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Volumn 7, Issue 1, 2014, Pages 29-47

Reconstructing biological gene regulatory networks: Where optimization meets big data

Author keywords

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

Indexed keywords

ARTIFICIAL LIFE; BIG DATA; BIOCHIPS; DATA SCIENCE; EVOLUTIONARY ALGORITHMS; GENE EXPRESSION; HEURISTIC ALGORITHMS; INFERENCE ENGINES; OPTIMIZATION; REVERSE ENGINEERING;

EID: 84894345697     PISSN: 18645909     EISSN: 18645917     Source Type: Journal    
DOI: 10.1007/s12065-013-0098-7     Document Type: Review
Times cited : (40)

References (170)
  • 1
    • 70449375094 scopus 로고    scopus 로고
    • Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics
    • Äijö T, Lähdesmäki H (2009) Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. Bioinformatics 25(22): 2937-2944.
    • (2009) Bioinformatics , vol.25 , Issue.22 , pp. 2937-2944
    • Äijö, T.1    Lähdesmäki, H.2
  • 2
    • 52949109243 scopus 로고    scopus 로고
    • Algorithms for inference, analysis and control of boolean networks
    • In: Horimoto K, Regensburger G, Rosenkranz M, Yoshida H (eds), Springer, Berlin
    • 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.
    • (2008) Algebraic biology. Lecture Notes in computer science , vol.5147 , pp. 1-15
    • Akutsu, T.1    Hayashida, M.2    Tamura, T.3
  • 3
    • 0034721164 scopus 로고    scopus 로고
    • Error and attack tolerance of complex networks
    • Albert R, Jeong H, Barabasi AL (2000) Error and attack tolerance of complex networks. Nature 406: 378-382.
    • (2000) Nature , vol.406 , pp. 378-382
    • Albert, R.1    Jeong, H.2    Barabasi, A.L.3
  • 4
    • 84868160004 scopus 로고    scopus 로고
    • Diverse control of metabolism and other cellular processes in streptomyces coelicolor by the phop transcription factor: genome-wide identification of in vivo targets
    • 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.
    • (2012) Nucleic Acids Res , vol.40 , Issue.19 , pp. 9543-9556
    • Allenby, N.E.E.1    Laing, E.2    Bucca, G.3    Kierzek, A.M.4    Smith, C.P.5
  • 6
    • 34249079154 scopus 로고    scopus 로고
    • Network motifs: theory and experimental approaches
    • Alon U (2007) Network motifs: theory and experimental approaches. Nat Rev Genet 8: 450-461.
    • (2007) Nat. Rev. Genet. , vol.8 , pp. 450-461
    • Alon, U.1
  • 10
    • 79953824573 scopus 로고    scopus 로고
    • Automated innovization for simultaneous discovery of multiple rules in bi-objective problems
    • R. Takahashi, K. Deb, E. Wanner, and S. Greco (Eds.), Berlin: Springer
    • 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.
    • (2011) Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science, Vol. 6576 , pp. 1-15
    • Bandaru, S.1    Deb, K.2
  • 11
    • 84860388963 scopus 로고    scopus 로고
    • Towards automating the discovery of certain innovative design principles through a clustering-based optimization technique
    • 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.
    • (2011) Engineering Optimization , vol.43 , Issue.9 , pp. 911-941
    • Bandaru, S.1    Deb, K.2
  • 12
    • 84875501275 scopus 로고    scopus 로고
    • A dimensionally-aware genetic programming architecture for automated innovization
    • R. Purshouse, P. Fleming, C. Fonseca, S. Greco, and J. Shaw (Eds.), Berlin Heidelberg: Springer
    • 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.
    • (2013) Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science, Vol. 7811 , pp. 513-527
    • Bandaru, S.1    Deb, K.2
  • 14
    • 33645307955 scopus 로고    scopus 로고
    • Inference of gene regulatory networks and compound mode of action from time course gene expression profiles
    • 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.
    • (2006) Bioinformatics , vol.22 , Issue.7 , pp. 815-822
    • Bansal, M.1    Gatta, G.D.2    di Bernardo, D.3
  • 15
    • 13844253637 scopus 로고    scopus 로고
    • A bayesian approach to reconstructing genetic regulatory networks with hidden factors
    • 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.
    • (2005) Bioinformatics , vol.21 , Issue.3 , pp. 349-356
    • Beal, M.J.1    Falciani, F.2    Ghahramani, Z.3    Rangel, C.4    Wild, D.L.5
  • 16
    • 0242321034 scopus 로고    scopus 로고
    • Artificial life: organization, adaptation and complexity from the bottom up
    • Bedau MA (2003) Artificial life: organization, adaptation and complexity from the bottom up. Trends Cogn Sci 7(11): 505-512.
    • (2003) Trends in Cognitive Sciences , vol.7 , Issue.11 , pp. 505-512
    • Bedau, M.A.1
  • 20
    • 0037592480 scopus 로고    scopus 로고
    • Evolution strategies a comprehensive introduction
    • Beyer HG, Schwefel HP (2002) Evolution strategies a comprehensive introduction. Nat Comput 1(1): 3-52.
    • (2002) Nat Comput , vol.1 , Issue.1 , pp. 3-52
    • Beyer, H.G.1    Schwefel, H.P.2
  • 25
    • 67649238145 scopus 로고    scopus 로고
    • Simultaneous structure discovery and parameter estimation in gene networks using a multi-objective gp-pso hybrid approach
    • 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.
    • (2009) Int J Bioinf Res Appl , vol.5 , Issue.3 , pp. 254-268
    • Cai, X.1    Koduru, P.2    Das, S.3    Welch, S.M.4
  • 27
    • 84894376059 scopus 로고    scopus 로고
    • Computational systems biology on networks and dynamics
    • World Publishing Corporation
    • 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.
    • (2007) Optimization and systems biology. Lecture notes in operations research , vol.7 , pp. 5-12
    • Chen, L.1
  • 28
    • 33745622668 scopus 로고    scopus 로고
    • An effective structure learning method for constructing gene networks
    • Chen Xw, Anantha G, Wang X (2006) An effective structure learning method for constructing gene networks. Bioinformatics 22(11): 1367-1374.
    • (2006) Bioinformatics , vol.22 , Issue.11 , pp. 1367-1374
    • Chen, X.1    Anantha, G.2    Wang, X.3
  • 30
    • 84869054145 scopus 로고    scopus 로고
    • On the reconstruction of genetic network from partial microarray data
    • T. Huang, Z. Zeng, C. Li, and C. Leung (Eds.), Berlin Heidelberg: Springer
    • 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.
    • (2012) Neural Information Processing, Lecture Notes in Computer Science, Vol. 7663 , pp. 689-696
    • Chowdhury, A.1    Chetty, M.2    Vinh, X.3
  • 31
    • 48249134043 scopus 로고    scopus 로고
    • Evolution of evolvability in gene regulatory networks
    • e1000,112
    • Crombach A, Hogeweg P (2008) Evolution of evolvability in gene regulatory networks. PLoS Comput Biol 4(7): e1000112.
    • (2008) PLoS Comput Biol , vol.4 , Issue.7
    • Crombach, A.1    Hogeweg, P.2
  • 32
    • 0034644270 scopus 로고    scopus 로고
    • The segment polarity network is a robust developmental module
    • doi:10.1038/35018085
    • 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.
    • (2000) Nature , vol.406 , pp. 188-192
    • von Dassow, G.1    Meir, E.2    Munro, E.M.3    Odell, G.M.4
  • 33
    • 12344321571 scopus 로고    scopus 로고
    • Discovery of meaningful associations in genomic data using partial correlation coefficients
    • 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.
    • (2004) Bioinformatics , vol.20 , Issue.18 , pp. 3565-3574
    • Dela Fuente, A.1    Bing, N.2    Hoeschele, I.3    Mendes, P.4
  • 41
    • 77954023542 scopus 로고    scopus 로고
    • Comparison of exact static and dynamic bayesian context inference methods for activity recognition
    • IEEE
    • 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.
    • (2010) PerCom workshops , pp. 189-195
    • Frank, K.1    Rckl, M.2    Nadales, M.J.V.3    Robertson, P.4    Pfeifer, T.5
  • 42
  • 43
    • 0038048325 scopus 로고    scopus 로고
    • Inferring genetic networks and identifying compound mode of action via expression profiling
    • 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.
    • (2003) Science , vol.301 , Issue.5629 , pp. 102-105
    • Gardner, T.S.1    di Bernardo, D.2    Lorenz, D.3    Collins, J.J.4
  • 44
    • 34548538013 scopus 로고    scopus 로고
    • Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge
    • Geier F, Timmer J, Fleck C (2007) Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge. BMC Syst Biol 1.
    • (2007) BMC Syst Biol , vol.1
    • Geier, F.1    Timmer, J.2    Fleck, C.3
  • 45
    • 84894327478 scopus 로고    scopus 로고
    • GenBank: National center for biotechnology information, genetic sequence data bank (June 15 2013)
    • GenBank: National center for biotechnology information, genetic sequence data bank (June 15 2013). ftp://ftp. ncbi. nih. gov/genbank/gbrel. txt.
  • 46
    • 70649104601 scopus 로고    scopus 로고
    • Coupling oscillations and switches in genetic networks
    • Gonze D (2010) Coupling oscillations and switches in genetic networks. Biosystems 99(1): 60-69.
    • (2010) Biosystems , vol.99 , Issue.1 , pp. 60-69
    • Gonze, D.1
  • 47
    • 68149164746 scopus 로고    scopus 로고
    • Reverse engineering of gene regulatory networks: a comparative study
    • 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.
    • (2009) EURASIP J. Bioinformatics Syst. Biol. , vol.2009 , Issue.8 , pp. 1-812
    • Hache, H.1    Lehrach, H.2    Herwig, R.3
  • 49
    • 84887667705 scopus 로고    scopus 로고
    • Borg: An auto-adaptive many-objective evolutionary computing framework
    • Hadka D, Reed P (2013) Borg: An auto-adaptive many-objective evolutionary computing framework. Evol Comput 21: 231-259.
    • (2013) Evolutionary Computation , vol.21 , pp. 231-259
    • Hadka, D.1    Reed, P.2
  • 55
    • 61349180117 scopus 로고    scopus 로고
    • Gene regulatory network inference: Data integration in dynamic modelsa review
    • 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.
    • (2009) Biosystems , vol.96 , Issue.1 , pp. 86-103
    • Hecker, M.1    Lambeck, S.2    Toepfer, S.3    van Someren, E.4    Guthke, R.5
  • 58
    • 84864386279 scopus 로고    scopus 로고
    • Multi-criteria optimization of regulation in metabolic networks
    • e41,122
    • Higuera C, Villaverde AF, Banga JR, Ross J, Morn F (2012) Multi-criteria optimization of regulation in metabolic networks. PLoS ONE 7(7): e41122.
    • (2012) PLoS ONE , vol.7 , Issue.7
    • Higuera, C.1    Villaverde, A.F.2    Banga, J.R.3    Ross, J.4    Morn, F.5
  • 59
    • 72749125592 scopus 로고    scopus 로고
    • Multiobjectivization for parameter estimation: a case-study on the segment polarity network of drosophila
    • In: Rothlauf F et al (eds), ACM, New York, NY, USA
    • 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.
    • (2009) GECCO'09: Genetic and evolutionary computation conference (GECCO 2009) , pp. 209-216
    • Hohm, T.1    Zitzler, E.2
  • 60
    • 0043130707 scopus 로고    scopus 로고
    • Inferring gene regulatory networks from time-ordered gene expression data of bacillus subtilis using differential equations
    • 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.
    • (2003) Proceedings of the pacific symposium on biocomputing , pp. 17-28
    • Hoon, M.D.1    Imoto, S.2    Miyano, S.3
  • 61
    • 84901435615 scopus 로고    scopus 로고
    • Exploring regenerative mechanisms found in flatworms by artificial evolutionary techniques using genetic regulatory networks
    • 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.
    • (2003) Proceedings of the congress on evolutionary computation, 2003. CEC'03 , vol.3 , pp. 2026-2033
    • Hotz, P.E.1
  • 63
    • 0036749274 scopus 로고    scopus 로고
    • Inference of a gene regulatory network by means of interactive evolutionary computing
    • 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.
    • (2002) Inf. Sci. Inf. Comput. Sci. , vol.145 , Issue.3-4 , pp. 225-236
    • Iba, H.1    Mimura, A.2
  • 64
    • 84876041347 scopus 로고    scopus 로고
    • (3/7/13)
    • IBM: What is big data? (3/7/13). http://www-01. ibm. com/software/data/bigdata/.
    • IBM: What is big data?
  • 65
    • 33745780500 scopus 로고    scopus 로고
    • Network motifs: structure does not determine function
    • Ingram P, Stumpf M, Stark J (2006) Network motifs: structure does not determine function. BMC Genomics 7(1): 1-12.
    • (2006) BMC Genomics , vol.7 , Issue.1 , pp. 1-12
    • Ingram, P.1    Stumpf, M.2    Stark, J.3
  • 67
    • 84875485202 scopus 로고    scopus 로고
    • 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), Springer, Berlin
    • 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.
    • (2013) Evolutionary multi-criterion optimization, Lecture Notes in computer science , vol.7811 , pp. 307-321
    • Jain, H.1    Deb, K.2
  • 68
    • 20944439075 scopus 로고    scopus 로고
    • Helper-objectives: Using multi-objective evolutionary algorithms for single-objective optimisation
    • Jensen MT (2004) Helper-objectives: using multi-objective evolutionary algorithms for single-objective optimisation. J Math Model Algorithms 3: 323-347.
    • (2004) Journal of Mathematical Modelling and Algorithms , vol.3 , pp. 323-347
    • Jensen, M.T.1
  • 71
    • 78650198665 scopus 로고    scopus 로고
    • Emergence of robust regulatory motifs from in silico evolution of sustained oscillation
    • Jin Y, Meng Y (2011) Emergence of robust regulatory motifs from in silico evolution of sustained oscillation. Biosystems 103(1): 38-44.
    • (2011) Biosystems , vol.103 , Issue.1 , pp. 38-44
    • Jin, Y.1    Meng, Y.2
  • 72
    • 0036207347 scopus 로고    scopus 로고
    • Modeling and simulation of genetic regulatory systems: A literature review
    • de Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 9: 67-103.
    • (2002) J Comput Biol , vol.9 , pp. 67-103
    • de Jong, H.1
  • 73
    • 84873667710 scopus 로고    scopus 로고
    • Modeling and simulation of genetic regulatory networks by ordinary differential equations
    • Hindawi Publishing Corporation, New York
    • 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.
    • (2005) Genomic signal processing and statistics , pp. 201-239
    • de Jong, H.1    Geiselmann, J.2
  • 74
    • 75149166629 scopus 로고    scopus 로고
    • Reverse engineering gene regulatory network from microarray data using linear time-variant model
    • Kabir M, Noman N, Iba H (2010) Reverse engineering gene regulatory network from microarray data using linear time-variant model. BMC Bioinform 11: S56.
    • (2010) BMC Bioinformatics , vol.11
    • Kabir, M.1    Noman, N.2    Iba, H.3
  • 75
    • 52649087274 scopus 로고    scopus 로고
    • Modelling and analysis of gene regulatory networks
    • Karlebach G, Shamir R (2008) Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 9: 770-780.
    • (2008) Nat Rev Mol Cell Biol , vol.9 , pp. 770-780
    • Karlebach, G.1    Shamir, R.2
  • 77
    • 41849102778 scopus 로고    scopus 로고
    • Reverse engineering: the architecture of biological networks
    • Khammash M (2008) Reverse engineering: the architecture of biological networks. Biotechniques 44: 323-329.
    • (2008) Biotechniques , vol.44 , pp. 323-329
    • Khammash, M.1
  • 78
    • 3442884234 scopus 로고    scopus 로고
    • Systems biology: from physiology to gene regulation
    • Khammash M, El-Samad H (2004) Systems biology: from physiology to gene regulation. Control Syst IEEE 24(4): 62-76.
    • (2004) Control Systems, IEEE , vol.24 , Issue.4 , pp. 62-76
    • Khammash, M.1    El-Samad, H.2
  • 79
    • 0037461033 scopus 로고    scopus 로고
    • Dynamic modeling of genetic networks using genetic algorithm and s-system
    • 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.
    • (2003) Bioinformatics , vol.19 , Issue.5 , pp. 643-650
    • Kikuchi, S.1    Tominaga, D.2    Arita, M.3    Takahashi, K.4    Tomita, M.5
  • 80
    • 0037079054 scopus 로고    scopus 로고
    • Computational systems biology
    • Kitano H (2002) Computational systems biology. Nature 420: 206-210.
    • (2002) Nature , vol.420 , pp. 206-210
    • Kitano, H.1
  • 82
    • 79952112403 scopus 로고    scopus 로고
    • Genetic algorithms and their application to in silico evolution of genetic regulatory networks
    • D. Fenyö (Ed.), New York City: Humana Press
    • 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.
    • (2010) Computational Biology, Methods in Molecular Biology, Vol. 673 , pp. 297-321
    • Knabe, J.F.1    Wegner, K.2    Nehaniv, C.L.3    Schilstra, M.J.4
  • 83
    • 0034199912 scopus 로고    scopus 로고
    • Approximating the nondominated front using the pareto archived evolution strategy
    • Knowles JD, Corne DW (2000) Approximating the nondominated front using the pareto archived evolution strategy. Evol Comput 8(2): 149-172.
    • (2000) Evol. Comput. , vol.8 , Issue.2 , pp. 149-172
    • Knowles, J.D.1    Corne, D.W.2
  • 86
    • 33746922321 scopus 로고    scopus 로고
    • Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence
    • 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.
    • (2006) Biosystems , vol.85 , Issue.3 , pp. 177-200
    • Kuo, P.D.1    Banzhaf, W.2    Leier, A.3
  • 87
    • 39749099388 scopus 로고    scopus 로고
    • Analysis of feedback loops and robustness in network evolution based on boolean models
    • doi: 10. 1186/1471-2105-8-430
    • 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.
    • (2007) BMC Bioinform , vol.430
    • Kwon, Y.K.1    Cho, K.H.2
  • 88
    • 41349112310 scopus 로고    scopus 로고
    • Quantitative analysis of robustness and fragility in biological networks based on feedback dynamics
    • Kwon YK, Cho KH (2008) Quantitative analysis of robustness and fragility in biological networks based on feedback dynamics. Bioinformatics 24(7).
    • (2008) Bioinformatics , vol.24 , Issue.7
    • Kwon, Y.K.1    Cho, K.H.2
  • 90
    • 79953021881 scopus 로고    scopus 로고
    • 1st edn., chap. Multicore computing and scientific discovery. Microsoft Research, Redmond, Washington
    • 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.
    • (2009) The fourth paradigm: data-intensive scientific discovery , pp. 125-129
    • Larus, J.1    Gannon, D.2
  • 93
    • 84872827093 scopus 로고    scopus 로고
    • Survival of the sparsest: robust gene networks are parsimonious
    • Leclerc RD (2008) Survival of the sparsest: robust gene networks are parsimonious. Mol Syst Biol 1-6.
    • (2008) Mol Syst Biol , pp. 1-6
    • Leclerc, R.D.1
  • 94
    • 84894326449 scopus 로고    scopus 로고
    • Inferring gene regulatory networks by incremental evolution and network decomposition
    • World Publishing Corporation
    • 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.
    • (2008) Optimization and systems biology. Lecture notes in operations research , vol.9 , pp. 311-324
    • Lee, W.-P.1    Hsiao, Y.-T.2
  • 96
    • 85032751721 scopus 로고    scopus 로고
    • A systems biology perspective on signal processing in genetic network motifs [life sciences]
    • 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.
    • (2007) Signal Processing Magazine, IEEE , vol.24 , Issue.2 , pp. 136-147
    • Li, C.1    Chen, L.2    Aihara, K.3
  • 97
    • 84894359363 scopus 로고    scopus 로고
    • An optimization model for gene regulatory network reconstruction with known biological information
    • World Publishing Corporation
    • 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.
    • (2007) Optimization and systems biology. Lecture notes in operations research , vol.7 , pp. 35-44
    • Li, J.1    Zhang, X.-S.2
  • 98
    • 84859719176 scopus 로고    scopus 로고
    • Cooperatively coevolving particle swarms for large scale optimization
    • Li X, Yao X (2012) Cooperatively coevolving particle swarms for large scale optimization. Evol Comput IEEE Trans 16(2): 210-224.
    • (2012) Evolutionary Computation, IEEE Transactions on , vol.16 , Issue.2 , pp. 210-224
    • Li, X.1    Yao, X.2
  • 103
    • 84878979335 scopus 로고    scopus 로고
    • Biology: The big challenges of big data
    • Marx V (2013) Biology: The big challenges of big data. Nature 498: 255-260.
    • (2013) Nature , vol.498 , pp. 255-260
    • Marx, V.1
  • 109
    • 84901454547 scopus 로고    scopus 로고
    • Finding multiple solutions based on an evolutionary algorithm for inference of genetic networks by s-system
    • CEC'03
    • 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.
    • (2003) The 2003 congress on evolutionary computation, 2003 , vol.1 , pp. 615-622
    • Morishita, R.1    Imade, H.2    Ono, L.3    Ono, N.4    Okamoto, M.5
  • 110
    • 82355170988 scopus 로고    scopus 로고
    • Inference of s-system models of gene regulatory networks using immune algorithm
    • 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.
    • (2011) J Bioinform Comput Biol , vol.9 , pp. 75-86
    • Nakayama, T.1    Seno, S.2    Matsuda, H.3
  • 112
    • 33750281420 scopus 로고    scopus 로고
    • Inference of genetic networks using s-system: information criteria for model selection
    • GECCO'06. ACM, New York, NY, USA
    • 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.
    • (2006) Proceedings of the 8th annual conference on Genetic and evolutionary computation , pp. 263-270
    • Noman, N.1    Iba, H.2
  • 113
    • 36248948496 scopus 로고    scopus 로고
    • Inferring gene regulatory networks using differential evolution with local search heuristics
    • 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.
    • (2007) IEEE/ACM Trans. Comput. Biol. Bioinformatics , vol.4 , Issue.4 , pp. 634-647
    • Noman, N.1    Iba, H.2
  • 116
    • 84859149116 scopus 로고    scopus 로고
    • How to infer gene networks from expression profiles, revisited
    • 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.
    • (2011) Interface Focus , vol.1 , Issue.6 , pp. 857-870
    • Penfold, C.A.1    Wild, D.L.2
  • 118
    • 0035375137 scopus 로고    scopus 로고
    • Computational analysis of microarray data
    • Quackenbush J (2001) Computational analysis of microarray data. Nat Rev Genet (6): 418-427.
    • (2001) Nat Rev Genet , Issue.6 , pp. 418-427
    • Quackenbush, J.1
  • 119
    • 77956630040 scopus 로고    scopus 로고
    • Symmetry in biology: from genetic code to stochastic gene regulation
    • 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.
    • (2010) IET Syst Biol , vol.4 , Issue.5 , pp. 311-329
    • Ramons, A.F.1    Innocentini, G.2    Forger, F.M.3    Hornos, J.E.4
  • 121
    • 84867979927 scopus 로고    scopus 로고
    • Reverse engineering gene regulatory networks using approximate bayesian computation
    • Rau A, Jaffrzic F, Foulley JL, Doerge R (2012) Reverse engineering gene regulatory networks using approximate bayesian computation. Stat Comput 22(6): 1257-1271.
    • (2012) Statistics and Computing , vol.22 , Issue.6 , pp. 1257-1271
    • Rau, A.1    Jaffrzic, F.2    Foulley, J.L.3    Doerge, R.4
  • 122
    • 56449130100 scopus 로고    scopus 로고
    • A simple modification in cma-es achieving linear time and space complexity
    • G. Rudolph, T. Jansen, S. Lucas, C. Poloni, and N. Beume (Eds.), Berlin Heidelberg: Springer
    • 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.
    • (2008) Parallel Problem Solving from Nature PPSN X, Lecture Notes in Computer Science, Vol. 5199 , pp. 296-305
    • Ros, R.1    Hansen, N.2
  • 123
    • 4344693709 scopus 로고    scopus 로고
    • Evolutionary inference of a biological network as differential equations by genetic programming
    • Sakamoto E, Iba H (2001) Evolutionary inference of a biological network as differential equations by genetic programming. Genome Inform 276-277.
    • (2001) Genome Inform , pp. 276-277
    • Sakamoto, E.1    Iba, H.2
  • 125
    • 0014658880 scopus 로고
    • Biochemical systems analysis: Ii. the steady-state solutions for an n-pool system using a power-law approximation
    • 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.
    • (1969) J Theor Biol , vol.25 , pp. 370-379
    • Savageau, M.A.1
  • 126
    • 15944364151 scopus 로고    scopus 로고
    • An empirical bayes approach to inferring large-scale gene association networks
    • Schäfer J, Strimmer K (2005) An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21(6): 754-764.
    • (2005) Bioinformatics , vol.21 , Issue.6 , pp. 754-764
    • Schäfer, J.1    Strimmer, K.2
  • 127
    • 38049095819 scopus 로고    scopus 로고
    • Bio-logic: gene expression and the laws of combinatorial logic
    • Schilstra MJ, Nehaniv CL (2008) Bio-logic: gene expression and the laws of combinatorial logic. Artif Life 14.
    • (2008) Artif Life , vol.14
    • Schilstra, M.J.1    Nehaniv, C.L.2
  • 128
    • 64249142111 scopus 로고    scopus 로고
    • Distilling free-form natural laws from experimental data
    • Schmidt M, Lipson H (2009) Distilling free-form natural laws from experimental data. Science 324(5923): 81-85.
    • (2009) Science , vol.324 , Issue.5923 , pp. 81-85
    • Schmidt, M.1    Lipson, H.2
  • 129
    • 85047687585 scopus 로고    scopus 로고
    • Evolution and analysis of genetic networks for stable cellular growth and regeneration
    • Schramm L, Jin Y, Sendhoff B (2012) Evolution and analysis of genetic networks for stable cellular growth and regeneration. Artif Life 18: 425-444.
    • (2012) Artificial Life , vol.18 , pp. 425-444
    • Schramm, L.1    Jin, Y.2    Sendhoff, B.3
  • 131
    • 74149089224 scopus 로고    scopus 로고
    • A MATLAB toolbox for granger causal connectivity analysis
    • Seth AK (2010) A MATLAB toolbox for granger causal connectivity analysis. J Neurosci Methods 186(2): 262-273.
    • (2010) J Neurosci Methods , vol.186 , Issue.2 , pp. 262-273
    • Seth, A.K.1
  • 132
    • 0036578795 scopus 로고    scopus 로고
    • Network motifs in the transcriptional regulations network of escherichia coli
    • 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.
    • (2002) Nat Genet , vol.31 , pp. 64-68
    • Shen-Orr, S.S.1    Milo, R.2    Mangan, S.3    Alon, U.4
  • 133
    • 14944363706 scopus 로고    scopus 로고
    • Construction of genetic network using evolutionary algorithm and combined fitness function
    • Shin A, Iba H (2003) Construction of genetic network using evolutionary algorithm and combined fitness function. Genome Inform 14: 2003.
    • (2003) Genome Inform , vol.14 , pp. 2003
    • Shin, A.1    Iba, H.2
  • 134
    • 84865248233 scopus 로고    scopus 로고
    • Integrating heterogeneous gene expression data for gene regulatory network modelling
    • Sîrbu A, Ruskin H, Crane M (2012) Integrating heterogeneous gene expression data for gene regulatory network modelling. Theory Biosci 131(2): 95-102.
    • (2012) Theory Biosci , vol.131 , Issue.2 , pp. 95-102
    • Sîrbu, A.1    Ruskin, H.2    Crane, M.3
  • 135
    • 77649176945 scopus 로고    scopus 로고
    • Comparison of evolutionary algorithms in gene regulatory network model inference
    • Sirbu A, Ruskin HJ, Crane M (2010) Comparison of evolutionary algorithms in gene regulatory network model inference. BMC Bioinform 11: 59.
    • (2010) BMC Bioinformatics , vol.11 , pp. 59
    • Sirbu, A.1    Ruskin, H.J.2    Crane, M.3
  • 136
    • 78649755610 scopus 로고    scopus 로고
    • Cross-platform microarray data normalisation for regulatory network inference
    • e13,822
    • Sîrbu A, Ruskin HJ, Crane M (2010) Cross-platform microarray data normalisation for regulatory network inference. PLoS ONE 5(11): e13822.
    • (2010) PLoS ONE , vol.5 , Issue.11
    • Sîrbu, A.1    Ruskin, H.J.2    Crane, M.3
  • 137
    • 84870387877 scopus 로고    scopus 로고
    • Stages of gene regulatory network inference: the evolutionary algorithm role
    • In: Kita PE (ed), InTech
    • Sîrbu A, Ruskin HJ, Crane M (2011) Stages of gene regulatory network inference: the evolutionary algorithm role. In: Kita PE (ed) Evolutionary algorithms. InTech.
    • (2011) Evolutionary algorithms
    • Sîrbu, A.1    Ruskin, H.J.2    Crane, M.3
  • 139
    • 79960295612 scopus 로고    scopus 로고
    • 1st edn., chap. All aboard: toward a machine-friendly scholarly communication system. Microscoft Research
    • 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.
    • (2009) The fourth paradigm: data-intensive scientific discovery , pp. 193-199
    • de Sompel, H.V.1    Lagoze, C.2
  • 142
    • 0000852513 scopus 로고
    • Multiobjective optimization using nondominated sorting in genetic algorithms
    • Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2: 221-248.
    • (1994) Evolutionary Computation , vol.2 , pp. 221-248
    • Srinivas, N.1    Deb, K.2
  • 145
    • 55749098451 scopus 로고    scopus 로고
    • Benchmark functions for the CEC 2008 special session and competition on large scale global optimization
    • Nature Inspired Computation and Applications Laboratory (NICAL), China
    • 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.
    • (2007) Technical report
    • Tang, K.1    Yao, X.2    Suganthan, P.N.3    MacNish, C.4    Chen, Y.P.5    Chen, C.M.6    Yang, Z.7
  • 146
    • 0037687416 scopus 로고    scopus 로고
    • Reverse engineering gene networks: Integrating genetic perturbations with dynamical modeling
    • 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.
    • (2003) Proc Nat Acad Sci , vol.100 , Issue.10 , pp. 5944-5949
    • Tegnèr, J.1    Yeung, M.K.S.2    Hasty, J.3    Collins, J.J.4
  • 148
    • 84879676784 scopus 로고    scopus 로고
    • Evolving connectivity between genetic oscillators and switches using evolutionary algorithms
    • 1341001
    • Thomas SA, Jin Y (2013) Evolving connectivity between genetic oscillators and switches using evolutionary algorithms. J Bioinform Comput Biol 11(3): 1341001.
    • (2013) J Bioinform Comput Biol , vol.11 , Issue.3
    • Thomas, S.A.1    Jin, Y.2
  • 149
    • 84875553363 scopus 로고    scopus 로고
    • Single and multi-objective in silico evolution of tunable genetic oscillators
    • In: Purshouse R, Fleming P, Fonseca C, Greco S, Shaw J (eds), Springer, Berlin
    • 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.
    • (2013) Evolutionary multi-criterion optimization. Lecture Notes in computer science , vol.7811 , pp. 696-709
    • Thomas, S.A.1    Jin, Y.2
  • 151
    • 4344703653 scopus 로고    scopus 로고
    • Nonlinear numerical optimization technique based on a genetic algorithm for inverse problems: towards the inference of genetic networks
    • 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.
    • (1999) German conference on bioinformatics' 99 , pp. 127-140
    • Tominaga, D.1    Okamoto, M.2    Maki, Y.3    Watanabe, S.4    Eguchi, Y.5
  • 153
    • 84874386593 scopus 로고    scopus 로고
    • Model identification: a key challenge is computational systems biology
    • World Publishing Corporation
    • 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.
    • (2008) Optimization and systems biology. Lecture notes in operations research , vol.9 , pp. 1-12
    • Voit, E.O.1
  • 154
    • 4444351989 scopus 로고    scopus 로고
    • Decoupling dynamical systems for pathway identification from metabolic profiles
    • Voit EO, Almeida J (2004) Decoupling dynamical systems for pathway identification from metabolic profiles. Bioinformatics 20(11): 1670-1681.
    • (2004) Bioinformatics , vol.20 , Issue.11 , pp. 1670-1681
    • Voit, E.O.1    Almeida, J.2
  • 155
    • 79952109162 scopus 로고    scopus 로고
    • Optimization meets systems biology
    • Wang Y, Zhang XS, Chen L (2010) Optimization meets systems biology. BMC Syst Biol 4(Suppl 2): 1-4.
    • (2010) BMC Syst Biol , vol.4 , Issue.SUPPL. 2 , pp. 1-4
    • Wang, Y.1    Zhang, X.S.2    Chen, L.3
  • 160
    • 84866642239 scopus 로고    scopus 로고
    • Gene regulatory network inference from multifactorial perturbation data using both regression and correlation analyses
    • e43,819
    • Xiong J, Zhou T (2012) Gene regulatory network inference from multifactorial perturbation data using both regression and correlation analyses. PLoS ONE 7(9): e43819.
    • (2012) PLoS ONE , vol.7 , Issue.9
    • Xiong, J.1    Zhou, T.2
  • 161
    • 55749099311 scopus 로고    scopus 로고
    • Multilevel cooperative coevolution for large scale optimization
    • (IEEE World Congress on Computational Intelligence)
    • 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.
    • (2008) IEEE congress on evolutionary computation, 2008. CEC 2008 , pp. 1663-1670
    • Yang, Z.1    Tang, K.2    Yao, X.3
  • 162
    • 63649099828 scopus 로고    scopus 로고
    • Gene regulatory network modeling using bayesian networks and cross correlation
    • CIBEC 2008. Cairo International
    • 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.
    • (2008) Biomedical engineering conference, 2008 , pp. 1-4
    • Yavari, F.1    Towhidkhah, F.2    Gharibzadeh, S.3
  • 163
    • 77749264188 scopus 로고    scopus 로고
    • Improved reconstruction of!'italic?'in silico!'/italic?' gene regulatory networks by integrating knockout and perturbation data
    • 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.
    • (2010) PLoS ONE , vol.5 , Issue.1
    • Yip, K.Y.1    Alexander, R.P.2    Yan, K.K.3    Gerstein, M.4
  • 164
    • 12344259602 scopus 로고    scopus 로고
    • Advances to bayesian network inference for generating causal networks from observational biological data
    • 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.
    • (2004) Bioinformatics , vol.20 , Issue.18 , pp. 3594-3603
    • Yu, J.1    Smith, V.A.2    Wang, P.P.3    Hartemink, A.J.4    Jarvis, E.D.5
  • 166
    • 84855160951 scopus 로고    scopus 로고
    • Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information
    • 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.
    • (2012) Bioinformatics , vol.28 , Issue.1 , pp. 98-104
    • Zhang, X.1    Zhao, X.M.2    He, K.3    Lu, L.4    Cao, Y.5    Liu, J.6    Hao, J.K.7    Liu, Z.P.8    Chen, L.9
  • 168
    • 55749113325 scopus 로고    scopus 로고
    • Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization
    • CEC 2008. (IEEE World Congress on Computational Intelligence)
    • 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.
    • (2008) IEEE congress on evolutionary computation, 2008 , pp. 3845-3852
    • Zhao, S.Z.1    Liang, J.J.2    Suganthan, P.3    Tasgetiren, M.4
  • 169
    • 84870578537 scopus 로고    scopus 로고
    • Reconstructing dynamic gene regulatory networks from sample-based transcriptional data
    • 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.
    • (2012) Nucleic Acids Res , vol.40 , Issue.21 , pp. 10657-10667
    • Zhu, H.1    Rao, R.S.P.2    Zeng, T.3    Chen, L.4


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