Skip to main content
SpringerLink
Log in

Inferring Gene Interaction Networks

  • Chapter
  • First Online:
Computational Cancer Biology

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSCONTROL))

  • 1788 Accesses

Abstract

This chapter contains the original research results on the monograph. We study the problem of reverse-engineering context-specific, genome-wide interaction networks from expression data. Two existing classes of methods, namely those based on mutual information and those based on Bayesian networks, are described first. Then a new algorithm, based on the so-called phi-mixing coefficient between random variables, is introduced. Unlike mutual information, the phi-mixing coefficient provides a directionally sensitive measure of the dependence between two random variables. The algorithm based on this new approach produces a gene interaction network in the form of a directed, strongly connected graph. The approach is validated on ChIP-seq data around the transcription factor ASCL1 in a lung cancer network.

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

Notes

  1. 1.

    Hereafter we shall avoid the unwieldy phrase ‘genes or gene products’ and shall instead say just ‘genes’. However, proteins are also encompassed in the phrase ‘gene products’, and protein interaction networks (PINs) are therefore subsumed by the phrase GINs introduced a little later.

  2. 2.

    Note that the data for a single cell line could itself be a compendium of data obtained through multiple experiments carried out at different times.

  3. 3.

    Note that language used here is not identical to that in [14] but is mathematically equivalent.

  4. 4.

    Note that since the graph is undirected, it is not necessary to specify the direction.

  5. 5.

    Medterms [17] defines a proto-oncogene as “A normal gene which, when altered by mutation, becomes an oncogene that can contribute to cancer,” and an oncogene as “A gene that played a normal role in the cell as a proto-oncogene and that has been altered by mutation and now may contribute to the growth of a tumor.”

  6. 6.

    The notion of a copula was introduced in [20]. See [21] for an excellent introduction to the topic.

  7. 7.

    It is interesting to note that in a preprint version of [22], their method is claimed to take only 1.6 h.

  8. 8.

    Recall that the ‘central dogma’ of biology, as enunciated by Francis Crick [34] states that DNA is converted to RNA (transcription) which is then converted to protein(s) (translation).

  9. 9.

    In the interests of brevity, these are referred to, somewhat inaccurately, as ‘ChIP-seq genes’.

References

  1. Zhou, Q., et al.: A gene regulatory network in mouse embryonic stem cells. Proc. Natl. Acad. Sci. 104(42), 16,438–16,443 (2007)

    Google Scholar 

  2. Intact: http://www.ebi.ac.uk/intact/

  3. Mint: http://mint.bio.uniroma2.it/mint/welcome.do

  4. Biogrid: http://thebiogrid.org/

  5. String: http://thebiogrid.org/

  6. Komurov, K., White, M.A., Ram, P.T.: Use of data-biased random walks on graphs for the retrieval of context-specific networks from genomic data. PLoS Comput. Biol. 6(8), (2010)

    Google Scholar 

  7. Szklarczyk, D., et al.: The string database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39, D561–D568 (2011)

    Google Scholar 

  8. Kim, Y., et al.: Principal network analysis: identification of subnetworks representing major dynamics using gene expression data. Bioinformatics 27(3), 391–398 (2011)

    Article  Google Scholar 

  9. Pe’er, D., Hacohen, M.: Principles and strategies for developing network models in cancer. Cell 144, 864–873 (2011)

    Google Scholar 

  10. Basso, K., et al.: Reverse engineering of regulatory networks in human b cells. Nat. Genet. 37(4), 382–390 (2005)

    Article  MathSciNet  Google Scholar 

  11. GEO: http://www.ncbi.nlm.nih.gov/geo/

  12. TCGA: http://cancergenome.nih.gov

  13. Butte, A.J., Kohane, I.S.: Mutual information relevance networks: functional genomic clustering using pairwise entropy measures. Pac. Symp. Biocomput. 5, 418–429 (2000)

    Google Scholar 

  14. Margolin, A.A., et al.: Aracne: an algorithm for the reconstruction of gene regulatory networks in a cellular context. BMC Bioinform. 7(Supplement 1), S7 (2008)

    Google Scholar 

  15. Chow, C.K., Liu, C.N.: Approximating discrete probability distributions with dependence trees. EEE Trans. Info. Thy. 14(3), 462–467 (1968)

    Article  MathSciNet  MATH  Google Scholar 

  16. Cover, T.M., Thomas, J.A.: Elements of Information Theory, 2nd edn. Wiley Interscience, New York (2006)

    MATH  Google Scholar 

  17. Medterms: http://www.medterms.com

  18. Wang, K., et al.: Genome-wide identification of post-translational modulators of transcription factor activity in human b cells. Nat. Biotechnol. 27(9), 829–839 (2009)

    Article  Google Scholar 

  19. Zhao, W., Serpedin, E., Dougherty, E.R.: Inferring connectivity of genetic regulatory networks using information theoretic criteria. IEEE/ACM Trans. Comput. Biol. Bioinf. 5(2), 262–274 (2008)

    Google Scholar 

  20. Sklar, M.: Fonctions de répartition à \(n\) dimension et leurs marges. Publications de l’Institut Statistiques, Université de Paris 8, 229–231 (1959)

    MathSciNet  Google Scholar 

  21. Durante, F., Sempi, C.: Copula theory: An introduction. In: Jaworski, P., Durante, F., Härdle, W., Rychlik, T. (eds.) Copula Theory and Its Applications. Lecture Notes in Statistics. Springer, Berlin (2010)

    Google Scholar 

  22. Qiu, P., Gentles, A.J., Plevritis, S.K.: Reducing the computational complexity of information theoretic approaches for reconstructing gene regulatory networks. J. Comput. Biol. 17(2), 169–176 (2010)

    Article  Google Scholar 

  23. Belcastro, V., et al.: Reverse engineering and analysis of genome-wide gene regulatory networks from gene expression profiles using high-performance computing. IEEE/ACM Trans. Comput. Biol. Bioinform. 9(3), 668–674 (2012)

    Google Scholar 

  24. Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco (1988)

    Google Scholar 

  25. Friedman, N., et al.: Using bayesian networks to analyze expression data. J. Comput. Biol. 7(3–4), 601–620 (2000)

    Google Scholar 

  26. Barash, Y., Friedman, N.: Context-specific bayesian clustering for gene expression data. J. Comput. Biol. 9(2), 169–191 (2002)

    Article  Google Scholar 

  27. Friedman, N.: Inferring cellular networks using probabilistic graphical models. Science 303, 799–805 (2004)

    Article  Google Scholar 

  28. Heckerman, D.: A tutorial on learning with bayesian networks. In: Jordan, M.I. (ed.) Learning in Graphical Models. MIT Press, Cambridge (1998)

    Google Scholar 

  29. Spitzer, F.: Markov random fields and gibbs ensembles. Am. Math. Monthly 78(2), 142–154 (1971)

    Article  MathSciNet  MATH  Google Scholar 

  30. Vidyasagar, M.: Learning and Generalization: With Applications to Neural Networks and Control Systems. Springer, London (2003)

    MATH  Google Scholar 

  31. Doukhan, P.: Mixing: Properties and Examples. Springer, Heidelberg (1994)

    MATH  Google Scholar 

  32. Ibragimov, I.A.: Some lilmit theorems for stationary processes. Theor. Probab. Appl. 7, 349–382 (1962)

    Article  Google Scholar 

  33. Wang, Q., Kulkarni, S.R., Verdú, S.: Divergence estimation of continuous distributions based on data-dependent partitions. IEEE Trans. Informa. Theor. 51(9), 3064–3074 (2005)

    Article  Google Scholar 

  34. Crick, F.H.C.: Central dogma of molecular biology. Nature 227, 561–563 (1970)

    Google Scholar 

  35. Liu, E.T., Pott, S., Huss, M.: Q&a: Chip-seq technologies and the study of gene regulation. BMC Biol. 8, 56 (2010). http://www.biomedcentral.com/1741-7007/8/56

  36. McLean, C.Y., et al.: Great improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28(5), 495–501 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Bioengineering Department, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX, 75080-3021, USA

    Mathukumalli Vidyasagar

Authors
  1. Mathukumalli Vidyasagar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mathukumalli Vidyasagar .

Rights and permissions

Reprints and permissions

Copyright information

© 2012 The Author(s)

About this chapter

Cite this chapter

Vidyasagar, M. (2012). Inferring Gene Interaction Networks. In: Computational Cancer Biology. SpringerBriefs in Electrical and Computer Engineering(). Springer, London. https://doi.org/10.1007/978-1-4471-4751-0_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-4751-0_3

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4750-3

  • Online ISBN: 978-1-4471-4751-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation