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Reconstructing biological gene regulatory networks: where optimization meets big data

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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.

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References

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

    Article  Google Scholar 

  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 1–15

  3. Albert R, Jeong H, Barabasi AL (2000) Error and attack tolerance of complex networks. Nature 406:378–382

    Google Scholar 

  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):9543–9556

    Google Scholar 

  5. Alon U (2006) An introduction to systems biology: design principles of biological circuits. Chapman & Hall/CRC

    Google Scholar 

  6. Alon U (2007) Network motifs: theory and experimental approaches. Nat Rev Genet 8:450–461

    Article  Google Scholar 

  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 459–473

  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 712–719

  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):283–291

    Article  Google Scholar 

  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 1–15

    Google Scholar 

  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):911–941

    Article  Google Scholar 

  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 513–527

    Google Scholar 

  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):1–10

    Google Scholar 

  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):815–822

    Article  Google Scholar 

  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):349–356

    Article  Google Scholar 

  16. Bedau MA (2003) Artificial life: organization, adaptation and complexity from the bottom up. Trends Cogn Sci 7(11):505–512

    Article  Google Scholar 

  17. Bell G (2009) The fourth paradigm: data-intensive scientific discovery, 1st edn., chap. Foreword. Microsoft Research, Redmond, Washington, pp xi–xv

  18. Ben-Gal I (2008) Bayesian Networks. Wiley, Hoboken

    Google Scholar 

  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):377–383

  20. Beyer HG, Schwefel HP (2002) Evolution strategies a comprehensive introduction. Nat Comput 1(1):3–52

    Article  MATH  MathSciNet  Google Scholar 

  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 765–772

  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):591–603

    Article  Google Scholar 

  23. Bröker HB et al (2012) gnuplot 4.6: an interactive plotting program http://www.gnuplot.info/

  24. Cai X (2009) A multi-objective gp-pso hybrid algorithm for gene regulatory network modeling. Ph.D. thesis, Kansas State University, Manhatten, Kansas

  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):254–268

    Article  Google Scholar 

  26. Chen BS, Hsu CY, Liou JJ (2011) Robust design of biological circuits: evolutionary systems biology approach. J Biomed Biotechnol 2011:14

    Google Scholar 

  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. Chen Xw, Anantha G, Wang X (2006) An effective structure learning method for constructing gene networks. Bioinformatics 22(11):1367–1374

    Article  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  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 689–696

    Google Scholar 

  31. Crombach A, Hogeweg P (2008) Evolution of evolvability in gene regulatory networks. PLoS Comput Biol 4(7):e1000112

    Article  MathSciNet  Google Scholar 

  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

    Article  Google Scholar 

  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):3565–3574

    Article  Google Scholar 

  34. Deb K (2001) Multi-objective optimisation using evolutionary algorithms, 1st edn. Wiley, Kanpur

    Google Scholar 

  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):182–197

    Article  Google Scholar 

  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 1629–1636

  37. DSouza RGL, Sekaran KC, Kandasamy A (2007) A phenomic algorithm for reconstruction of gene networks. World Acad Sci Eng Technol 31:53–58

    Google Scholar 

  38. D’Souza RGL, Sekaran KCAK (2010) Reconstruction of gene networks using phenomic algorithms. Int J Artif Intell Appl 1(2)

  39. Filkov V (2005) Identifying gene regulatory networks from gene expression data, chap. 27. Chapman & Hall/CRC, pp 27-1–27-29

  40. Fioravanti F, Helmer-Citterich M, Nardelli E (2012) Modeling gene regulatory network motifs using statecharts. BMC Bioinform 13(4):1–12

    Google Scholar 

  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 189–195

  42. Friedman N, Linial M, Nachman I, Peér D (2000) Using bayesian networks to analyze expression data. J Comput Biol 7:601–620

    Article  Google Scholar 

  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):102–105

    Article  Google Scholar 

  44. Geier F, Timmer J, Fleck C (2007) Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge. BMC Syst Biol 1

  45. GenBank: National center for biotechnology information, genetic sequence data bank (June 15 2013). ftp://ftp.ncbi.nih.gov/genbank/gbrel.txt

  46. Gonze D (2010) Coupling oscillations and switches in genetic networks. Biosystems 99(1):60–69

    Article  Google Scholar 

  47. Hache H, Lehrach H, Herwig R (2009) Reverse engineering of gene regulatory networks: a comparative study. EURASIP J Bioinform Syst Biol 2009(8):1–812

    Google Scholar 

  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 319–324

  49. Hadka D, Reed P (2013) Borg: An auto-adaptive many-objective evolutionary computing framework. Evol Comput 21:231–259

    Article  Google Scholar 

  50. Hallinan J (2007) Gene networks and evolutionary computation. Wiley, Hoboken, pp 67–96

    Google Scholar 

  51. Handl J, Kell DB, Knowles J (2007) Multiobjective optimization in bioinformatics and computational biology. IEEE/ACM Trans Comput Biol Bioinform 4(2):279–292

    Article  Google Scholar 

  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 702–711

  53. Hartwell LH, Hopfield JJ, Leibler S, Murray AW (1999) From molecular to modular cell biology. Nature 402:C47–C52

    Article  Google Scholar 

  54. Haynes WA, Higdon R, Stanberry L, Collins D, Kolker E (2013) Differential expression analysis for pathways. PLoS Comput Biol 9(3):e1002967

    Article  Google Scholar 

  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):86–103

    Article  Google Scholar 

  56. Hey T, Tansley S, Tolle K (eds) (2009) The fourth paradigm: data-intensive scientific discovery, 1st edn. Microsoft Research, Redmond, Washington

  57. Hickman GJ, Hodgman TC (2009) Inference of gene regulatory networks using boolean-network inference methods. J Bioinform Comput Biol 07(06):1013–1029

    Article  Google Scholar 

  58. Higuera C, Villaverde AF, Banga JR, Ross J, Morn F (2012) Multi-criteria optimization of regulation in metabolic networks. PLoS ONE 7(7):e41122

    Article  Google Scholar 

  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 209–216

  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 17–28

  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 2026–2033

  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 1777–1784

  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):225–236

    Google Scholar 

  64. IBM: What is big data? (3/7/13). http://www-01.ibm.com/software/data/bigdata/

  65. Ingram P, Stumpf M, Stark J (2006) Network motifs: structure does not determine function. BMC Genomics 7(1):1–12

    Google Scholar 

  66. Ishibuchi H, Tsukamoto N, Nojima Y (2008) Evolutionary many-objective optimization: a short review. In: Proceedings of congress on evolutionary computation, CEC, pp 2424–2431

  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 307–321

  68. Jensen MT (2004) Helper-objectives: using multi-objective evolutionary algorithms for single-objective optimisation. J Math Model Algorithms 3:323–347

    Article  MATH  MathSciNet  Google Scholar 

  69. Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments-a survey. Evol Comput IEEE Trans 9(3):303–317

    Article  Google Scholar 

  70. Jin Y, Gruna R, Sendhoff B (2009) Pareto analysis of evolutionary and learning systems. Front Comput Sci China 3(1):4–17

    Article  Google Scholar 

  71. Jin Y, Meng Y (2011) Emergence of robust regulatory motifs from in silico evolution of sustained oscillation. Biosystems 103(1):38–44

    Article  MathSciNet  Google Scholar 

  72. de Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 9:67–103

    Article  Google Scholar 

  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 201–239

  74. Kabir M, Noman N, Iba H (2010) Reverse engineering gene regulatory network from microarray data using linear time-variant model. BMC Bioinform 11:S56

    Article  Google Scholar 

  75. Karlebach G, Shamir R (2008) Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 9:770–780

    Article  Google Scholar 

  76. Keedwell E, Narayanan A (2005) Discovering gene networks with a neural-genetic hybrid. IEEE/ACM Trans Comput Biol Bioinform 2(3):231–242

    Article  Google Scholar 

  77. Khammash M (2008) Reverse engineering: the architecture of biological networks. Biotechniques 44:323–329

    Article  Google Scholar 

  78. Khammash M, El-Samad H (2004) Systems biology: from physiology to gene regulation. Control Syst IEEE 24(4):62–76

    Article  MathSciNet  Google Scholar 

  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):643–650

    Article  Google Scholar 

  80. Kitano H (2002) Computational systems biology. Nature 420:206–210

    Google Scholar 

  81. Klemm SL (2008) Causal structure identification in nonlinear dynamical systems. Department of Engineering, University of Cambridge, UK

  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 297–321

    Google Scholar 

  83. Knowles JD, Corne DW (2000) Approximating the nondominated front using the pareto archived evolution strategy. Evol Comput 8(2):149–172

    Article  Google Scholar 

  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 269–283

  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 443–450

  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):177–200

    Google Scholar 

  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. Kwon YK, Cho KH (2008) Quantitative analysis of robustness and fragility in biological networks based on feedback dynamics. Bioinformatics 24(7)

  89. Lanely D (2001) 3D data management: controlling data volume, velocity and variety. Technical report, Application Delivery strategies: META Group

  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 125–129

  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 241–249

  92. Lèbre S (2009) Inferring dynamic genetic networks with low order independencies. Stat Appl Genet Mol Biol 8:1–38

    Article  MathSciNet  Google Scholar 

  93. Leclerc RD (2008) Survival of the sparsest: robust gene networks are parsimonious. Mol Syst Biol 1–6

  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. 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 132–142

    Google Scholar 

  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):136–147

    Article  Google Scholar 

  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. Li X, Yao X (2012) Cooperatively coevolving particle swarms for large scale optimization. Evol Comput IEEE Trans 16(2):210–224

    Article  MathSciNet  Google Scholar 

  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):231–250

    Article  Google Scholar 

  100. Lochtefeld D, Ciarallo F (2012) Multiobjectivization via helper-objectives with the tunable objectives problem. Evol Comput IEEE Trans16(3):373–390

    Article  Google Scholar 

  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 446–458

  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):S7

    Article  Google Scholar 

  103. Marx V (2013) Biology: The big challenges of big data. Nature 498:255–260

    Article  Google Scholar 

  104. McLachlan GJ, Do KA, Ambroise C (2004) Analyzing microarray gene expression data. Wiley, Hoboken

    Book  MATH  Google Scholar 

  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 291–298

  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):824–827

    Article  Google Scholar 

  107. Mitchell M (1999) An Introduction to genetic algorithms. The MIT Press, Cambridge

    Google Scholar 

  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 440–445

  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 615–622

  110. Nakayama T, Seno S, Matsuda H (2011) Inference of s-system models of gene regulatory networks using immune algorithm. J Bioinform Comput Biol 9:75–86

    Article  Google Scholar 

  111. Nguyen TT, Yang S, Branke J (2012) Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evol Comput 6(0):1–24

    Article  Google Scholar 

  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 263–270

  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):634–647

    Article  Google Scholar 

  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 418–423

  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): e1002833

    Google Scholar 

  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. 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, Berlin

  118. Quackenbush J (2001) Computational analysis of microarray data. Nat Rev Genet (6):418–427

  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):311–329

    Article  Google Scholar 

  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):1361–1372

    Article  Google Scholar 

  121. Rau A, Jaffrzic F, Foulley JL, Doerge R (2012) Reverse engineering gene regulatory networks using approximate bayesian computation. Stat Comput 22(6):1257–1271

    Article  MATH  MathSciNet  Google Scholar 

  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 296–305

    Google Scholar 

  123. Sakamoto E, Iba H (2001) Evolutionary inference of a biological network as differential equations by genetic programming. Genome Inform 276–277

  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

    Article  MATH  Google Scholar 

  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 25:370–379

    Article  Google Scholar 

  126. Schäfer J, Strimmer K (2005) An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21(6):754–764

    Article  Google Scholar 

  127. Schilstra MJ, Nehaniv CL (2008) Bio-logic: gene expression and the laws of combinatorial logic. Artif Life 14

  128. Schmidt M, Lipson H (2009) Distilling free-form natural laws from experimental data. Science 324(5923):81–85

    Article  Google Scholar 

  129. Schramm L, Jin Y, Sendhoff B (2012) Evolution and analysis of genetic networks for stable cellular growth and regeneration. Artif Life 18:425–444

    Article  Google Scholar 

  130. Schwefel HP (1981) Numerical optimization of computer models. Wiley, Chichster

    MATH  Google Scholar 

  131. Seth AK (2010) A MATLAB toolbox for granger causal connectivity analysis. J Neurosci Methods 186(2):262–273

    Article  Google Scholar 

  132. Shen-Orr SS, Milo R, Mangan S, Alon U (2002) Network motifs in the transcriptional regulations network of Escherichia coli. Nat Genet 31:64–68

    Article  Google Scholar 

  133. Shin A, Iba H (2003) Construction of genetic network using evolutionary algorithm and combined fitness function. Genome Inform 14:2003

    Google Scholar 

  134. Sîrbu A, Ruskin H, Crane M (2012) Integrating heterogeneous gene expression data for gene regulatory network modelling. Theory Biosci 131(2):95–102

    Article  Google Scholar 

  135. Sirbu A, Ruskin HJ, Crane M (2010) Comparison of evolutionary algorithms in gene regulatory network model inference. BMC Bioinform 11:59

    Article  Google Scholar 

  136. Sîrbu A, Ruskin HJ, Crane M (2010) Cross-platform microarray data normalisation for regulatory network inference. PLoS ONE 5(11):e13822

    Article  Google Scholar 

  137. Sîrbu A, Ruskin HJ, Crane M (2011) Stages of gene regulatory network inference: the evolutionary algorithm role. In: Kita PE (ed) Evolutionary algorithms. InTech

  138. Solé RV, Valverde S (2006) Are network motifs the spandrels of cellular complexity? Trends Ecol Evol 21(8):419–422

    Article  Google Scholar 

  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 193–199

  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 461–470

  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 1–7

  142. Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2:221–248

    Article  Google Scholar 

  143. Stricker J, Cookson S, Bennett MR, Mather WH, Tsimring LS, Hasty J (2008) A fast, robust and tunable synthetic gene oscillator. Nature 456:516–519

    Article  Google Scholar 

  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 512–519

  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), China

  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):5944–5949

    Article  Google Scholar 

  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 –34

  148. Thomas SA, Jin Y (2013) Evolving connectivity between genetic oscillators and switches using evolutionary algorithms. J Bioinform Comput Biol 11(3):1341001

    Google Scholar 

  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 696–709

  150. Tobin FL, Damian-iordache V, Greller LD (1999) Towards the reconstruction of gene regulatory networks

  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 127–140

  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):e29165

    Article  Google Scholar 

  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. Voit EO, Almeida J (2004) Decoupling dynamical systems for pathway identification from metabolic profiles. Bioinformatics 20(11):1670–1681

    Article  Google Scholar 

  155. Wang Y, Zhang XS, Chen L (2010) Optimization meets systems biology. BMC Syst Biol 4(Suppl 2):1–4

    Article  MathSciNet  Google Scholar 

  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, Boston

  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 60:221–364

    Article  Google Scholar 

  158. Wiggins C (2012) It’s an exciting time for data in new york city. University of Columbia Engineering, Newsletter

    Google Scholar 

  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 184–188

  160. Xiong J, Zhou T (2012) Gene regulatory network inference from multifactorial perturbation data using both regression and correlation analyses. PLoS ONE 7(9):e43819

    Article  Google Scholar 

  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 1663–1670

  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 1–4

  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):e8121

    Article  Google Scholar 

  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):3594–3603

    Article  Google Scholar 

  165. Yuan Y, Stan GB, Warnick S, Goncalves JM (2011) Robust dynamical network structure reconstruction. Autom Spe Issue Syst Biol 47:1230–1235

    MATH  MathSciNet  Google Scholar 

  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):98–104

    Article  Google Scholar 

  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 41–56

  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 3845–3852

  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):10657–10667

    Google Scholar 

  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 832–842

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Acknowledgments

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

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  1. Department of Computing, University of Surrey, Guildford, Surrey, GU2 7XH, UK

    Spencer Angus Thomas & Yaochu Jin

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Thomas, S.A., Jin, Y. Reconstructing biological gene regulatory networks: where optimization meets big data. Evol. Intel. 7, 29–47 (2014). https://doi.org/10.1007/s12065-013-0098-7

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