Evolutionary Intelligence

, Volume 7, Issue 1, pp 29–47 | Cite as

Reconstructing biological gene regulatory networks: where optimization meets big data

Review Article
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Abstract

The importance of ‘big data’ in biology is increasing as vast quantities of data are being produced from high-throughput experiments. Techniques such as DNA microarrays are providing a genome-wide picture of gene expression levels, allowing us to investigate the structure and interactions of gene networks in biological systems. Inference of gene regulatory network (GRN) is an underdetermined problem suited to Metaheuristic algorithms which can operate on limited information. Thus GRN inference offers a platform for investigations into data intensive sciences and large scale optimization problems. Here we examine the link between data intensive research and optimization problems for the reverse engineering of GRNs. Briefly, we detail the benefit of the data deluge and the study of ALife for modelling GRNs as well as their reconstruction. We discuss how metaheuristics can solve big data problems and the inference of GRNs offer real world problems for both areas of research. We overview some current reconstruction algorithms and investigate some modelling and computational limits of the inference processes and suggest some areas for development. Furthermore we identify links and synergies between optimization and big data, e.g., dynamic, uncertain and large scale optimization problems, and discuss the potential benefit of multi- and many-objective optimization. We stress the importance of data integration techniques in order to maximize the data available, particularly for the case of inferring GRNs from microarray data. Such multi-disciplinary research is vital as biology is rapidly becoming a quantitative, data intensive science.

Keywords

Metaheuristics Evolutionary algorithms Data-driven optimization Gene regulatory network reconstruction Big data Data science 
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Notes

Acknowledgments

This work was funded by an Engineering and Physics Science Research Council (EPSRC) Doctoral Trainning Centre (DTC) studentship at the University of Surrey.

References

  1. 1.
    Äijö T, Lähdesmäki H (2009) Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. Bioinformatics 25(22):2937–2944CrossRefGoogle Scholar
  2. 2.
    Akutsu T, Hayashida M, Tamura T (2008) Algorithms for inference, analysis and control of boolean networks. In: Horimoto K, Regensburger G, Rosenkranz M, Yoshida H (eds) Algebraic biology. Lecture Notes in computer science, vol 5147. Springer, Berlin, pp 1–15Google Scholar
  3. 3.
    Albert R, Jeong H, Barabasi AL (2000) Error and attack tolerance of complex networks. Nature 406:378–382Google Scholar
  4. 4.
    Allenby NEE, Laing E, Bucca G, Kierzek AM, Smith CP (2012) Diverse control of metabolism and other cellular processes in streptomyces coelicolor by the phop transcription factor: genome-wide identification of in vivo targets. Nucleic Acids Res 40(19):9543–9556Google Scholar
  5. 5.
    Alon U (2006) An introduction to systems biology: design principles of biological circuits. Chapman & Hall/CRCGoogle Scholar
  6. 6.
    Alon U (2007) Network motifs: theory and experimental approaches. Nat Rev Genet 8:450–461CrossRefGoogle Scholar
  7. 7.
    Alvarez-benitez JE, Everson RM, Fieldsend JE (2005) A mopso algorithm based exclusively on pareto dominance concepts. In: Proceedings of the third international conference on evolutionary multicriterion optimization, EMO 2005, Springer, Berlin, pp 459–473Google Scholar
  8. 8.
    Ando S, Iba H (2001) Inference of gene regulatory model by genetic algorithms. In: Proceedings of the 2001 congress on evolutionary computation, 2001, vol 1, pp 712–719Google Scholar
  9. 9.
    Babu MM, Luscombe NM, Aravind L, Gerstein M, Teichmann SA (2004) Structure and evolution of transcriptional regulatory networks. Curr Opin Struct Biol 14(3):283–291CrossRefGoogle Scholar
  10. 10.
    Bandaru S, Deb K (2011) Automated innovization for simultaneous discovery of multiple rules in bi-objective problems. In: Takahashi R, Deb K, Wanner E, Greco S (eds) Evolutionary multi-criterion optimization. Lecture Notes in computer science, vol 6576, Springer, Berlin, pp 1–15Google Scholar
  11. 11.
    Bandaru S, Deb K (2011) Towards automating the discovery of certain innovative design principles through a clustering-based optimization technique. Eng Optim 43(9):911–941CrossRefGoogle Scholar
  12. 12.
    Bandaru S, Deb K (2013) A dimensionally-aware genetic programming architecture for automated innovization. In: Purshouse R, Fleming P, Fonseca C, Greco S, Shaw J (eds) Evolutionary multi-criterion optimization. Lecture Notes in computer science, vol 7811, Springer, Berlin, pp 513–527Google Scholar
  13. 13.
    Bansal M, Belcastro V, Ambesi-Impiombato A, di Bernardo D (2007) How to infer gene networks from expression profiles. Mol Syst Biol 3(78):1–10Google Scholar
  14. 14.
    Bansal M, Gatta GD, di Bernardo D (2006) Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics 22(7):815–822CrossRefGoogle Scholar
  15. 15.
    Beal MJ, Falciani F, Ghahramani Z, Rangel C, Wild DL (2005) A bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics 21(3):349–356CrossRefGoogle Scholar
  16. 16.
    Bedau MA (2003) Artificial life: organization, adaptation and complexity from the bottom up. Trends Cogn Sci 7(11):505–512CrossRefGoogle Scholar
  17. 17.
    Bell G (2009) The fourth paradigm: data-intensive scientific discovery, 1st edn., chap. Foreword. Microsoft Research, Redmond, Washington, pp xi–xvGoogle Scholar
  18. 18.
    Ben-Gal I (2008) Bayesian Networks. Wiley, HobokenGoogle Scholar
  19. 19.
    di Bernardo D, Thompson MJ, Gardner TS, Chobot SE, Eastwood EL, Wojtovich AP, Elliott SJ, Schaus SE, Collins JJ (2005) Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat Biotechnol (3):377–383Google Scholar
  20. 20.
    Beyer HG, Schwefel HP (2002) Evolution strategies a comprehensive introduction. Nat Comput 1(1):3–52CrossRefMATHMathSciNetGoogle Scholar
  21. 21.
    Brockhoff D, Friedrich T, Hebbinghaus N, Klein C, Neumann F, Zitzler E (2007) Do additional objectives make a problem harder? In: Proceedings of the 9th annual conference on Genetic and evolutionary computation, GECCO’07. ACM, New York, NY, USA, pp 765–772Google Scholar
  22. 22.
    Brockhoff D, Friedrich T, Hebbinghaus N, Klein C, Neumann F, Zitzler E (2009) On the effects of adding objectives to plateau functions. Evol Comput IEEE Trans 13(3):591–603CrossRefGoogle Scholar
  23. 23.
    Bröker HB et al (2012) gnuplot 4.6: an interactive plotting program http://www.gnuplot.info/
  24. 24.
    Cai X (2009) A multi-objective gp-pso hybrid algorithm for gene regulatory network modeling. Ph.D. thesis, Kansas State University, Manhatten, KansasGoogle Scholar
  25. 25.
    Cai X, Koduru P, Das S, Welch SM (2009) Simultaneous structure discovery and parameter estimation in gene networks using a multi-objective gp-pso hybrid approach. Int J Bioinform Res Appl 5(3):254–268CrossRefGoogle Scholar
  26. 26.
    Chen BS, Hsu CY, Liou JJ (2011) Robust design of biological circuits: evolutionary systems biology approach. J Biomed Biotechnol 2011:14Google Scholar
  27. 27.
    Chen L (2007) Computational systems biology on networks and dynamics. In: Optimization and systems biology. Lecture notes in operations research, vol 7. World Publishing Corporation, pp 5–12. http://www.aporc.org/LNOR/7/OSB2007F02.pdf
  28. 28.
    Chen Xw, Anantha G, Wang X (2006) An effective structure learning method for constructing gene networks. Bioinformatics 22(11):1367–1374CrossRefGoogle Scholar
  29. 29.
    Chiquet J, Grandvalet Y, Ambroise C (2011) Inferring multiple graphical structures. Stat Comput 21(4):537–553CrossRefMATHMathSciNetGoogle Scholar
  30. 30.
    Chowdhury A, Chetty M, Vinh X (2012) On the reconstruction of genetic network from partial microarray data. In: Huang T, Zeng Z, Li C, Leung C (eds) Neural information processing. Lecture Notes in computer science, vol 7663. Springer, Berlin, pp 689–696Google Scholar
  31. 31.
    Crombach A, Hogeweg P (2008) Evolution of evolvability in gene regulatory networks. PLoS Comput Biol 4(7):e1000112CrossRefMathSciNetGoogle Scholar
  32. 32.
    von Dassow G, Meir E, Munro EM, Odell GM (2000) The segment polarity network is a robust developmental module. Nature 406:188–192. doi: 10.1038/35018085 CrossRefGoogle Scholar
  33. 33.
    DeLa Fuente A, Bing N, Hoeschele I, Mendes P (2004) Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20(18):3565–3574CrossRefGoogle Scholar
  34. 34.
    Deb K (2001) Multi-objective optimisation using evolutionary algorithms, 1st edn. Wiley, KanpurGoogle Scholar
  35. 35.
    Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: nsga-ii. Evol Comput IEEE Trans 6(2):182–197CrossRefGoogle Scholar
  36. 36.
    Deb K, Srinivasan A (2006) Innovization: innovating design principles through optimization. In: Proceedings of the 8th annual conference on genetic and evolutionary computation, GECCO’06. ACM, New York, NY, USA, pp 1629–1636Google Scholar
  37. 37.
    DSouza RGL, Sekaran KC, Kandasamy A (2007) A phenomic algorithm for reconstruction of gene networks. World Acad Sci Eng Technol 31:53–58Google Scholar
  38. 38.
    D’Souza RGL, Sekaran KCAK (2010) Reconstruction of gene networks using phenomic algorithms. Int J Artif Intell Appl 1(2)Google Scholar
  39. 39.
    Filkov V (2005) Identifying gene regulatory networks from gene expression data, chap. 27. Chapman & Hall/CRC, pp 27-1–27-29Google Scholar
  40. 40.
    Fioravanti F, Helmer-Citterich M, Nardelli E (2012) Modeling gene regulatory network motifs using statecharts. BMC Bioinform 13(4):1–12Google Scholar
  41. 41.
    Frank K, Rckl M, Nadales MJV, Robertson P, Pfeifer T (2010) Comparison of exact static and dynamic bayesian context inference methods for activity recognition. In: PerCom workshops. IEEE, pp 189–195Google Scholar
  42. 42.
    Friedman N, Linial M, Nachman I, Peér D (2000) Using bayesian networks to analyze expression data. J Comput Biol 7:601–620CrossRefGoogle Scholar
  43. 43.
    Gardner TS, di Bernardo D, Lorenz D, Collins JJ (2003) Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301(5629):102–105CrossRefGoogle Scholar
  44. 44.
    Geier F, Timmer J, Fleck C (2007) Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge. BMC Syst Biol 1Google Scholar
  45. 45.
    GenBank: National center for biotechnology information, genetic sequence data bank (June 15 2013). ftp://ftp.ncbi.nih.gov/genbank/gbrel.txt
  46. 46.
    Gonze D (2010) Coupling oscillations and switches in genetic networks. Biosystems 99(1):60–69CrossRefGoogle Scholar
  47. 47.
    Hache H, Lehrach H, Herwig R (2009) Reverse engineering of gene regulatory networks: a comparative study. EURASIP J Bioinform Syst Biol 2009(8):1–812Google Scholar
  48. 48.
    Hache H, Wierling C, Lehrach H, Herwig R (2007) Reconstruction and validation of gene regulatory networks with neural networks. In: The 2nd foundations of systems biology in engineering conference. FOSBE 2007, pp 319–324Google Scholar
  49. 49.
    Hadka D, Reed P (2013) Borg: An auto-adaptive many-objective evolutionary computing framework. Evol Comput 21:231–259CrossRefGoogle Scholar
  50. 50.
    Hallinan J (2007) Gene networks and evolutionary computation. Wiley, Hoboken, pp 67–96Google Scholar
  51. 51.
    Handl J, Kell DB, Knowles J (2007) Multiobjective optimization in bioinformatics and computational biology. IEEE/ACM Trans Comput Biol Bioinform 4(2):279–292CrossRefGoogle Scholar
  52. 52.
    Handl J, Lovell SC, Knowles J (2008) Investigations into the effect of multiobjectivization in protein structure prediction. In: Proceedings of the 10th international conference on parallel problem solving from nature: PPSN X. Springer, Berlin, pp 702–711Google Scholar
  53. 53.
    Hartwell LH, Hopfield JJ, Leibler S, Murray AW (1999) From molecular to modular cell biology. Nature 402:C47–C52CrossRefGoogle Scholar
  54. 54.
    Haynes WA, Higdon R, Stanberry L, Collins D, Kolker E (2013) Differential expression analysis for pathways. PLoS Comput Biol 9(3):e1002967CrossRefGoogle Scholar
  55. 55.
    Hecker M, Lambeck S, Toepfer S, van Someren E, Guthke R (2009) Gene regulatory network inference: data integration in dynamic modelsa review. Biosystems 96(1):86–103CrossRefGoogle Scholar
  56. 56.
    Hey T, Tansley S, Tolle K (eds) (2009) The fourth paradigm: data-intensive scientific discovery, 1st edn. Microsoft Research, Redmond, WashingtonGoogle Scholar
  57. 57.
    Hickman GJ, Hodgman TC (2009) Inference of gene regulatory networks using boolean-network inference methods. J Bioinform Comput Biol 07(06):1013–1029CrossRefGoogle Scholar
  58. 58.
    Higuera C, Villaverde AF, Banga JR, Ross J, Morn F (2012) Multi-criteria optimization of regulation in metabolic networks. PLoS ONE 7(7):e41122CrossRefGoogle Scholar
  59. 59.
    Hohm T, Zitzler, E (2009) Multiobjectivization for parameter estimation: a case-study on the segment polarity network of drosophila. In: Rothlauf F et al (eds) GECCO’09: genetic and evolutionary computation conference (GECCO 2009). ACM, New York, NY, USA, pp 209–216Google Scholar
  60. 60.
    Hoon MD, Imoto S, Miyano S (2003) Inferring gene regulatory networks from time-ordered gene expression data of bacillus subtilis using differential equations. In: Proceedings of the pacific symposium on biocomputing, pp 17–28Google Scholar
  61. 61.
    Hotz PE (2003) Exploring regenerative mechanisms found in flatworms by artificial evolutionary techniques using genetic regulatory networks. In: Proceedings of the congress on evolutionary computation, 2003. CEC’03, vol 3. pp 2026–2033Google Scholar
  62. 62.
    Hsieh ST, Sun TY, Liu CC, Tsai SJ (2008) Solving large scale global optimization using improved particle swarm optimizer. In: Proceedings of the IEEE congress on evolutionary computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence), pp 1777–1784Google Scholar
  63. 63.
    Iba H, Mimura A (2002) Inference of a gene regulatory network by means of interactive evolutionary computing. Inf Sci Inf Comput Sci 145(3–4):225–236Google Scholar
  64. 64.
    IBM: What is big data? (3/7/13). http://www-01.ibm.com/software/data/bigdata/
  65. 65.
    Ingram P, Stumpf M, Stark J (2006) Network motifs: structure does not determine function. BMC Genomics 7(1):1–12Google Scholar
  66. 66.
    Ishibuchi H, Tsukamoto N, Nojima Y (2008) Evolutionary many-objective optimization: a short review. In: Proceedings of congress on evolutionary computation, CEC, pp 2424–2431Google Scholar
  67. 67.
    Jain H, Deb K (2013) An improved adaptive approach for elitist nondominated sorting genetic algorithm for many-objective optimization. In: Purshouse R, Fleming P, Fonseca C, Greco S, Shaw J (eds) Evolutionary multi-criterion optimization, Lecture Notes in computer science, vol 7811. Springer, Berlin, pp 307–321Google Scholar
  68. 68.
    Jensen MT (2004) Helper-objectives: using multi-objective evolutionary algorithms for single-objective optimisation. J Math Model Algorithms 3:323–347CrossRefMATHMathSciNetGoogle Scholar
  69. 69.
    Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments-a survey. Evol Comput IEEE Trans 9(3):303–317CrossRefGoogle Scholar
  70. 70.
    Jin Y, Gruna R, Sendhoff B (2009) Pareto analysis of evolutionary and learning systems. Front Comput Sci China 3(1):4–17CrossRefGoogle Scholar
  71. 71.
    Jin Y, Meng Y (2011) Emergence of robust regulatory motifs from in silico evolution of sustained oscillation. Biosystems 103(1):38–44CrossRefMathSciNetGoogle Scholar
  72. 72.
    de Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 9:67–103CrossRefGoogle Scholar
  73. 73.
    de Jong H, Geiselmann J (2005) Modeling and simulation of genetic regulatory networks by ordinary differential equations. In: Genomic signal processing and statistics. Hindawi Publishing Corporation, New York, pp 201–239Google Scholar
  74. 74.
    Kabir M, Noman N, Iba H (2010) Reverse engineering gene regulatory network from microarray data using linear time-variant model. BMC Bioinform 11:S56CrossRefGoogle Scholar
  75. 75.
    Karlebach G, Shamir R (2008) Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 9:770–780CrossRefGoogle Scholar
  76. 76.
    Keedwell E, Narayanan A (2005) Discovering gene networks with a neural-genetic hybrid. IEEE/ACM Trans Comput Biol Bioinform 2(3):231–242CrossRefGoogle Scholar
  77. 77.
    Khammash M (2008) Reverse engineering: the architecture of biological networks. Biotechniques 44:323–329CrossRefGoogle Scholar
  78. 78.
    Khammash M, El-Samad H (2004) Systems biology: from physiology to gene regulation. Control Syst IEEE 24(4):62–76CrossRefMathSciNetGoogle Scholar
  79. 79.
    Kikuchi S, Tominaga D, Arita M, Takahashi K, Tomita M (2003) Dynamic modeling of genetic networks using genetic algorithm and s-system. Bioinformatics 19(5):643–650CrossRefGoogle Scholar
  80. 80.
    Kitano H (2002) Computational systems biology. Nature 420:206–210Google Scholar
  81. 81.
    Klemm SL (2008) Causal structure identification in nonlinear dynamical systems. Department of Engineering, University of Cambridge, UKGoogle Scholar
  82. 82.
    Knabe JF, Wegner K, Nehaniv CL, Schilstra MJ (2010) Genetic algorithms and their application to in silico evolution of genetic regulatory networks. In: Fenyö D (eds) Computational biology, methods in molecular biology, vol 673, Humana Press, New York City, pp 297–321Google Scholar
  83. 83.
    Knowles JD, Corne DW (2000) Approximating the nondominated front using the pareto archived evolution strategy. Evol Comput 8(2):149–172CrossRefGoogle Scholar
  84. 84.
    Knowles JD, Watson RA, Corne D (2001) Reducing local optima in single-objective problems by multi-objectivization. In: Proceedings of the first international conference on evolutionary multi-criterion optimization, EMO’01. Springer, London, pp 269–283Google Scholar
  85. 85.
    Kukkonen S, Lampinen J (2005) Gde3: the third evolution step of generalized differential evolution. In: The 2005 IEEE congress on evolutionary computation, 2005, vol 1, pp 443–450Google Scholar
  86. 86.
    Kuo PD, Banzhaf W, Leier A (2006) Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence. Biosystems 85(3):177–200Google Scholar
  87. 87.
    Kwon YK, Cho KH (2007) Analysis of feedback loops and robustness in network evolution based on boolean models. BMC Bioinform 430. doi: 10.1186/1471-2105-8-430
  88. 88.
    Kwon YK, Cho KH (2008) Quantitative analysis of robustness and fragility in biological networks based on feedback dynamics. Bioinformatics 24(7)Google Scholar
  89. 89.
    Lanely D (2001) 3D data management: controlling data volume, velocity and variety. Technical report, Application Delivery strategies: META GroupGoogle Scholar
  90. 90.
    Larus J, Gannon D (2009) The fourth paradigm: data-intensive scientific discovery, 1st edn., chap. Multicore computing and scientific discovery. Microsoft Research, Redmond, Washington, pp 125–129Google Scholar
  91. 91.
    Laumanns M, Rudolph G, Schwefel HP (1998) A spatial predator-prey approach to multi-objective optimization: a preliminary study. In: Proceedings of the 5th international conference on parallel problem solving from nature, PPSN V. Springer, London, pp 241–249Google Scholar
  92. 92.
    Lèbre S (2009) Inferring dynamic genetic networks with low order independencies. Stat Appl Genet Mol Biol 8:1–38CrossRefMathSciNetGoogle Scholar
  93. 93.
    Leclerc RD (2008) Survival of the sparsest: robust gene networks are parsimonious. Mol Syst Biol 1–6Google Scholar
  94. 94.
    Lee W-P, Hsiao Y-T (2008) Inferring gene regulatory networks by incremental evolution and network decomposition. In: Optimization and systems biology. Lecture notes in operations research, vol 9. World Publishing Corporation, pp 311–324. http://www.aporc.org/LNOR/9/OSB2008F40.pdf
  95. 95.
    Lenser T, Hinze T, Ibrahim B, Dittrich P (2007) Towards evolutionary network reconstruction tools for systems biology. In: Marchiori E, Moore J, Rajapakse J (eds) Evolutionary computation,machine learning and data mining in bioinformatics. Lecture Notes in computer science, vol 4447, Springer, Berlin, pp 132–142Google Scholar
  96. 96.
    Li C, Chen L, Aihara K (2007) A systems biology perspective on signal processing in genetic network motifs [life sciences]. Signal Process Mag IEEE 24(2):136–147CrossRefGoogle Scholar
  97. 97.
    Li J, Zhang X-S (2007) An optimization model for gene regulatory network reconstruction with known biological information. In: Optimization and systems biology. Lecture notes in operations research, vol 7. World Publishing Corporation, pp 35–44. http://www.aporc.org/LNOR/7/OSB2007F06.pdf
  98. 98.
    Li X, Yao X (2012) Cooperatively coevolving particle swarms for large scale optimization. Evol Comput IEEE Trans 16(2):210–224CrossRefMathSciNetGoogle Scholar
  99. 99.
    Liu Y, Niculescu-Mizil A, Lozano AC, Lu Y (2011) Temporal graphical models for cross-species gene regulatory network discovery. J Bioinform Comput Biol 9(2):231–250CrossRefGoogle Scholar
  100. 100.
    Lochtefeld D, Ciarallo F (2012) Multiobjectivization via helper-objectives with the tunable objectives problem. Evol Comput IEEE Trans16(3):373–390CrossRefGoogle Scholar
  101. 101.
    Maki Y, Tominaga D, Okamoto M, Watanabe S, Eguchi Y (2001) Development of a system for the inference of large scale genetic networks. Pac Symp Biocomput 446–458Google Scholar
  102. 102.
    Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Favera RD, Califano A (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform 7(Suppl 1):S7CrossRefGoogle Scholar
  103. 103.
    Marx V (2013) Biology: The big challenges of big data. Nature 498:255–260CrossRefGoogle Scholar
  104. 104.
    McLachlan GJ, Do KA, Ambroise C (2004) Analyzing microarray gene expression data. Wiley, HobokenCrossRefMATHGoogle Scholar
  105. 105.
    Mendoza MR, Bazzan ALC (2011) Evolving random boolean networks with genetic algorithms for regulatory networks reconstruction. In: Proceedings of the 13th annual conference on genetic and evolutionary computation, GECCO’11. ACM, New York, NY, USA, pp 291–298Google Scholar
  106. 106.
    Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, net U (2002) Network motifs: simple building blocks of complex networks. Science 298(5594):824–827CrossRefGoogle Scholar
  107. 107.
    Mitchell M (1999) An Introduction to genetic algorithms. The MIT Press, CambridgeGoogle Scholar
  108. 108.
    Mondal B, Sarkar A, Hasan M, Noman N (2010) Reconstruction of gene regulatory networks using differential evolution. In: Proceedings of the 13th international conference on computer and information technology (ICCIT), 2010, pp 440–445Google Scholar
  109. 109.
    Morishita R, Imade H, Ono l, Ono N, Okamoto M (2003) Finding multiple solutions based on an evolutionary algorithm for inference of genetic networks by s-system. In: The 2003 congress on evolutionary computation, 2003. CEC’03, vol 1. pp 615–622Google Scholar
  110. 110.
    Nakayama T, Seno S, Matsuda H (2011) Inference of s-system models of gene regulatory networks using immune algorithm. J Bioinform Comput Biol 9:75–86CrossRefGoogle Scholar
  111. 111.
    Nguyen TT, Yang S, Branke J (2012) Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evol Comput 6(0):1–24CrossRefGoogle Scholar
  112. 112.
    Noman N, Iba H (2006) Inference of genetic networks using s-system: information criteria for model selection. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation, GECCO’06. ACM, New York, NY, USA, pp 263–270Google Scholar
  113. 113.
    Noman N, Iba H (2007) Inferring gene regulatory networks using differential evolution with local search heuristics. IEEE/ACM Trans Comput Biol Bioinform 4(4):634–647CrossRefGoogle Scholar
  114. 114.
    Noor A, Serpedin E, Nounou M, Nounou H, Mohamed N, Chouchane L (2012) Information theoretic methods for modeling of gene regulatory networks. In: IEEE symposium on computational intelligence in bioinformatics and computational biology (CIBCB), 2012, pp 418–423Google Scholar
  115. 115.
    Pastrello C, Otasek D, Fortney K, Agapito G, Cannataro M, Shirdel E, Jurisica I (2013) Visual data mining of biological networks: one size does not fit all. PLoS Comput Biol 9(1): e1002833Google Scholar
  116. 116.
    Penfold CA, Wild DL (2011) How to infer gene networks from expression profiles, revisited. Interface Focus 1(6):857–870. http://rsfs.royalsocietypublishing.org/content/early/2011/07/26/rsfs.2011.0053.abstract
  117. 117.
    Purshouse RC, Fleming PJ, Fonseca CM, Greco S, Shaw J (2013) 7th international conference, emo 2013, sheffield, uk, march 19–22, 2013 proceedings. In: Evolutionary multi-criterion optimization, vol 7811. Springer, BerlinGoogle Scholar
  118. 118.
    Quackenbush J (2001) Computational analysis of microarray data. Nat Rev Genet (6):418–427Google Scholar
  119. 119.
    Ramons AF, Innocentini G, Forger FM, Hornos JE (2010) Symmetry in biology: from genetic code to stochastic gene regulation. IET Syst Biol 4(5):311–329CrossRefGoogle Scholar
  120. 120.
    Rangel C, Angus J, Ghahramani Z, Lioumi M, Sotheran E, Gaiba A, Wild DL, Falciani F (2004) Modeling t-cell activation using gene expression profiling and state-space models. Bioinformatics 20(9):1361–1372CrossRefGoogle Scholar
  121. 121.
    Rau A, Jaffrzic F, Foulley JL, Doerge R (2012) Reverse engineering gene regulatory networks using approximate bayesian computation. Stat Comput 22(6):1257–1271CrossRefMATHMathSciNetGoogle Scholar
  122. 122.
    Ros R, Hansen N (2008) A simple modification in cma-es achieving linear time and space complexity. In: Rudolph G, Jansen T, Lucas S, Poloni C, Beume N (eds) Parallel problem solving from nature PPSN X. Lecture Notes in computer science, vol 5199. Springer, Berlin, pp 296–305Google Scholar
  123. 123.
    Sakamoto E, Iba H (2001) Evolutionary inference of a biological network as differential equations by genetic programming. Genome Inform 276–277Google Scholar
  124. 124.
    Samad HE, Khammash M, Petzold L, Gillespie D (2005) Stochastic modelling of gene regulatory networks. Int J Robust Nonlinear Control 15:691–711. doi: 10.1002/rnc.1018 CrossRefMATHGoogle Scholar
  125. 125.
    Savageau MA (1969) Biochemical systems analysis: Ii. The steady-state solutions for an n-pool system using a power-law approximation. J Theor Biol 25:370–379CrossRefGoogle Scholar
  126. 126.
    Schäfer J, Strimmer K (2005) An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21(6):754–764CrossRefGoogle Scholar
  127. 127.
    Schilstra MJ, Nehaniv CL (2008) Bio-logic: gene expression and the laws of combinatorial logic. Artif Life 14Google Scholar
  128. 128.
    Schmidt M, Lipson H (2009) Distilling free-form natural laws from experimental data. Science 324(5923):81–85CrossRefGoogle Scholar
  129. 129.
    Schramm L, Jin Y, Sendhoff B (2012) Evolution and analysis of genetic networks for stable cellular growth and regeneration. Artif Life 18:425–444CrossRefGoogle Scholar
  130. 130.
    Schwefel HP (1981) Numerical optimization of computer models. Wiley, ChichsterMATHGoogle Scholar
  131. 131.
    Seth AK (2010) A MATLAB toolbox for granger causal connectivity analysis. J Neurosci Methods 186(2):262–273CrossRefGoogle Scholar
  132. 132.
    Shen-Orr SS, Milo R, Mangan S, Alon U (2002) Network motifs in the transcriptional regulations network of Escherichia coli. Nat Genet 31:64–68CrossRefGoogle Scholar
  133. 133.
    Shin A, Iba H (2003) Construction of genetic network using evolutionary algorithm and combined fitness function. Genome Inform 14:2003Google Scholar
  134. 134.
    Sîrbu A, Ruskin H, Crane M (2012) Integrating heterogeneous gene expression data for gene regulatory network modelling. Theory Biosci 131(2):95–102CrossRefGoogle Scholar
  135. 135.
    Sirbu A, Ruskin HJ, Crane M (2010) Comparison of evolutionary algorithms in gene regulatory network model inference. BMC Bioinform 11:59CrossRefGoogle Scholar
  136. 136.
    Sîrbu A, Ruskin HJ, Crane M (2010) Cross-platform microarray data normalisation for regulatory network inference. PLoS ONE 5(11):e13822CrossRefGoogle Scholar
  137. 137.
    Sîrbu A, Ruskin HJ, Crane M (2011) Stages of gene regulatory network inference: the evolutionary algorithm role. In: Kita PE (ed) Evolutionary algorithms. InTechGoogle Scholar
  138. 138.
    Solé RV, Valverde S (2006) Are network motifs the spandrels of cellular complexity? Trends Ecol Evol 21(8):419–422CrossRefGoogle Scholar
  139. 139.
    de Sompel HV, Lagoze C (2009) The fourth paradigm: data-intensive scientific discovery, 1st edn., chap. All aboard: toward a machine-friendly scholarly communication system. Microscoft Research, pp 193–199Google Scholar
  140. 140.
    Spieth C, Streichert F, Speer N, Zell A (2004) Optimizing topology and parameters of gene regulatory network models from time-series experiments. In: Deb K (eds) Genetic and evolutionary computation GECCO 2004. Lecture Notes in computer science, vol 3102. Springer, Berlin, pp 461–470Google Scholar
  141. 141.
    Spieth C, Streichert F, Supper J, Speer N, Zell A (2005) Algorithms for modeling gene regulatory networks. In: Proceedings of the 2005 IEEE symposium on computational intelligence in bioinformatics and computational biology, 2005. CIBCB ’05, pp 1–7Google Scholar
  142. 142.
    Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2:221–248CrossRefGoogle Scholar
  143. 143.
    Stricker J, Cookson S, Bennett MR, Mather WH, Tsimring LS, Hasty J (2008) A fast, robust and tunable synthetic gene oscillator. Nature 456:516–519CrossRefGoogle Scholar
  144. 144.
    Swain M, Hunniford T, Mandel J, Palfreyman N, Dubitzky W (2005) Modeling gene-regulatory networks using evolutionary algorithms and distributed computing. In: IEEE international symposium on cluster computing and the grid, 2005. CCGrid 2005, vol 1. pp 512–519Google Scholar
  145. 145.
    Tang K, Yao X, Suganthan PN, MacNish C, Chen YP, Chen CM, Yang Z (2007) Benchmark functions for the CEC 2008 special session and competition on large scale global optimization. Technical report. Nature Inspired Computation and Applications Laboratory (NICAL), ChinaGoogle Scholar
  146. 146.
    Tegnèr J, Yeung MKS, Hasty J, Collins JJ (2003) Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc Nat Acad Sci 100(10):5944–5949CrossRefGoogle Scholar
  147. 147.
    Thomas SA, Jin Y (2012) Combining genetic oscillators and switches using evolutionary algorithms. In: IEEE symposium on computational intelligence in bioinformatics and computational biology (CIBCB), 2012, pp 28 –34Google Scholar
  148. 148.
    Thomas SA, Jin Y (2013) Evolving connectivity between genetic oscillators and switches using evolutionary algorithms. J Bioinform Comput Biol 11(3):1341001Google Scholar
  149. 149.
    Thomas SA, Jin Y (2013) Single and multi-objective in silico evolution of tunable genetic oscillators. In: Purshouse R, Fleming P, Fonseca C, Greco S, Shaw J (eds) Evolutionary multi-criterion optimization. Lecture Notes in computer science, vol 7811. Springer, Berlin, pp 696–709Google Scholar
  150. 150.
    Tobin FL, Damian-iordache V, Greller LD (1999) Towards the reconstruction of gene regulatory networksGoogle Scholar
  151. 151.
    Tominaga D, Okamoto M, Maki Y, Watanabe S, Eguchi Y (1999) Nonlinear numerical optimization technique based on a genetic algorithm for inverse problems: towards the inference of genetic networks. In: German conference on bioinformatics’ 99, pp 127–140Google Scholar
  152. 152.
    Vignes M, Vandel J, Allouche D, Ramadan-Alban N, Cierco-Ayrolles C, Schiex T, Mangin B, de Givry S (2011) Gene regulatory network reconstruction using bayesian networks, the dantzig selector, the lasso and their meta-analysis. PLoS ONE 6(12):e29165CrossRefGoogle Scholar
  153. 153.
    Voit EO (2008) Model identification: a key challenge is computational systems biology. In: Optimization and systems biology. Lecture notes in operations research, vol 9, World Publishing Corporation, pp 1–12. http://www.aporc.org/LNOR/9/OSB2008F01.pdf
  154. 154.
    Voit EO, Almeida J (2004) Decoupling dynamical systems for pathway identification from metabolic profiles. Bioinformatics 20(11):1670–1681CrossRefGoogle Scholar
  155. 155.
    Wang Y, Zhang XS, Chen L (2010) Optimization meets systems biology. BMC Syst Biol 4(Suppl 2):1–4CrossRefMathSciNetGoogle Scholar
  156. 156.
    Whitehead DJ, Skusa A, Kennedy PJ (2004) Evaluating an evolutionary approach for reconstructing gene regulatory networks. In: Ninth international conference on the simulation and synthesis of living systems (ALIFE9). MIT Press, BostonGoogle Scholar
  157. 157.
    Wieczorek MA, Jolliff BL, Khan A, Pritchard ME, Weiss BP, Williams JG, Hood LL, Righter K, Neal CR, Shearer CK, McCallum IS, Tompkins S, Hawke BR, Peterson C, Gillis JJ, Bussey B (2006) The constitution and structure of the lunar interior. Rev Mineral Geochem 60:221–364CrossRefGoogle Scholar
  158. 158.
    Wiggins C (2012) It’s an exciting time for data in new york city. University of Columbia Engineering, NewsletterGoogle Scholar
  159. 159.
    Xiao M, Zhang L, He B, Xie J, Zhang W (2009) A parallel algorithm of constructing gene regulatory networks. In: Du DZ, Zhang XS (eds) Optimization and systems biology. Lecture Notes in operations research, vol 11. World Publishing Corporation, pp 184–188Google Scholar
  160. 160.
    Xiong J, Zhou T (2012) Gene regulatory network inference from multifactorial perturbation data using both regression and correlation analyses. PLoS ONE 7(9):e43819CrossRefGoogle Scholar
  161. 161.
    Yang Z, Tang K, Yao X (2008) Multilevel cooperative coevolution for large scale optimization. In: IEEE congress on evolutionary computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence), pp 1663–1670Google Scholar
  162. 162.
    Yavari F, Towhidkhah F, Gharibzadeh S (2008) Gene regulatory network modeling using bayesian networks and cross correlation. In: Biomedical engineering conference, 2008. CIBEC 2008. Cairo International, pp 1–4Google Scholar
  163. 163.
    Yip KY, Alexander RP, Yan KK, Gerstein M (2010) Improved reconstruction of !‘italic? ‘in silico!‘/italic?’ gene regulatory networks by integrating knockout and perturbation data. PLoS ONE 5(1):e8121CrossRefGoogle Scholar
  164. 164.
    Yu J, Smith VA, Wang PP, Hartemink AJ, Jarvis ED (2004) Advances to bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20(18):3594–3603CrossRefGoogle Scholar
  165. 165.
    Yuan Y, Stan GB, Warnick S, Goncalves JM (2011) Robust dynamical network structure reconstruction. Autom Spe Issue Syst Biol 47:1230–1235MATHMathSciNetGoogle Scholar
  166. 166.
    Zhang X, Zhao XM, He K, Lu L, Cao Y, Liu J, Hao JK, Liu ZP, Chen L (2012) Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics 28(1):98–104CrossRefGoogle Scholar
  167. 167.
    Zhang Z, Bajic VB, Yu J, Cheung KH, Townsend JP (2011) Data integration in bioinformatics: current efforts and challenges. In: Mahdavi DMA (ed) Trends and methodologies, chap. 2. InTech, pp 41–56Google Scholar
  168. 168.
    Zhao SZ, Liang JJ, Suganthan P, Tasgetiren M (2008) Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization. In: IEEE congress on evolutionary computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence), pp 3845–3852Google Scholar
  169. 169.
    Zhu H, Rao RSP, Zeng T, Chen L (2012) Reconstructing dynamic gene regulatory networks from sample-based transcriptional data. Nucleic Acids Res 40(21):10657–10667Google Scholar
  170. 170.
    Zitzler E, Künzli S (2004) Indicator-based selection in multiobjective search. In: Proceedings of the 8th international conference on parallel problem solving from nature (PPSN VIII). Springer, Berlin, pp 832–842Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of ComputingUniversity of SurreyGuildfordUK

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