Advertisement

Guidance for Data Collection and Computational Modelling of Regulatory Networks

  • Adam Christopher Palmer
  • Keith Edward Shearwin
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 541)

Abstract

Many model regulatory networks are approaching the depth of characterisation of bacteriophage λ, wherein the vast majority of individual components and interactions are identified, and research can focus on understanding whole network function and the role of interactions within that broader context. In recent years, the study of the system-wide behaviour of phage λ’s genetic regulatory network has been greatly assisted by the combination of quantitative measurements with theoretical and computational analyses. Such research has demonstrated the value of a number of general principles and guidelines for making use of the interplay between experiments and modelling. In this chapter we discuss these guidelines and provide illustration through reference to case studies from phage λ biology.

In our experience, computational modelling is best facilitated with a large and diverse set of quantitative, in vivo data, preferably obtained from standardised measurements and expressed as absolute units rather than relative units. Isolation of subsets of regulatory networks may render a system amenable to ‘bottom-up’ modelling, providing a valuable tool to the experimental molecular biologist. Decoupling key components and rendering their concentration or activity an independent experimental variable provide excellent information for model building, though conclusions drawn from isolated and/or decoupled systems should be checked against studies in the full physiological context; discrepancies are informative. The construction of a model makes possible in silico experiments, which are valuable tools for both the data analysis and the design of wet experiments.

Key words

Computational modelling systems biology gene regulatory network experiment design promoter regulation in silico experiment bacteriophage λ DNA looping 

Notes

Acknowledgements

We thank J. Barry Egan and Ian B. Dodd for discussions. Research in our laboratory is supported by the U.S. NIH (GM062976) and the Australian Research Council.

References

  1. 1.
    Kolch W, Calder M, Gilbert D. When kinases meet mathematics: the systems biology of MAPK signalling. FEBS Lett 2005;579(8):1891–5.PubMedCrossRefGoogle Scholar
  2. 2.
    Kitano H. Computational systems biology Nature 2002;420(6912):206–10.Google Scholar
  3. 3.
    Miller JH, ed. Experiments in Molecular Genetics. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press; 1972.Google Scholar
  4. 4.
    Dodd IB, Perkins AJ, Tsemitsidis D, Egan JB. Octamerization of lambda CI repressor is needed for effective repression of P(RM) and efficient switching from lysogeny. Genes Dev 2001;15(22):3013–22.PubMedCrossRefGoogle Scholar
  5. 5.
    Simons RW, Houman F, Kleckner N. Improved single and multicopy lac-based cloning vectors for protein and operon fusions. Gene 1987;53(1):85–96.PubMedCrossRefGoogle Scholar
  6. 6.
    Haldimann A, Wanner BL. Conditional-replication, integration, excision, and retrieval plasmid-host systems for gene structure-function studies of bacteria. J Bacteriol 2001;183(21):6384–93.PubMedCrossRefGoogle Scholar
  7. 7.
    Jensen PR, Hammer K. Artificial promoters for metabolic optimization. Biotechnol Bioeng 1998;58(2–3):191–5.PubMedCrossRefGoogle Scholar
  8. 8.
    Su LT, Agapito MA, Li M, et al. TRPM7 regulates cell adhesion by controlling the calcium-dependent protease calpain. J Biol Chem 2006;281(16):11260–70.PubMedCrossRefGoogle Scholar
  9. 9.
    Linn T, St Pierre R. Improved vector system for constructing transcriptional fusions that ensures independent translation of lacZ. J Bacteriol 1990;172(2):1077–84.PubMedGoogle Scholar
  10. 10.
    Liang S, Bipatnath M, Xu Y, et al. Activities of constitutive promoters in Escherichia coli. J Mol Biol 1999;292(1):19–37.PubMedCrossRefGoogle Scholar
  11. 11.
    Blakeslee S. Scientist at Work: John Henry Holland; searching for simple rules of complexity. The New York Times 1995 December 26, 1995.Google Scholar
  12. 12.
    Atsumi S, Little JW. Regulatory circuit design and evolution using phage lambda. Genes Dev 2004;18(17):2086–94.PubMedCrossRefGoogle Scholar
  13. 13.
    Atsumi S, Little JW. Role of the lytic repressor in prophage induction of phage lambda as analyzed by a module-replacement approach. Proc Natl Acad Sci U S A 2006;103(12):4558–63.PubMedCrossRefGoogle Scholar
  14. 14.
    Raser JM, O’Shea EK. Noise in gene expression: origins, consequences, and control. Science 2005;309(5743):2010–3.PubMedCrossRefGoogle Scholar
  15. 15.
    Sneppen K, Dodd IB, Shearwin KE, et al. A mathematical model for transcriptional interference by RNA polymerase traffic in Escherichia coli. J Mol Biol 2005;346(2):399–409.PubMedCrossRefGoogle Scholar
  16. 16.
    Forger DB, Peskin CS. Stochastic simulation of the mammalian circadian clock. Proc Natl Acad Sci U S A 2005;102(2):321–4.PubMedCrossRefGoogle Scholar
  17. 17.
    Vilar JM, Leibler S. DNA looping and physical constraints on transcription regulation. J Mol Biol 2003;331(5):981–9.PubMedCrossRefGoogle Scholar
  18. 18.
    Revet B, von Wilcken-Bergmann B, Bessert H, Barker A, Muller-Hill B. Four dimers of lambda repressor bound to two suitably spaced pairs of lambda operators form octamers and DNA loops over large distances. Curr Biol 1999;9(3):151–4.PubMedCrossRefGoogle Scholar
  19. 19.
    Dodd IB, Shearwin KE, Perkins AJ, Burr T, Hochschild A, Egan JB. Cooperativity in long-range gene regulation by the lambda CI repressor. Genes Dev 2004;18(3):344–54.PubMedCrossRefGoogle Scholar
  20. 20.
    Hochschild A. The lambda switch: cI closes the gap in autoregulation. Curr Biol 2002;12(3):R87–9.PubMedCrossRefGoogle Scholar
  21. 21.
    Ptashne M. A Genetic switch. Phage Lambda Revisited. 3rd ed. Cold Spring Harbor, N.Y.: Cold Spring Harbor Laboratory Press; 2004.Google Scholar
  22. 22.
    Saiz L, Rubi JM, Vilar JM. Inferring the in vivo looping properties of DNA. Proc Natl Acad Sci U S A 2005;102(49):17642–5.PubMedCrossRefGoogle Scholar
  23. 23.
    Callen BP, Shearwin KE, Egan JB. Transcriptional interference between convergent promoters caused by elongation over the promoter. Mol Cell 2004;14(5):647–56.PubMedCrossRefGoogle Scholar
  24. 24.
    Shearwin KE, Callen BP, Egan JB. Transcriptional interference – a crash course. Trends Genet 2005;21(6):339–45.PubMedCrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Adam Christopher Palmer
    • 1
  • Keith Edward Shearwin
    • 1
  1. 1.School of Molecular and Biomedical Science, The University of AdelaideAdelaideAustralia

Personalised recommendations