Biophysical Reviews

, Volume 3, Issue 1, pp 1–13 | Cite as

Network modelling of gene regulation

  • Joshua W. K. HoEmail author
  • Michael A. Charleston


Gene regulatory network (GRN) modelling has gained increasing attention in the past decade. Many computational modelling techniques have been proposed to facilitate the inference and analysis of GRN. However, there is often confusion about the aim of GRN modelling, and how a gene network model can be fully utilised as a tool for systems biology. The aim of the present article is to provide an overview of this rapidly expanding subject. In particular, we review some fundamental concepts of systems biology and discuss the role of network modelling in understanding complex biological systems. Several commonly used network modelling paradigms are surveyed with emphasis on their practical use in systems biology research.


Gene regulatory network Systems biology Bioinformatics 



We thank Professor Cristobal dos Remedios for critically revising an earlier draft of this manuscript and for his valuable comments on this work.


  1. Ahn AC, Tewari M, Poon CS, Phillips RS (2006) The clinical applications of a systems approach. PLoS Med 3:e209PubMedCrossRefGoogle Scholar
  2. Akutsu T, Miyano S, Kuhara S (2000) Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics 16:727–734PubMedCrossRefGoogle Scholar
  3. Aldana M (2003) Boolean dynamics of networks with scale-free topology. Phys Nonlinear Phenom 185:45–66CrossRefGoogle Scholar
  4. Aldana M, Balleza E, Kauffman S, Resendiz O (2007) Robustness and evolvability in genetic regulatory networks. J Theor Biol 245:433–448PubMedCrossRefGoogle Scholar
  5. Alon U (2007) Network motifs: theory and experimental approaches. Nat Rev Genet 8:450–461PubMedCrossRefGoogle Scholar
  6. Astbury WT (1961) Molecular biology or ultrastructural biology. Nature 190:1124PubMedCrossRefGoogle Scholar
  7. Balaji S, lyer LM, Aravind L, Babu MM (2006) Uncovering a hidden distributed architecture behind scale-free transcriptional regulatory networks. J Mol Biol 360:204–212PubMedCrossRefGoogle Scholar
  8. Barabási AL (2007) Network medicine—from obesity to the “diseasome”. N Engl J Med 357:404–407PubMedCrossRefGoogle Scholar
  9. Barash Y, Calarco JA, Gao W, Pan Q, Wang X, Shai O, Blencowe BJ, Frey BJ (2010) Deciphering the splicing code. Nature 465:53–59PubMedCrossRefGoogle Scholar
  10. Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136:215–233PubMedCrossRefGoogle Scholar
  11. 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 37:382–390PubMedCrossRefGoogle Scholar
  12. Bergmann FT, Sauro HM (2008) Comparing simulation results of SBML capable simulators. Bioinformatics 24:1963–1965PubMedCrossRefGoogle Scholar
  13. Bornholdt S (2005) Less is more in modeling large genetic networks. Science 310:449–450PubMedCrossRefGoogle Scholar
  14. Box GEP, Draper NR (1986) Empirical model-building and response surface. John Wiley and Sons, IncGoogle Scholar
  15. Brazhnik P, de la Fuente A, Mendes P (2002) Gene networks: how to put the function in genomics. Trends Biotechnol 20:467–472PubMedCrossRefGoogle Scholar
  16. Carter SL, Brechbuhler CM, Griffin M, Bond AT (2004) Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics 20:2242–2250PubMedCrossRefGoogle Scholar
  17. Chen T, He HL, Church GM (1999) Modeling gene expression with differential equations. Pac Symp Biocomput 4:29–44Google Scholar
  18. Chen TY, Ho JWK, Liu H, Xie X (2009) An innovative approach for testing bioinformatics programs using metamorphic testing. BMC Bioinformatics 10:24PubMedCrossRefGoogle Scholar
  19. Choi JK, Yu U, Yoo OJ, Kim S (2005) Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics 21:4348–4355PubMedCrossRefGoogle Scholar
  20. Conant GC, Wagner A (2003) Convergent evolution of gene circuits. Nat Genet 34:264–266PubMedCrossRefGoogle Scholar
  21. de Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 9:67–103PubMedCrossRefGoogle Scholar
  22. de la Fuente A (2010) From ‘differential expression’ to ‘differential networking’—identification of dysfunctional regulatory networks in diseases. Trends Gent 26:326–333CrossRefGoogle Scholar
  23. de la Fuente A, Brazhnik P, Mendes P (2002) Linking the genes: Inferring quantitative gene networks from microarray data. Trends Genet 18:395–398PubMedCrossRefGoogle Scholar
  24. de la Fuente A, Bing N, Hoeschele I, Mendes P (2004) Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20:3565–3574PubMedCrossRefGoogle Scholar
  25. D’haeseleer P, Wen X, Fuhrman S, Somogyi R (1999) Linear modeling of mRNA expression levels during CNS development and injury. Pac Sym Biocomput 4:41–52Google Scholar
  26. Dojer N, Gambin A, Mizera A, Wilczynski B, Tiuryn J (2006) Applying dynamic Bayesian networks to purturbed gene expression data. BMC Bioinformatics 7:249PubMedCrossRefGoogle Scholar
  27. Endy D, Brent R (2001) Modelling cellular behaviour. Nature 409:391–395PubMedCrossRefGoogle Scholar
  28. Evans TW, Gillespie CS, Wilkinson DJ (2008) The SBML discrete stochastic models test suite. Bioinformatics 24:285–286PubMedCrossRefGoogle Scholar
  29. Franke L, van Bakel H, Fokkens L, de Jong ED, Egmont-Petersen M, Wijmenga C (2006) Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am J Hum Genet 78:1011–1025PubMedCrossRefGoogle Scholar
  30. Friedman N (2004) Inferring cellular networks using probabilistic graphical models. Science 303:799–805PubMedCrossRefGoogle Scholar
  31. Friedman N, Linial M, Nachman I, Pe’er D (2000) Using bayesian networks to analyze expression data. J Comput Biol 7:601–620PubMedCrossRefGoogle Scholar
  32. Gardner TS, di Bernardo D, Lorenz D, Collins JJ (2003) Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301:102–105PubMedCrossRefGoogle Scholar
  33. Gat-Viks I, Tanay A, Raijman D, Shamir R (2006) A probabilistic methodology for integrating knowledge and experiments on biological networks. J Comput Biol 13:165–181PubMedCrossRefGoogle Scholar
  34. Glass L, Kauffman SA (1973) The logical analysis of continuous, non-linear biochemical control networks. J Theor Biol 39:103–129PubMedCrossRefGoogle Scholar
  35. Goss PJ, Peccoud J (1998) Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets. Proc Natl Acad Sci USA 95:6750–6755PubMedCrossRefGoogle Scholar
  36. Guelzim N, Bottani S, Bourgine P, Képès F (2002) Topological and causal structure of the yeast transcriptional regulatory network. Nat Genet 31:60–63PubMedCrossRefGoogle Scholar
  37. Hawkins RD, Hon GC, Ren B (2010) Next-generation genomics: an integrative approach. Nat Rev Genet 11:476–486PubMedGoogle Scholar
  38. Helikar T, Konvalina J, Heidel J, Rogers JA (2008) Emergent decision-making in biological signal transduction networks. Proc Natl Acad Sci USA 105:1913–1918PubMedCrossRefGoogle Scholar
  39. Heymans M, Singh AK (2003) Deriving phylogenetic trees from the similarity analysis of metabolic pathways. Bioinformatics 19:i138–i146PubMedCrossRefGoogle Scholar
  40. Hill AV (1910) The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves. J Physiol 40:iv–viiGoogle Scholar
  41. Ho JWK, Charleston MA (2007) Modeling the evolution of gene regulatory networks. In: Proceedings of the 8th international conference on systems biology (ICSB’07), p 44Google Scholar
  42. Ho JWK, Koundinya R, Caetano T, dos Remedios CG, Charleston MA (2008a) Inferring differential leukocyte activity from antibody microarrays using a latent variable model. Genome Inform 21:126–137PubMedCrossRefGoogle Scholar
  43. Ho JWK, Stefani M, dos Remedios CG, Charleston MA (2008b) Differential variability analysis of gene expression and its application to human diseases. Bioinformatics 24:i390–i398Google Scholar
  44. Hofmeyr JHS, Cornish-Bowden A (1997) The reversible Hill equation: how to incorporate cooperative enzymes into metabolic models. Comput Appl Biosci 13:377–385PubMedGoogle Scholar
  45. Huang S (2004) Back to the biology in systems biology: what can we learn from biomolecular networks? Brief Funct Genomic Proteomic 2:279–297PubMedCrossRefGoogle Scholar
  46. Huang S (2010) Cell lineage determination in state space: a systems view brings flexibility to dogmatic canonical rules. PLoS Biol 8:e1000380PubMedCrossRefGoogle Scholar
  47. Huang S, Ernberg I, Kauffman S (2009) Cancer attractors: a systems view of tumors from a gene network dynamics and developmental perspective. Semin Cell Dev Biol 20:869–876PubMedCrossRefGoogle Scholar
  48. Husmeier D (2003) Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic bayesian networks. Bioinformatics 19:2271–2282PubMedCrossRefGoogle Scholar
  49. Jiang C, Pugh BF (2009) Nucleosome positioning and gene regulation: advances through genomics. Nat Rev Genet 10: 161–172PubMedCrossRefGoogle Scholar
  50. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucl Acids Res 28:27–30PubMedCrossRefGoogle Scholar
  51. Karlebach G, Shamir R (2008) Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 9:770–780PubMedCrossRefGoogle Scholar
  52. Kauffman S (1969) Metabolic stability and epigenesis in randomly constructed gene nets. J Theor Biol 44:167–190CrossRefGoogle Scholar
  53. Kelly D, Sanders R (2008) Assessing the quality of scientific software. In: Proceedings of the 1st international workshop on software engineering for computational science and engineeringGoogle Scholar
  54. Kelley BP, Sharan R, Karp RM, Sittler T, Root DE, Stockwell BR, Ideker T (2003) Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proc Natl Acad Sci USA 100:11,394–11,399CrossRefGoogle Scholar
  55. Kim JR, Yoon Y, Cho KH (2008) Coupled feedback loops form dynamic motifs of cellular networks. Biophys J 94:359–365PubMedCrossRefGoogle Scholar
  56. Kitano H (2007a) A robustness-based approach to systems-oriented drug design. Nat Rev Drug Design 6:202–210CrossRefGoogle Scholar
  57. Kitano H (2007b) Towards a theory of biological robustness. Mol Syst Biol 3:137PubMedCrossRefGoogle Scholar
  58. Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. The MIT Press, CambridgeGoogle Scholar
  59. Küffner R, Petri T, Windhager L, Zimmer R (2010) Petri nets with fuzzy logic (pnfl): reverse engineering and parametrization. PLoS One 5:e12807PubMedCrossRefGoogle Scholar
  60. Kwon YK, Cho KH (2008) Quantitative analysis of robustness and fragility in biological networks based on feedback dynamics. Bioinformatics 24:987–994PubMedCrossRefGoogle Scholar
  61. Laird PW (2010) Principles and challenges of genome-wide DNA methylation analysis. Nat Rev Genet 11:191–203PubMedCrossRefGoogle Scholar
  62. Lander A (2010) The edges of understanding. BMC Biology 8:40PubMedGoogle Scholar
  63. Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, Hannett NM, Harbison CT, Thompson CM, Simon I, Zeitlinger J, Jennings EG, Murray HL, Gordon B, Ren B, Wyrick JJ, Tagne JB, Volkert TL, Fraenkel E, Gifford DK, Young RA (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298:799–804PubMedCrossRefGoogle Scholar
  64. Liang S, Fuhrmann S, Somogyi R (1998) REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. Pac Symp Biocomput 3:18–29Google Scholar
  65. Liang Z, Xu M, Teng M, Niu L (2006) Comparison of protein interaction networks reveals species conservation and divergence. BMC Bioinformatics 7:457PubMedCrossRefGoogle Scholar
  66. Luscombe NM, Babu MM, Yu H, Snyder M, Teichmann SA, Gerstein M (2004) Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431: 308–312PubMedCrossRefGoogle Scholar
  67. Ma HW, Kumar B, Ditges U, Gunzer F, Buer J, Zeng AP (2004) An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs. Nucl Acids Res 32:6643–6649PubMedCrossRefGoogle Scholar
  68. Martin S, Zhang Z, Martino A, Faulon JL (2007) Boolean dynamics of genetic regulatory networks inferred from microarray time series data. Bioinformatics 23:866–874PubMedCrossRefGoogle Scholar
  69. Matsuno H, Doi A, Nagasaki M, Miyano S (2000) Hybrid petri net representation of gene regulatory network. Pac Symp Biocompt 5:338–349Google Scholar
  70. Mendell JT, Sharifi NA, Meyers JL, Martinez-Murillo F, Dietz HC (2004) Nonsense surveillance regulates expression of diverse classes of mammalian transcripts and mutes genomic noise. Nat Genet 36:1073–1078PubMedCrossRefGoogle Scholar
  71. Mendes P, Sha W, Ye K (2003) Artificial gene networks for objective comparison of analysis algorithms. Bioinformatics 19:ii122–ii129PubMedCrossRefGoogle Scholar
  72. Metzker ML (2010) Sequencing technologies—the next generation. Nat Rev, Genet 11:31–46CrossRefGoogle Scholar
  73. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovshii D, Alon U (2002) Network motifs: simple building blocks of complex networks. Science 298:824–827PubMedCrossRefGoogle Scholar
  74. Nagasaki M, Yamaguchi R, Yoshida R, Imoto S, Doi A, Tamada Y, Matsuno H, Miyano S, Higuchi T (2006) Genomic data assimilation for estimating hybrid functional Petri net from time-course gene expression data. Genome Inform 17: 46–61PubMedGoogle Scholar
  75. Newman M, Barabási AL, Watts DJ (2006) The structure and dynamics of networks. Princeton University Press, Princeton, NJGoogle Scholar
  76. Noble D (2002) The rise of computational biology. Nat Rev Mol Cell Biol 3:459–463PubMedCrossRefGoogle Scholar
  77. Noble D (2008) Genes and causation. Phil Trans R Soc A 366:3001–3015PubMedCrossRefGoogle Scholar
  78. Ogata H, Fujibuchi W, Goto S, Kanehisa M (2000) A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters. Nucl Acids Res 28:4021–4028PubMedCrossRefGoogle Scholar
  79. Park PJ (2009) ChIP-seq: advantages and challenges of a maturing technology. Nat Rev Genet 10:669–680PubMedCrossRefGoogle Scholar
  80. Pearl J (1988) Probabilistic reasoning in intelligent systems. Morgan Kaufmann, MassachusettsGoogle Scholar
  81. Pearl J (2000) Causality: models, reasoning, and inference. Cambridge University Press, CambridgeGoogle Scholar
  82. Pe’er D (2005) Bayesian network analysis of signaling networks: a primer. Sci STKE 2005:l4Google Scholar
  83. Petri CA (1962) Kommunikation mit automaten. Ph.D. thesis, Institut für Instrumentelle Mathematik, BonnGoogle Scholar
  84. Pinney J, Westhead D, McConkey G (2003) Petri net representations in systems biology. Biochem Soc Trans 31:1513–1515PubMedCrossRefGoogle Scholar
  85. Quackenbush J (2003) Microarrays—guilt by association. Science 302:240–241PubMedCrossRefGoogle Scholar
  86. Reisig W (1985) Petri nets: an introduction. Monographs on Theoretical Computater Science. Springer, BerlinGoogle Scholar
  87. Reisig W, Rozenberg G (eds) (1998) Lectures on Petri nets I: basic models. Lecture notes in computer science. Springer, BerlinGoogle Scholar
  88. Rodriguez-Caso C, Medina MA, Solé RV (2005) Topology, tinkering and evolution of the human transcription factor network. FEBS J 272:6423–6434PubMedCrossRefGoogle Scholar
  89. Sachs K, Perez O, Pe’er D, Lauffenburger DA, Nolan GP (2005) Causal protein-signaling networks derived from multiparameter single-cell data. Science 308:523–529PubMedCrossRefGoogle Scholar
  90. Salgado H, Gamma-Castro S, Peralta-Gil M, Díaz-Peredo E, Sánchez-Solano F, Santo-Zavaleta A, Martínez-Flores I, Jiménez-Jacinto V, Bonavides-Martinez C, Segura-Salazar J, Martínez-Antonio A, Collado-Vides J (2006) RegulonDB (version 5.0): Escherichia coli k-12 transcriptional regulatory network, operon organization, and growth conditions. Nucl Acids Res 34:D394–D397CrossRefGoogle Scholar
  91. Sandelin A, Alkema W, Engstrom P, Wasserman WW, Lenhard B (2004) JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucl Acids Res 32:D91–D94PubMedCrossRefGoogle Scholar
  92. Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH (2009) PID: the pathway interaction database. Nucl Acids Res 37:D674–D679PubMedCrossRefGoogle Scholar
  93. Schwartz R (2008) Biological modeling and simulation. The MIT Press, CambridgeGoogle Scholar
  94. Segal E, Wang H, Koller D (2003) Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics 19:i264–i272PubMedCrossRefGoogle Scholar
  95. Sharan R, Ideker T (2006) Modeling cellular machinery through biological network comparison. Nat Biotechnol 24:427–433PubMedCrossRefGoogle Scholar
  96. Shen-Orr SS, Milo R, Mangan S, Alon U (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31:64–68PubMedCrossRefGoogle Scholar
  97. Smith VA, Jarvis ED, Hartemink AJ (2003) Influence of network topology and data collection on network inference. Pac Symp Biocomput 8:164–175Google Scholar
  98. Steggles LJ, Banks R, Shaw O, Wipat A (2007) Qualitatively modelling and analysing genetic regulatory networks: a Patri net approach. Bioinformatics 23:336–343PubMedCrossRefGoogle Scholar
  99. Stolovitzky G, Monroe D, Califano A (2007) Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference. Ann N Y Acad Sci 1115:1–22PubMedCrossRefGoogle Scholar
  100. Strahl BD, Allis CD (2000) The language of covalent histone modifications. Nature 403:41–45PubMedCrossRefGoogle Scholar
  101. Strogatz SH (2001) Exploring complex networks. Nature 410:268–276PubMedCrossRefGoogle Scholar
  102. Sutherland H, Bickmore WA (2009) Transcription factories: gene expression in unions? Nat Rev Genet 10:457–466PubMedCrossRefGoogle Scholar
  103. Suzuki MM, Bird A (2008) DNA methylation landscapes: provocative insights from epigenomics. Nat Rev Genet 9:465–476PubMedCrossRefGoogle Scholar
  104. Szallasi Z, Stelling J, Periwal V (eds) (2006) System modeling in cell biology: from concept to nuts and bolts. The MIT Press, CambridgeGoogle Scholar
  105. Tong AHY, Lesage G, Bader G, Ding H, Xu H, Xin X, Young J, Berriz GF, Brost RL, Chang M, Chen YQ, Cheng X, Chua G, Friesen H, Goldberg DS, Haynes J, Humphries C, He G, Hussein S, Ke L, Krogan N, Li Z, Levinson JN, Lu H, Menard P, Munyana C, Parsons A, Ryan O, Tonikian R, Roberts T, Sdicu AM, Shapiro J, Sheikh B, Suter B, Wong SL, Zhang LV, Zhu H, Burd CG, Munro S, Sander C, Rine J, Greenblatt J, Roth FP, Brown GW, Andrews B, Bussey H, Boone C (2004) Global mapping of the yeast genetic interaction network. Science 303:808–813PubMedCrossRefGoogle Scholar
  106. Trusina A, Sneppen K, Dodd IB, Shearwin KE, Egan JB (2005) Functional alignment of regulatory networks: a study of temperate phages. PLoS Comput Biol 1:e74PubMedCrossRefGoogle Scholar
  107. Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, Zhu J, Haussler D, Stuart JM (2010) Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26:i237–i245PubMedCrossRefGoogle Scholar
  108. Wagner A (2000) Robustness against mutations in genetic networks of yeast. Nat Genet 24:355–361PubMedCrossRefGoogle Scholar
  109. Wagner A (2003) How the global structure of protein interaction networks evolves. Proc R Soc Lond B 270:457–466CrossRefGoogle Scholar
  110. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63PubMedCrossRefGoogle Scholar
  111. Wolfe CJ, Kohane IS, Butte AJ (2005) Systematic survey reveals general applicability of “guilt-by-association” within gene coexpression networks. BMC Bioinformatics 6:227PubMedCrossRefGoogle Scholar
  112. Xie X, Ho JWK, Murphy C, Kaiser G, Xu B, Chen TY (2009) Application of metamorphic testing to supervised classifiers. In: Proceedings of the 9th international conference on quality software (QSIC’09). pp. 135–144Google Scholar
  113. Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4:17Google Scholar
  114. Zou M, Conzen SD (2005) A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 21: 71–79PubMedCrossRefGoogle Scholar

Copyright information

© International Union for Pure and Applied Biophysics (IUPAB) and Springer 2010

Authors and Affiliations

  1. 1.School of Information TechnologiesThe University of SydneySydneyAustralia
  2. 2.Centre for Mathematical BiologyThe University of SydneySydneyAustralia

Personalised recommendations