Gene regulatory network inference: An Introductory Survey

[en] Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-throughput measurement technologies in biology in the late 90s, 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 categorisation 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 specialised chapters of this book.

Disciplines :

Computer science
Genetics & genetic processes

Author, co-author :

Huynh-Thu, Vân Anh ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique

Sanguinetti, Guido

Language :

English

Title :

Gene regulatory network inference: An Introductory Survey

Publication date :

2019

Main work title :

Gene Regulatory Networks

Editor :

Sanguinetti, Guido

Huynh-Thu, Vân Anh ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science

Publisher :

Humana Press, New York, United States - New York

Collection name :

Methods in Molecular Biology, vol 1883

Available on ORBi :

since 19 December 2018

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Bibliography

Crick F (1970) Central dogma of molecular biology. Nature 227(5258):561–563
Ptashne M, Gann A (2002) Genes & signals, vol 192. Cold Spring Harbor Laboratory Press, Cold Spring Harbor
Ptashne M (2004) A genetic switch: phage lambda revisited. Cold Spring Harbor Laboratory Press, Cold Spring Harbor
Ptashne M (2014) The chemistry of regulation of genes and other things. J Biol Chem 289(9):5417–5435
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
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
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
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
Bird A (2002) DNA methylation patterns and epigenetic memory. Genes Dev 16(1):6–21
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
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
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
Tebaldi T, Re A, Viero G, Pegoretti I, Passerini A, Blanzieri E, Quattrone A (2012) Widespread uncoupling between transcriptome and trans-latome variations after a stimulus in mammalian cells. BMC Genomics 13(1):220
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
Brown PO, Botstein D (1999) Exploring the new world of the genome with DNA microarrays. Nat Genet 21:33–37
Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63
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
Park PJ (2009) ChIP–seq: advantages and challenges of a maturing technology. Nat Rev Genet 10(10):669–680
West DB (2001) Introduction to graph theory, vol 2. Prentice Hall, Upper Saddle River
Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4(1)
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
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
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
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
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
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
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
Huynh-Thu VA, Sanguinetti G (2015) Combining tree-based and dynamical systems for the inference of gene regulatory networks. Bioinformatics 31(10):1614–1622
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
Schäfer J, Strimmer K (2004) An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21(6):754–764
Friedman J, Hastie T, Tibshirani R (2008) Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3):432–441
Pearl J (2014) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Francisco
Barber D (2012) Bayesian reasoning and machine learning. Cambridge University Press, Cambridge
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
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
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
Oates CJ, Mukherjee S (2012) Network inference and biological dynamics. Ann Appl Stat 6(3):1209
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
Äijö T, Lähdesmäki H (2009) Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. Bioinformatics 25(22):2937–2944
Grzegorczyk M, Husmeier D (2011) Non-homogeneous dynamic Bayesian networks for continuous data. Mach Learn 83(3):355–419
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
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
Trejo Banos D, Millar AJ, Sanguinetti G (2015) A Bayesian approach for structure learning in oscillating regulatory networks. Bioinformatics 31(22):3617–3624
Dondelinger F, Husmeier D, Rogers S, Filip-pone M (2013) ODE parameter inference using adaptive gradient matching with Gaussian processes. In: Artificial intelligence and statistics, pp 216–228
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
Alon U (2006) An introduction to systems biology: design principles of biological circuits. CRC Press, Boca Raton
Niculescu-Mizil A, Caruana R (2007) Inductive transfer for Bayesian network structure learning. In: Artificial intelligence and statistics, pp 339–346
Chiquet J, Grandvalet Y, Ambroise C (2011) Inferring multiple graphical structures. Stat Comput 21(4):537–553
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
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
Ahmed A, Xing EP (2009) Recovering time-varying networks of dependencies in social and biological studies. Proc Natl Acad Sci 106(29):11878–11883
Robinson JW, Hartemink AJ (2010) Learning non-stationary dynamic Bayesian networks. J Mach Learn Res 11(Dec):3647–3680
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
Thorne T, Stumpf MP (2012) Inference of temporally varying Bayesian networks. Bioinformatics 28(24):3298–3305
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
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
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
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