Skip to main content
SpringerLink
Log in

Switching Gene Regulatory Networks

  • Conference paper
  • First Online:
Information Processing in Cells and Tissues (IPCAT 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9303))

Abstract

A fundamental question in biology is how cells change into specific cell types with unique roles throughout development. This process can be viewed as a program prescribing the system dynamics, governed by a network of genetic interactions. Recent experimental evidence suggests that these networks are not fixed but rather change their topology as cells develop. Currently, there are limited tools for the construction and analysis of such self-modifying biological programs.We introduce Switching Gene Regulatory Networks to enable the modeling and analysis of network reconfiguration, and define the synthesis problem of constructing switching networks from observations of cell behavior. We solve the synthesis problem using Satisfiability Modulo Theories (SMT) based methods, and evaluate the feasibility of our method by considering a set of synthetic benchmarks exhibiting typical biological behavior of cell development.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ahmed, A., Xing, E.: Recovering time-varying networks of dependencies in social and biological studies. In: Proceedings of the National Academy of Sciences, vol. 106, no. 29, pp. 11878-11883 (2009)

    Google Scholar 

  2. Albert, R.: Scale-free networks in cell biology. J. Cell Sci. 118(21), 4947–4957 (2005)

    Article  Google Scholar 

  3. Bashe, C., Johnson, L., Palmer, J., Pugh, E.: IBM’s early computers. MIT Press (1986)

    Google Scholar 

  4. Biere, A., Cimatti, A., Clarke, E., Zhu, Y.: Symbolic model checking without BDDs. In: Cleaveland, W.R. (ed.) TACAS 1999. LNCS, vol. 1579, p. 193. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  5. Chaouiya, C.: Petri net modelling of biological networks. Briefings Bioinform. 8(4), 210–219 (2007)

    Article  Google Scholar 

  6. Dondelinger, F., Lébre, S., Husmeier, D.: Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure. Mach. Learn. 90(2), 191–230 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  7. Doursat, R.: The growing canvas of biological development: multiscale pattern generation on an expanding lattice of gene regulatory nets. In: Minai, A., Braha, D., Bar-Yam, Y. (eds.) Unifying Themes in Complex Systems, pp. 205–210. Springer, Heidelberg (2008)

    Google Scholar 

  8. Dunn, S., Martello, G., Yordanov, B., Emmott, S., Smith, A.: Defining an essential transcription factor program for naïve pluripotency. Sci. 344(6188), 1156–1160 (2014)

    Article  Google Scholar 

  9. Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using Bayesian networks to analyze expression data. J. Comp. Bio. 3(7), 601–620 (2000)

    Article  Google Scholar 

  10. Giavittob, J., Klaudela, H., Pommereau, F.: Integrated regulatory networks (IRNs): spatially organized biochemical modules. Theoret. Comput. Sci. 431, 219–234 (2012)

    Article  MathSciNet  Google Scholar 

  11. Heiner, M., Gilbert, D., Donaldson, R.: Petri nets for systems and synthetic biology. In: Bernardo, M., Degano, P., Zavattaro, G. (eds.) SFM 2008. LNCS, vol. 5016, pp. 215–264. Springer, Heidelberg (2008). Advanced Lectures

    Chapter  Google Scholar 

  12. Hofestädt, R., Thelen, S.: Quantitative modeling of biochemical networks. Silico Biol. 1(1), 39–53 (1998)

    Google Scholar 

  13. Kauffman, S.: Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22(3), 437–467 (1969)

    Article  Google Scholar 

  14. Khan, J., Bouaynaya, N., Fathallah-Shaykh, H.: Tracking of time-varying genomic regulatory networks with a LASSO-Kalman smoother. EURASIP J. Bioinf. Sys. Bio. 1(2014), 1–15 (2014)

    Google Scholar 

  15. Maraninchi, F., Rémond, Y.: Mode-automata: about modes and states for reactive systems. In: Hankin, C. (ed.) ESOP 1998. LNCS, vol. 1381, p. 185. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  16. de Moura, L., Bjørner, N.S.: Z3: An Efficient SMT Solver. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 337–340. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  17. von Neumann, J.: First draft of a report on the EDVAC. Technical report Contract No. W670ORD4926, Moore School of Electrical Engineering, University of Pennsylvania (1945)

    Google Scholar 

  18. Parikh, A., Wu, W., Curtis, R., Xing, E.: TREEGL: reverse engineering tree-evolving gene networks underlying developing biological lineages. Bioinf. 27(13), i196–i204 (2011)

    Article  Google Scholar 

  19. Phillips, T.: Regulation of transcription and gene expression in eukaryotes. Nat. Educ. 1(1), 199 (2008)

    Google Scholar 

  20. Ramadge, P.J., Wonham, W.M.: Supervisory control of a class of discrete event processes. SIAM J. Control Optim. 25(1), 206–230 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  21. Rao, A., Hero, A., States, D., Engel, J.: Inferring time-varying network topologies from gene expression data. EURASIP J. Bioinformatics Syst. Biol. 2007, 7–7 (2007)

    Google Scholar 

  22. Song, L., Kolar, M., Xing, E.: Time-varying dynamic Bayesian networks. In: Advances in Neural Information Processing Systems (NIPS) pp. 1732-1740 (2009)

    Google Scholar 

  23. Stergachis, A.B., et al.: Developmental fate and cellular maturity encoded in human regulatory DNA landscapes. Cell 154(4), 888–903 (2013)

    Article  Google Scholar 

  24. Thomas, R., Kaufman, M.: Multistationarity, the basis of cell differentiation and memory. ii. logical analysis of regulatory networks in terms of feedback circuits. Chaos: An Interdisciplinary J. Nonlinear Sci. 11(1), 180–195 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  25. Valk, R.: Self-modifying nets, a natural extension of Petri nets. In: Ausiello, G., Böhm, R. (eds.) Colloquium on Automata, Languages and Programming. LNCS, pp. 464–476. Springer, Heidelberg (1978)

    Chapter  Google Scholar 

  26. Yordanov, B., Wintersteiger, C., Hamadi, Y., Kugler, H.: Z34Bio: an SMT-based framework for analyzing biological computation. In: SMT (2013)

    Google Scholar 

Download references

Acknowledgments.

Yoli Shavit is supported by the Cambridge International Scholarship Scheme (CISS). The research was carried out during her internship at Microsoft Research Cambridge, UK.

Author information

Authors and Affiliations

  1. University of Cambridge, Cambridge, UK

    Yoli Shavit

  2. Microsoft Research, Cambridge, UK

    Yoli Shavit, Boyan Yordanov, Sara-Jane Dunn, Christoph M. Wintersteiger, Youssef Hamadi & Hillel Kugler

  3. Bar-Ilan University, Ramat Gan, Israel

    Hillel Kugler

Authors
  1. Yoli Shavit
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Boyan Yordanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sara-Jane Dunn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christoph M. Wintersteiger
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Youssef Hamadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hillel Kugler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hillel Kugler .

Editor information

Editors and Affiliations

  1. Heriot-Watt University, Edinburgh, United Kingdom

    Michael Lones

  2. University of York, York, United Kingdom

    Andy Tyrrell

  3. University of York, York, United Kingdom

    Stephen Smith

  4. Natural Selection, Inc., San Diego, California, USA

    Gary Fogel

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Shavit, Y., Yordanov, B., Dunn, SJ., Wintersteiger, C.M., Hamadi, Y., Kugler, H. (2015). Switching Gene Regulatory Networks. In: Lones, M., Tyrrell, A., Smith, S., Fogel, G. (eds) Information Processing in Cells and Tissues. IPCAT 2015. Lecture Notes in Computer Science(), vol 9303. Springer, Cham. https://doi.org/10.1007/978-3-319-23108-2_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23108-2_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23107-5

  • Online ISBN: 978-3-319-23108-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation