Comparison of evolutionary algorithms in gene regulatory network model inference

BMC Bioinformatics

Volumn 11, Issue , 2010, Pages

  Sîrbu, Alina ^a   Ruskin, Heather J ^a   Crane, Martin ^a  

^a DUBLIN CITY UNIVERSITY   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

CAUSAL RELATIONSHIPS; COMPREHENSIVE COMPARISONS; GENE EXPRESSION DATA; GENE EXPRESSION LEVELS; GENE REGULATORY NETWORK MODEL; GENE REGULATORY NETWORKS; HIGH THROUGHPUT TECHNOLOGY; ROBUSTNESS TO NOISE;

BIOASSAY; BIOCHIPS; GENE EXPRESSION; REVERSE ENGINEERING;

BIOLOGY;

ALGORITHM; ARTICLE; BIOLOGY; COMPARATIVE STUDY; DNA MICROARRAY; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORK; METHODOLOGY; MOLECULAR EVOLUTION;

ALGORITHMS; COMPUTATIONAL BIOLOGY; EVOLUTION, MOLECULAR; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS;

EID: 77649176945     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-11-59     Document Type: Article

References (60)

1. de Jong H. Modeling and Simulation of Genetic Regulatory Systems: A Literature Review. Journal of Computational Biology 2002, 9:67-103. 10.1089/10665270252833208, 11911796.
2. Xu R, Hu X, Wunsch IDC. Inference of genetic regulatory networks from time series gene expression data. Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on 2004 2:1215-1220. vol.2.
3. Davidich M, Bornholdt S. The transition from differential equations to Boolean networks: A case study in simplifying a regulatory network model. Journal of Theoretical Biology 2008, 255(3):269-277. 10.1016/j.jtbi.2008.07.020, 18692073.
4. Schlitt T, Brazma A. Current approaches to gene regulatory network modelling. BMC Bioinformatics 2007, 8(Suppl 6):S9. 10.1186/1471-2105-8-S6-S9, 1995542, 17903290.
5. Alvarez-Buylla ER, Benitez M, Davila EB, Chaos A, Espinosa-Soto C, Padilla-Longoria P. Gene regulatory network models for plant development. Current Opinion in Plant Biology 2007, 10:83-91. [Growth and Development/Edited by Cris Kuhlemeier and Neelima Sinha], 10.1016/j.pbi.2006.11.008, 17142086.
6. Liang S, Fuhrman S, Somogyi R. Reveal: a general reverse engineering algorithm for inference of genetic network architectures. Pacific Symposium on Biocomputing 1998, 18-29.
7. Barrett CL, Kim TY, Kim HU, Palsson B, Lee SY. Systems biology as a foundation for genome-scale synthetic biology. Current Opinions in Biotechnology 2006, 17(5):488-92.
8. Kim S, Dougherty ER, Chen Y, Sivakumar K, Meltzer P, Trent JM, Bittner M. Multivariate measurement of gene expression relationships. Genomics 2000, 67:201-209. 10.1006/geno.2000.6241, 10903845.
9. Maki Y, Tominaga D, Okamoto M, Watanabe S, Eguchi Y. Development of a system for the inference of large scale genetic networks. Pac Symp Biocomput 2001, 446-458.
10. Friedman N, Linial M, Nachman I, Pe'er D. Using Bayesian networks to analyze expression data. Journal of Computational Biology 2000, 7:601-620. 10.1089/106652700750050961, 11108481.
11. Akutsu T, Miyano S, Kuhara S. Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics 2000, 16(8):727-734. 10.1093/bioinformatics/16.8.727, 11099258.
12. Shmulevich I, Dougherty ER, Kim S, Zhang W. Probabilistic Boolean networks: a rule based uncertainty model for gene regulatory networks. Bioinformatics 2002, 18:261-274. 10.1093/bioinformatics/18.2.261, 11847074.
13. Zhao W, Serpedin E, Dougherty E. Inferring Connectivity of Genetic Regulatory Networks Using Information-Theoretic Criteria. Computational Biology and Bioinformatics, IEEE/ACM Transactions on 2008 5(2):262-274.
14. Nguyen VA, Zomaya A. Inference of large-scale structural features of gene regulation networks using genetic algorithms. Parallel and Distributed Processing, 2008. IPDPS 2008. IEEE International Symposium on 2008 1-8.
15. Dougherty J, Tabus I, Astola J. Inference of gene regulatory networks based on a universal minimum description length. EURASIP Journal of Bioinformatics and Systems Biology 2008, 8:1-11.
16. Chaouiya C, Remy E, Thieffry D. Petri net modelling of biological regulatory networks. Journal of Discrete Algorithms 2008, 6(2):165-177.
17. Grunwald S, Speer A, Ackermann J, Koch I. Petri net modelling of gene regulation of the Duchenne muscular dystrophy. Bio Systems 2008, 92(2):189-205.
18. Ferrazzi F, Sebastiani P, Ramoni M, Bellazzi R. Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks. BMC Bioinformatics 2007, 8(Suppl 5):S2. 10.1186/1471-2105-8-S5-S2, 1892090, 17570861.
19. Liu TF, Sung WK, Mittal A. Model gene network by semi-fixed Bayesian network. Expert Systems with Applications 2006, 30:42-49. [Intelligent Bioinformatics Systems].
20. Nariai N, Tamada Y, Imoto S, Miyano S. Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data. Bioinformatics 2005, 21(Suppl 2):206-212.
21. Morishita R, Imade H, Ono I, Ono N, Okamoto M. Finding multiple solutions based on an evolutionary algorithm for inference of genetic networks by S-system. Evolutionary Computation, 2003. CEC '03. The 2003 Congress on 2003 1:615-622. Vol.1.
22. Wahde M, Hertz J. Coarse-grained reverse engineering of genetic regulatory networks. Biosystems 2000, 55(1-3):129-136. 10.1016/S0303-2647(99)00090-8, 10745116.
23. Kikuchi S, Tominaga D, Arita M, Takahashi K, Tomita M. Dynamic modeling of genetic networks using genetic algorithm and S-system. Bioinformatics 2003, 19(5):643-650. 10.1093/bioinformatics/btg027, 12651723.
24. Tominaga D, Okamoto M, Maki Y, Watanabe S, Eguchi Y. Nonlinear Numerical Optimization Technique Based on a Genetic Algorithm for Inverse Problems: Towards the Inference of Genetic Networks. GCB99 German Conference on Bioinformatics 1999, 101-111.
25. Tian T, Burrage K. Stochastic neural network models for gene regulatory networks. Evolutionary Computation, 2003. CEC '03. The 2003 Congress on 2003 1:162-169. Vol.1.
26. Noman N, Iba H. Inference of genetic networks using S-system: information criteria for model selection. GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation 2006, 263-270. New York, NY, USA: ACM.
27. Xu R, Venayagamoorthy GK, Donald C, Wunsch I. Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization. Neural Networks 2007, 20(8):917-927. 10.1016/j.neunet.2007.07.002, 17714912.
28. Kotte O, Heinemann M. A divide-and-conquer approach to analyze underdetermined biochemical models. Bioinformatics 2009, 25(4):519-525. 10.1093/bioinformatics/btp004, 19126574.
29. Bauer DC, Bailey TL. Optimizing static thermodynamic models of transcriptional regulation. Bioinformatics 2009, 25(13):1640-1646. 10.1093/bioinformatics/btp283, 2732318, 19398449.
30. Janssens H, Hou S, Jaeger J, Kim ARR, Myasnikova E, Sharp D, Reinitz J. Quantitative and predictive model of transcriptional control of the Drosophila melanogaster even skipped gene. Nature Genetics 2006, 38:1159-1165. 10.1038/ng1886, 16980977.
31. Noman N, Iba H. Inferring gene regulatory networks using differential evolution with local search heuristics. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2007, 4(4):634-647.
32. Ferrazzi F, Magni P, Sacchi L, Nuzzo A, Petrovic U, Bellazzi R. Inferring gene regulatory networks by integrating static and dynamic data. International Journal of Medical Informatics 2007, 76(Supplement 3):S462-S475. 10.1016/j.ijmedinf.2007.07.005, 17825607.
33. Lee WP, Yang KC. A clustering-based approach for inferring recurrent neural networks as gene regulatory networks. Neurocomputation 2008, 71(4-6):600-610.
34. Spieth C, Streichert F, Zell NSA. Optimizing Topology and Parameters of Gene Regulatory Network Models from Time-Series Experiments. Genetic and Evolutionary Computation - GECCO 2004 2004, 461-470.
35. Keedwell E, Narayanan A. Discovering gene networks with a neural-genetic hybrid. Computational Biology and Bioinformatics, IEEE/ACM Transactions on 2005 2(3):231-242.
36. Spieth C, Streichert F, Supper J, Speer N, Zell A. Feedback Memetic Algorithms for Modeling Gene Regulatory Networks. Computational Intelligence in Bioinformatics and Computational Biology, 2005. CIBCB '05. Proceedings of the 2005 IEEE Symposium on 2005 1-7.
37. Stolovitzky G, Monroe DON, Califano A. Dialogue on Reverse-Engineering Assessment and Methods: The DREAM of High-Throughput Pathway Inference. Annals of the New York Academy of Sciences 2007, 1115:1-22. 10.1196/annals.1407.021, 17925349.
38. Stolovitzky G, Prill RJ, Califano A. Lessons from the DREAM2 Challenges: A Community Effort to Assess Biological Network Inference. Annals of the New York Academy of Sciences 2009, 1158(37):159-195. 10.1111/j.1749-6632.2009.04497.x, 19348640.
39. Hurd PJ, Nelson CJ. Advantages of next-generation sequencing versus the microarray in epigenetic research. Brief Funct Genomic Proteomic 2009, elp013.
40. Baeck T, Fogel DB, Michalewicz Z. Evolutionary Computation 1: Basic Algorithms and Operators 2000, Institute of Physics Publishing Bristol and Philadelphia.
41. Pal S, Bandyopadhyay S, Ray S. Evolutionary computation in bioinformatics: a review. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 2006 36(5):601-615.
42. Noman N, Iba H. Inference of gene regulatory networks using s-system and differential evolution. GECCO '05: Proceedings of the 2005 conference on Genetic and evolutionary computation 2005, 439-446. New York, NY, USA: ACM.
43. Streichert F, Ulmer H. JavaEvA - A Java Framework for Evolutionary Algorithms. Technical Report WSI-2005-06 2005, Centre for Bioinformatics Tübingen, University of Tübingen.
44. Savageau MA. Introduction to S-systems and the underlying power-law formalism. Elsevier 1988,
45. Shin A, Iba H. Construction of genetic network using evolutionary algorithm and combined fitness function. Genome Inform Ser Workshop Genome Inform 2003, 14:94-103.
46. Mitchel T. Machine Learning 1997, McGraw-Hill Science/Engineering/Math.
47. Koduru P, Das S, Welch S, Roe JL. Fuzzy Dominance Based Multi-objective GA-Simplex Hybrid Algorithms Applied to Gene Network Models. Genetic and Evolutionary Computation - GECCO 2004 2004, 356-367.
48. Kimura S, Hatakeyama M, Konagaya A. Inference of S-system models of genetic networks using a genetic local search. Evolutionary Computation, 2003. CEC '03. The 2003 Congress on 2003 1:631-638. Vol.1.
49. Marbach D, Schaffter T, Mattiussi C, Floreano D. Generating realistic in silico gene networks for performance assessment of reverse engineering methods. Journal of Computational Biology 2009, 16(2):229-239. 10.1089/cmb.2008.09TT, 19183003.
50. Nacher J, Akutsu T. Sensitivity of the power-law exponent in gene expression distribution to mRNA decay rate. Physics Letters A 2006, 360:174-178.
51. McGill R, Tukey JW, Larsen WA. Variations of box plots. The American Statistician 1978, 32:12-16.
52. Wessa P. Notched Boxplots (v1.0.5) in Free Statistics Software (v1.1.23-r4). 2008, , http://www.wessa.net/rwasp_notchedbox1.wasp/
53. Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D, Futcher B. Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization. Molecular Biology of the Cell 1998, 9(12):3273-3297. 25624, 9843569.
54. Aoki-Kinoshita KF, Kanehisa M. Gene annotation and pathway mapping in KEGG. Methods in molecular biology (Clifton, N.J.) 2007, 396:71-91. full_text, 18025687.
55. Chen KC, Csikasz-Nagy A, Gyorffy B, Val J, Novak B, Tyson JJ. Kinetic Analysis of a Molecular Model of the Budding Yeast Cell Cycle. Mol Biol Cell 2000, 11:369-391. 14780, 10637314.
56. Oshima Y, Ogawa N, Harashima S. Regulation of phosphatase synthesis in Saccharomyces cerevisiae - a review. Gene 1996, 179:171-177. 10.1016/S0378-1119(96)00425-8, 8955644.
57. Sakamoto E, Iba H. Inferring a System of Differential Equations for a Gene regulatory network by using Genetic Programming. Proceedings of Congress on Evolutionary Computation 2001, 720-726.
58. Stekel D. Microarray Bioinformatics 2003, Cambridge, Cambridge University Press.
59. Bernard A, Hartemink AJ. Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data. Proceedings of Pacific Symposium on Biocomputing 2005, 459-470. Duke University, Dept of Computer Science, Durham, NC 27708, USA.: World Scientific, Altman RB, Jung TA, Klein TE, Dunker KA, Hunter L.
60. Spieth C, Streichert F, Speer N, Zell A. Clustering-based Approach to Identify Solutions for the Inference of Regulatory Networks. Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005) 2005, 1:660-667.