(4), 517–537.PubMedCrossRefGoogle Scholar
  38. 38.
    Mendoza, L. and Alvarez-Buylla, E.R. (2000) Genetic regulation of root hair development in Arabidopsis thaliana: a network model. J. Theor. Biol. 204(3), 311–326.PubMedCrossRefGoogle Scholar
  39. 39.
    Chaves, M., Sontag, E.D., and Albert, R. (2006) Methods of robustness analysis for Boolean models of gene control networks. Syst. Biol. (Stevenage) 153(4), 154–167.Google Scholar

Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Sarah M. Assmann
    • 1
  • Réka Albert
    • 2
  1. 1.Biology DepartmentPenn State UniversityUniversity ParkUSA
  2. 2.Physics DepartmentPenn State UniversityUniversity ParkUSA

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