Advertisement

The Reconstruction and Analysis of Gene Regulatory Networks

  • Guangyong Zheng
  • Tao Huang
Part of the Methods in Molecular Biology book series (MIMB, volume 1754)

Abstract

In post-genomic era, an important task is to explore the function of individual biological molecules (i.e., gene, noncoding RNA, protein, metabolite) and their organization in living cells. For this end, gene regulatory networks (GRNs) are constructed to show relationship between biological molecules, in which the vertices of network denote biological molecules and the edges of network present connection between nodes (Strogatz, Nature 410:268–276, 2001; Bray, Science 301:1864–1865, 2003). Biologists can understand not only the function of biological molecules but also the organization of components of living cells through interpreting the GRNs, since a gene regulatory network is a comprehensively physiological map of living cells and reflects influence of genetic and epigenetic factors (Strogatz, Nature 410:268–276, 2001; Bray, Science 301:1864–1865, 2003). In this paper, we will review the inference methods of GRN reconstruction and analysis approaches of network structure. As a powerful tool for studying complex diseases and biological processes, the applications of the network method in pathway analysis and disease gene identification will be introduced.

Key words

Gene regulatory network Network reconstruction Module detection Pathway analysis Disease gene identification 

References

  1. 1.
    Strogatz SH (2001) Exploring complex networks. Nature 410(6825):268–276. https://doi.org/10.1038/35065725CrossRefPubMedGoogle Scholar
  2. 2.
    Bray D (2003) Molecular networks: the top-down view. Science 301(5641):1864–1865. https://doi.org/10.1126/science.1089118CrossRefPubMedGoogle Scholar
  3. 3.
    Noor A, Serpedin E, Nounou M, Nounou H (2013) Reverse engineering sparse gene regulatory networks using cubature Kalman filter and compressed sensing. Adv Bioinforma 205763. https://doi.org/10.1155/2013/205763
  4. 4.
    Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Muertter RN, Edgar R (2009) NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res 37(Database issue):D885–D890. https://doi.org/10.1093/nar/gkn764CrossRefPubMedGoogle Scholar
  5. 5.
    Zhu Y, Stephens RM, Meltzer PS, Davis SR SRAdb: query and use public next-generation sequencing data from within R. BMC Bioinformatics 14:19. https://doi.org/10.1186/1471-2105-14-19CrossRefGoogle Scholar
  6. 6.
    Petryszak R, Keays M, Tang YA, Fonseca NA, Barrera E, Burdett T, Fullgrabe A, Fuentes AM, Jupp S, Koskinen S, Mannion O, Huerta L, Megy K, Snow C, Williams E, Barzine M, Hastings E, Weisser H, Wright J, Jaiswal P, Huber W, Choudhary J, Parkinson HE, Brazma A Expression Atlas update--an integrated database of gene and protein expression in humans, animals and plants. Nucleic Acids Res 44(D1):D746–D752. https://doi.org/10.1093/nar/gkv1045
  7. 7.
    Zhao W, Serpedin E, Dougherty ER (2008) Inferring connectivity of genetic regulatory networks using information-theoretic criteria. IEEE/ACM Trans Comput Biol Bioinform 5(2):262–274. https://doi.org/10.1109/TCBB.2007.1067CrossRefPubMedGoogle Scholar
  8. 8.
    Noor A, Serpedin E, Nounou M, Nounou H, Mohamed N, Chouchane L (2013) An overview of the statistical methods used for inferring gene regulatory networks and protein-protein interaction networks. Adv Bioinforma:953814. https://doi.org/10.1155/2013/953814
  9. 9.
    Usadel B, Obayashi T, Mutwil M, Giorgi FM, Bassel GW, Tanimoto M, Chow A, Steinhauser D, Persson S, Provart NJ (2009) Co-expression tools for plant biology: opportunities for hypothesis generation and caveats. Plant Cell Environ 32(12):1633–1651. https://doi.org/10.1111/j.1365-3040.2009.02040.xCrossRefPubMedGoogle Scholar
  10. 10.
    Nounou M, Nounou H, Serpedin E, Datta A, Huang Y (2013) Computational and statistical approaches for modeling of proteomic and genomic networks. Adv Bioinforma:561968. https://doi.org/10.1155/2013/561968
  11. 11.
    Ma C, Wang X Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis. 160(1):192–Plant Physiol, 203. https://doi.org/10.1104/pp.112.201962
  12. 12.
    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 Bioinformatics 7(Suppl 1):S7. https://doi.org/10.1186/1471-2105-7-S1-S7CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Zhang X, Zhao XM, He K, Lu L, Cao Y, Liu J, Hao JK, Liu ZP, Chen L Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics 28(1):98–104. https://doi.org/10.1093/bioinformatics/btr626CrossRefGoogle Scholar
  14. 14.
    Zheng G, Xu Y, Zhang X, Liu ZP, Wang Z, Chen L, Zhu XG CMIP: a software package capable of reconstructing genome-wide regulatory networks using gene expression data. BMC Bioinformatics 17(Suppl 17):535. https://doi.org/10.1186/s12859-016-1324-y
  15. 15.
    Zhao J, Zhou Y, Zhang X, Chen L Part mutual information for quantifying direct associations in networks. Proc Natl Acad Sci USA 113(18):5130–5135. https://doi.org/10.1073/pnas.1522586113CrossRefGoogle Scholar
  16. 16.
    Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell's functional organization. Nat Rev Genet 5(2):101–113. https://doi.org/10.1038/nrg1272CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Needham CJ, Bradford JR, Bulpitt AJ, Westhead DR (2006) Inference in Bayesian networks. Nat Biotechnol 24(1):51–53. https://doi.org/10.1038/nbt0106-51CrossRefPubMedGoogle Scholar
  18. 18.
    Cooper GF (1990) The computational complexity of probabilistic inference using Bayesian belief networks. Artif Intell 42:393–405CrossRefGoogle Scholar
  19. 19.
    Pedro Larranag HK, Bielza C, Santana R (2013) A review on evolutionary algorithms in Bayesian network learning and inference tasks. Inf Sci 233:109–125CrossRefGoogle Scholar
  20. 20.
    Friedman N (2004) Inferring cellular networks using probabilistic graphical models. Science 303(5659):799–805. https://doi.org/10.1126/science.1094068CrossRefPubMedGoogle Scholar
  21. 21.
    Menendez P, Kourmpetis YA, ter Braak CJ, van Eeuwijk FA Gene regulatory networks from multifactorial perturbations using graphical Lasso: application to the DREAM4 challenge. PLoS One 5(12):e14147. https://doi.org/10.1371/journal.pone.0014147CrossRefGoogle Scholar
  22. 22.
    Kramer N, Schafer J, Boulesteix AL (2009) Regularized estimation of large-scale gene association networks using graphical Gaussian models. BMC Bioinformatics 10:384. https://doi.org/10.1186/1471-2105-10-384CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D, Stolovitzky G Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci USA 107(14):6286–6291. https://doi.org/10.1073/pnas.0913357107CrossRefGoogle Scholar
  24. 24.
    Noor A, Serpedin E, Nounou M, Nounou HN Inferring gene regulatory networks via nonlinear state-space models and exploiting sparsity. IEEE/ACM Trans Comput Biol Bioinform 9(4):1203–1211. https://doi.org/10.1109/TCBB.2012.32CrossRefGoogle Scholar
  25. 25.
    Wang Z, Yang F, Ho DW, Swift S, Tucker A, Liu X (2008) Stochastic dynamic modeling of short gene expression time-series data. IEEE Trans Nanobioscience 7(1):44–55. https://doi.org/10.1109/TNB.2008.2000149CrossRefPubMedGoogle Scholar
  26. 26.
    Koh C, Wu F-X, Selvaraj G, Kusalik AJ (2009) Using a State-Space Model and Location Analysis to Infer Time-Delayed Regulatory Networks. EURASIP Journal on Bioinformatics and Systems Biology 2009(1):484601Google Scholar
  27. 27.
    Califano A, Butte AJ, Friend S, Ideker T, Schadt E Leveraging models of cell regulation and GWAS data in integrative network-based association studies. Nat Genet 44(8):841–847. https://doi.org/10.1038/ng.2355CrossRefGoogle Scholar
  28. 28.
    Marbach D, Costello JC, Kuffner R, Vega NM, Prill RJ, Camacho DM, Allison KR, Kellis M, Collins JJ, Stolovitzky G Wisdom of crowds for robust gene network inference. Nat Methods 9(8):796–804. https://doi.org/10.1038/nmeth.2016CrossRefGoogle Scholar
  29. 29.
    Ravasz E, Barabasi AL (2003) Hierarchical organization in complex networks. Phys Rev E Stat Nonlinear Soft Matter Phys 67(2 Pt 2):026112. https://doi.org/10.1103/PhysRevE.67.026112CrossRefGoogle Scholar
  30. 30.
    Barrat A, Barthelemy M, Pastor-Satorras R, Vespignani A (2004) The architecture of complex weighted networks. Proc Natl Acad Sci USA 101(11):3747–3752. https://doi.org/10.1073/pnas.0400087101CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Kashtan N, Itzkovitz S, Milo R, Alon U (2004) Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20(11):1746–1758. https://doi.org/10.1093/bioinformatics/bth163CrossRefPubMedGoogle Scholar
  32. 32.
    Karlebach G, Shamir R (2008) Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 9(10):770–780. https://doi.org/10.1038/nrm2503CrossRefPubMedGoogle Scholar
  33. 33.
    Enright AJ, Van Dongen S, Ouzounis CA (2002) An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res 30(7):1575–1584CrossRefGoogle Scholar
  34. 34.
    Rivera CG, Vakil R, Bader JS NeMo: Network Module identification in Cytoscape. BMC Bioinformatics 11(Suppl 1):S61. https://doi.org/10.1186/1471-2105-11-S1-S61
  35. 35.
    Rhrissorrakrai K, Gunsalus KCMINE Module identification in networks. BMC Bioinformatics 12:192. https://doi.org/10.1186/1471-2105-12-192
  36. 36.
    Shen-Orr SS, Milo R, Mangan S, Alon U (2002) Network motifs in the transcriptional regulation network of Escherichia Coli. Nat Genet 31(1):64–68. https://doi.org/10.1038/ng881CrossRefPubMedGoogle Scholar
  37. 37.
    Wernicke S, Rasche F (2006) FANMOD: a tool for fast network motif detection. Bioinformatics 22(9):1152–1153. https://doi.org/10.1093/bioinformatics/btl038CrossRefPubMedGoogle Scholar
  38. 38.
    Li X, Stones DS, Wang H, Deng H, Liu X, Wang G NetMODE: network motif detection without Nauty. PLoS One 7(12):e50093. https://doi.org/10.1371/journal.pone.0050093CrossRefGoogle Scholar
  39. 39.
    Li Y, Pearl SA, Jackson SA Gene networks in plant biology: approaches in reconstruction and analysis. Trends Plant Sci 20(10):664–675. https://doi.org/10.1016/j.tplants.2015.06.013CrossRefGoogle Scholar
  40. 40.
    Lynch M (2007) The evolution of genetic networks by non-adaptive processes. Nat Rev Genet 8(10):803–813. https://doi.org/10.1038/nrg2192CrossRefPubMedGoogle Scholar
  41. 41.
    Crombach A, Hogeweg P (2008) Evolution of evolvability in gene regulatory networks. PLoS Comput Biol 4(7):e1000112. https://doi.org/10.1371/journal.pcbi.1000112CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Movahedi S, Van de Peer Y, Vandepoele K Comparative network analysis reveals that tissue specificity and gene function are important factors influencing the mode of expression evolution in Arabidopsis and rice. Plant Physiol 156(3):1316–1330. https://doi.org/10.1104/pp.111.177865CrossRefGoogle Scholar
  43. 43.
    Oliver S (2000) Guilt-by-association goes global. Nature 403(6770):601–603. https://doi.org/10.1038/35001165CrossRefPubMedGoogle Scholar
  44. 44.
    Barabasi AL, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12(1):56–68. https://doi.org/10.1038/nrg2918CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Schwikowski B, Uetz P, Fields S (2000) A network of protein-protein interactions in yeast. Nat Biotechnol 18(12):1257–1261. https://doi.org/10.1038/82360CrossRefPubMedGoogle Scholar
  46. 46.
    Macropol K, Can T, Singh AK (2009) RRW: repeated random walks on genome-scale protein networks for local cluster discovery. BMC bioinformatics 10:283. https://doi.org/10.1186/1471-2105-10-283CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Li Y, Patra JC (2010) Genome-wide inferring gene-phenotype relationship by walking on the heterogeneous network. Bioinformatics 26(9):1219–1224. https://doi.org/10.1093/bioinformatics/btq108CrossRefPubMedGoogle Scholar
  48. 48.
    Kohler S, Bauer S, Horn D, Robinson PN (2008) Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet 82(4):949–958. https://doi.org/10.1016/j.ajhg.2008.02.013CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Jiang R, Gan M, He P (2011) Constructing a gene semantic similarity network for the inference of disease genes. BMC Syst Biol 5(Suppl 2):S2. https://doi.org/10.1186/1752-0509-5-S2-S2CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Chen X, Liu MX, Yan GY (2012) Drug-target interaction prediction by random walk on the heterogeneous network. Mol BioSyst 8(7):1970–1978. https://doi.org/10.1039/c2mb00002dCrossRefPubMedGoogle Scholar
  51. 51.
    Shi H, Xu J, Zhang G, Xu L, Li C, Wang L, Zhao Z, Jiang W, Guo Z, Li X (2013) Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes. BMC Syst Biol 7:101. https://doi.org/10.1186/1752-0509-7-101CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Huang T, Liu C-L, Li L-L, Cai M-H, Chen W-Z, Y-F X, O’Reilly PF, Cai L, He L (2016) A new method for identifying causal genes of schizophrenia and anti-tuberculosis drug-induced hepatotoxicity. Sci Rep 6:32571. https://doi.org/10.1038/srep32571CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Chen L, Yang J, Xing Z, Yuan F, Shu Y, Zhang Y, Kong X, Huang T, Li H, Cai Y-D (2017) An integrated method for the identification of novel genes related to oral cancer. PLoS One 12(4):e0175185CrossRefGoogle Scholar
  54. 54.
    Chen L, Chu C, Kong X, Huang G, Huang T, Cai YD (2015) A hybrid computational method for the discovery of novel reproduction-related genes. PLoS One 10(3):e0117090. https://doi.org/10.1371/journal.pone.0117090CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Lee I, Blom UM, Wang PI, Shim JE, Marcotte EM (2011) Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res 21(7):1109–1121. https://doi.org/10.1101/gr.118992.110CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Managbanag JR, Witten TM, Bonchev D, Fox LA, Tsuchiya M, Kennedy BK, Kaeberlein M (2008) Shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity. PLoS One 3(11):e3802. https://doi.org/10.1371/journal.pone.0003802CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Zhang J, Jiang M, Yuan F, Feng KY, Cai YD, Xu X, Chen L (2013) Identification of age-related macular degeneration related genes by applying shortest path algorithm in protein-protein interaction network. Biomed Res Int 2013:523415PubMedPubMedCentralGoogle Scholar
  58. 58.
    Li B-Q, You J, Chen L, Zhang J, Zhang N, Li H-P, Huang T, Kong X-Y, Cai Y-D (2013) Identification of lung-cancer-related genes with the shortest path approach in a protein-protein interaction network. Biomed Res Int 2013:267375. https://doi.org/10.1155/2013/267375CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Jiang M, Chen Y, Zhang Y, Chen L, Zhang N, Huang T, Cai Y-D, Kong X (2013) Identification of hepatocellular carcinoma related genes with k-th shortest paths in a protein–protein interaction network. Mol BioSyst 9(11):2720–2728CrossRefGoogle Scholar
  60. 60.
    Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1:269–271CrossRefGoogle Scholar
  61. 61.
    Chartrand G, Oellermann OR (1992) Applied and algorithmic graph theory. Mcgraw-Hill College, Pennsylvania NYGoogle Scholar
  62. 62.
    Cormen TH, Leiserson CE, R RL, Stein C (2001) Introduction to algorithms, second edn. MIT press and Mcgraw-Hill, Cambridge MAGoogle Scholar
  63. 63.
    Hart PENN, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4:100–107CrossRefGoogle Scholar
  64. 64.
    EW D (1959) A note on two problems in connection with graphs. Numer Math 1:269–271CrossRefGoogle Scholar
  65. 65.
    Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A (2005) Reverse engineering of regulatory networks in human B cells. Nat Genet 37(4):382–390. https://doi.org/10.1038/ng1532CrossRefPubMedGoogle Scholar
  66. 66.
    Butte AJ, Kohane IS (2000) Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput:418–429Google Scholar
  67. 67.
    Scutari M (2010) Learning Bayesian networks with the bnlearn R package. J Stat Softw 35(3):1–22CrossRefGoogle Scholar
  68. 68.
    Huang T, Yang J, Cai Y-D (2015) Novel candidate key drivers in the integrative network of genes, MicroRNAs, methylations, and copy number variations in squamous cell lung carcinoma. Biomed Res Int 2015:358125. https://doi.org/10.1155/2015/358125CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Huang T, Liu L, Qian Z, Tu K, Li Y, Xie L (2010) Using GeneReg to construct time delay gene regulatory networks. BMC Res Notes 3(1):142. https://doi.org/10.1186/1756-0500-3-142CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C, Jensen LJ (2013) STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 41(Database issue):D808–D815. https://doi.org/10.1093/nar/gks1094CrossRefPubMedGoogle Scholar
  71. 71.
    Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M (1999) KEGG: Kyoto Encyclopedia of genes and genomes. Nucleic Acids Res 27(1):29–34CrossRefGoogle Scholar
  72. 72.
    Kamburov A, Wierling C, Lehrach H, Herwig R (2009) ConsensusPathDB--a database for integrating human functional interaction networks. Nucleic Acids Res 37(Database issue):D623–D628. https://doi.org/10.1093/nar/gkn698CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Guangyong Zheng
    • 1
  • Tao Huang
    • 2
  1. 1.Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
  2. 2.Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina

Personalised recommendations