Skip to main content
SpringerLink
Log in

Gene Regulatory Network Inference: An Introductory Survey

  • Protocol
  • First Online:
Gene Regulatory Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1883))

Abstract

Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-throughput measurement technologies in biology in the late 1990s, reconstructing the structure of such networks has been a central computational problem in systems biology. While the problem is certainly not solved in its entirety, considerable progress has been made in the last two decades, with mature tools now available. This chapter aims to provide an introduction to the basic concepts underpinning network inference tools, attempting a categorization which highlights commonalities and relative strengths. While the chapter is meant to be self-contained, the material presented should provide a useful background to the later, more specialized chapters of this book.

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

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover 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. Crick F (1970) Central dogma of molecular biology. Nature 227(5258):561–563

    Article  CAS  PubMed  Google Scholar 

  2. Ptashne M, Gann A (2002) Genes & signals, vol 192. Cold Spring Harbor Laboratory Press, Cold Spring Harbor

    Google Scholar 

  3. Ptashne M (2004) A genetic switch: phage lambda revisited. Cold Spring Harbor Laboratory Press, Cold Spring Harbor

    Google Scholar 

  4. Ptashne M (2014) The chemistry of regulation of genes and other things. J Biol Chem 289(9):5417–5435

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  5. Bettenbrock K, Bai H, Ederer M, Green J, Hellingwerf KJ, Holcombe M, Kunz S, Rolfe MD, Sanguinetti G, Sawodny O, et al (2014) Towards a systems level understanding of the oxygen response of Escherichia coli. Adv Microb Physiol 64:65–114

    Article  CAS  PubMed  Google Scholar 

  6. Partridge JD, Sanguinetti G, Dibden DP, Roberts RE, Poole RK, Green J (2007) Transition of Escherichia coli from aerobic to micro-aerobic conditions involves fast and slow reacting regulatory components. J Biol Chem 282(15):11230–11237

    Article  CAS  PubMed  Google Scholar 

  7. Rolfe MD, Ter Beek A, Graham AI, Trotter EW, Asif HS, Sanguinetti G, de Mattos JT, Poole RK, Green J (2011) Transcript profiling and inference of Escherichia coli K-12 ArcA activity across the range of physiologically relevant oxygen concentrations. J Biol Chem 286(12):10147–10154

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  8. Alberts B, Bray D, Lewis J, Raff M, Roberts K, Watson JD (1994) Molecular biology of the cell, 3rd edn., vol 43(1294). Garland Pub, New York, p 67

    Google Scholar 

  9. Bird A (2002) DNA methylation patterns and epigenetic memory. Genes Dev 16(1):6–21

    Article  CAS  PubMed  Google Scholar 

  10. Karlić R, Chung HR, Lasserre J, Vlahoviček K, Vingron M (2010) Histone modification levels are predictive for gene expression. Proc Natl Acad Sci 107(7):2926–2931

    Article  PubMed  PubMed Central  Google Scholar 

  11. Benveniste D, Sonntag HJ, Sanguinetti G, Sproul D (2014) Transcription factor binding predicts histone modifications in human cell lines. Proc Natl Acad Sci 111(37):13367–13372

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hogan DJ, Riordan DP, Gerber AP, Herschlag D, Brown PO (2008) Diverse RNA-binding proteins interact with functionally related sets of RNAs, suggesting an extensive regulatory system. PLoS Biol 6(10):e255

    Article  PubMed Central  PubMed  Google Scholar 

  13. Tebaldi T, Re A, Viero G, Pegoretti I, Passerini A, Blanzieri E, Quattrone A (2012) Widespread uncoupling between transcriptome and translatome variations after a stimulus in mammalian cells. BMC Genomics 13(1):220

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  14. Bantscheff M, Lemeer S, Savitski MM, Kuster B (2012) Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Anal Bioanal Chem 404(4):939–965

    Article  CAS  PubMed  Google Scholar 

  15. Brown PO, Botstein D (1999) Exploring the new world of the genome with DNA microarrays. Nat Genet 21:33–37

    Article  CAS  PubMed  Google Scholar 

  16. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Evans C, Hardin J, Stoebel DM (2017) Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions. Brief Bioinform. https://doi.org/10.1093/bib/bbx008

    Article  PubMed Central  Google Scholar 

  18. Park PJ (2009) ChIP–seq: advantages and challenges of a maturing technology. Nat Rev Genet 10(10):669–680

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. West DB (2001) Introduction to graph theory, vol 2. Prentice Hall, Upper Saddle River

    Google Scholar 

  20. Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4(1)

    Google Scholar 

  21. Butte AJ, Kohane IS (1999) Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. In: Pacific symposium on biocomputing 2000. World Scientific, Singapore, pp 418–429

    Chapter  Google Scholar 

  22. Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinf 7(1):S7

    Article  Google Scholar 

  23. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 5(1):e8

    Article  PubMed Central  PubMed  Google Scholar 

  24. Meyer PE, Kontos K, Lafitte F, Bontempi G (2007) Information-theoretic inference of large transcriptional regulatory networks. EURASIP J Bioinf Syst Biol 2007(1):79879

    Google Scholar 

  25. Haury AC, Mordelet F, Vert JP, Vera-Licona P (2012) TIGRESS: trustful inference of gene regulation using stability selection. BMC Syst Biol 6(1):145

    Article  PubMed Central  PubMed  Google Scholar 

  26. Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P (2010) Inferring regulatory networks from expression data using tree-based methods. PLoS One 5(9):e12776

    Article  PubMed Central  PubMed  Google Scholar 

  27. Huynh-Thu VA, Wehenkel L, Geurts P (2013) Gene regulatory network inference from systems genetics data using tree-based methods. In: Gene network inference: verification of methods for systems genetics data. Springer, Berlin, p 63

    Chapter  Google Scholar 

  28. Huynh-Thu VA, Sanguinetti G (2015) Combining tree-based and dynamical systems for the inference of gene regulatory networks. Bioinformatics 31(10):1614–1622

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  29. Michailidis G, d’Alché Buc F (2013) Autoregressive models for gene regulatory network inference: sparsity, stability and causality issues. Math Biosci 246(2):326–334

    Article  PubMed  Google Scholar 

  30. Schäfer J, Strimmer K (2004) An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21(6):754–764

    Article  PubMed  Google Scholar 

  31. Friedman J, Hastie T, Tibshirani R (2008) Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3):432–441

    Article  PubMed  Google Scholar 

  32. Pearl J (2014) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Francisco

    Google Scholar 

  33. Barber D (2012) Bayesian reasoning and machine learning. Cambridge University Press, Cambridge

    Google Scholar 

  34. Friedman N, Linial M, Nachman I, Pe’er D (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7(3–4):601–620

    Article  CAS  PubMed  Google Scholar 

  35. Friedman N, Koller D (2003) Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks. Mach Learn 50(1–2):95–125

    Article  Google Scholar 

  36. Hill SM, Lu Y, Molina J, Heiser LM, Spellman PT, Speed TP, Gray JW, Mills GB, Mukherjee S (2012) Bayesian inference of signaling network topology in a cancer cell line. Bioinformatics 28(21):2804–2810

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  37. Oates CJ, Mukherjee S (2012) Network inference and biological dynamics. Ann Appl Stat 6(3):1209

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  38. Morrissey ER, Juárez MA, Denby KJ, Burroughs NJ (2010) On reverse engineering of gene interaction networks using time course data with repeated measurements. Bioinformatics 26(18):2305–2312

    Article  CAS  PubMed  Google Scholar 

  39. Äijö T, Lähdesmäki H (2009) Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. Bioinformatics 25(22):2937–2944

    Article  PubMed  Google Scholar 

  40. Grzegorczyk M, Husmeier D (2011) Non-homogeneous dynamic Bayesian networks for continuous data. Mach Learn 83(3):355–419

    Article  Google Scholar 

  41. Nodelman U, Shelton CR, Koller D (2002) Continuous time Bayesian networks. In: Proceedings of the eighteenth conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., San Francisco, pp 378–387

    Google Scholar 

  42. Bonneau R, Reiss DJ, Shannon P, Facciotti M, Hood L, Baliga NS, Thorsson V (2006) The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol 7(5):R36

    Article  PubMed Central  PubMed  Google Scholar 

  43. Trejo Banos D, Millar AJ, Sanguinetti G (2015) A Bayesian approach for structure learning in oscillating regulatory networks. Bioinformatics 31(22):3617–3624

    PubMed Central  PubMed  Google Scholar 

  44. Dondelinger F, Husmeier D, Rogers S, Filippone M (2013) ODE parameter inference using adaptive gradient matching with Gaussian processes. In: Artificial intelligence and statistics, pp 216–228

    Google Scholar 

  45. McGoff KA, Guo X, Deckard A, Kelliher CM, Leman AR, Francey LJ, Hogenesch JB, Haase SB, Harer JL (2016) The local edge machine: inference of dynamic models of gene regulation. Genome Biol 17(1):214

    Article  PubMed Central  PubMed  Google Scholar 

  46. Alon U (2006) An introduction to systems biology: design principles of biological circuits. CRC Press, Boca Raton

    Google Scholar 

  47. Niculescu-Mizil A, Caruana R (2007) Inductive transfer for Bayesian network structure learning. In: Artificial intelligence and statistics, pp 339–346

    Google Scholar 

  48. Chiquet J, Grandvalet Y, Ambroise C (2011) Inferring multiple graphical structures. Stat Comput 21(4):537–553

    Article  Google Scholar 

  49. Werhli AV, Husmeier D, et al (2007) Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge. Stat Appl Genet Mol Biol 6(1):15

    Article  Google Scholar 

  50. Penfold CA, Buchanan-Wollaston V, Denby KJ, Wild DL (2012) Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks. Bioinformatics 28(12):i233–i241

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  51. Ahmed A, Xing EP (2009) Recovering time-varying networks of dependencies in social and biological studies. Proc Natl Acad Sci 106(29):11878–11883

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  52. Robinson JW, Hartemink AJ (2010) Learning non-stationary dynamic Bayesian networks. J Mach Learn Res 11(Dec):3647–3680

    Google Scholar 

  53. Lebre S, Becq J, Devaux F, Stumpf MP, Lelandais G (2010) Statistical inference of the time-varying structure of gene-regulation networks. BMC Syst Biol 4(1):130

    Article  PubMed Central  PubMed  Google Scholar 

  54. Thorne T, Stumpf MP (2012) Inference of temporally varying Bayesian networks. Bioinformatics 28(24):3298–3305

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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

    Article  Google Scholar 

  56. Marbach D, Costello JC, Küffner R, Vega NM, Prill RJ, Camacho DM, Allison KR, Kellis M, Collins JJ, Stolovitzky G, et al (2012) Wisdom of crowds for robust gene network inference. Nat Methods 9(8):796–804

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  57. Cantone I, Marucci L, Iorio F, Ricci MA, Belcastro V, Bansal M, Santini S, Di Bernardo M, Di Bernardo D, Cosma MP (2009) A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell 137(1):172–181

    Article  CAS  PubMed  Google Scholar 

  58. Davis J, Goadrich M (2006) The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd international conference on machine learning. ACM, New York, pp 233–240

    Google Scholar 

Download references

Acknowledgements

GS acknowledges support from the European Research Council under grant MLCS 306999. VAHT is a Post-doctoral Fellow of the F.R.S.-FNRS.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium

    Vân Anh Huynh-Thu

  2. School of Informatics, University of Edinburgh, Edinburgh, UK

    Guido Sanguinetti

Authors
  1. Vân Anh Huynh-Thu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guido Sanguinetti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guido Sanguinetti .

Editor information

Editors and Affiliations

  1. School of Informatics, University of Edinburgh, Edinburgh, UK

    Guido Sanguinetti

  2. Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium

    Vân Anh Huynh-Thu

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Huynh-Thu, V.A., Sanguinetti, G. (2019). Gene Regulatory Network Inference: An Introductory Survey. In: Sanguinetti, G., Huynh-Thu, V. (eds) Gene Regulatory Networks. Methods in Molecular Biology, vol 1883. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8882-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-8882-2_1

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8881-5

  • Online ISBN: 978-1-4939-8882-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation