Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Reverse engineering of gene regulatory networks

Reverse engineering of gene regulatory networks

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Systems Biology — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Systems biology is a multi-disciplinary approach to the study of the interactions of various cellular mechanisms and cellular components. Owing to the development of new technologies that simultaneously measure the expression of genetic information, systems biological studies involving gene interactions are increasingly prominent. In this regard, reconstructing gene regulatory networks (GRNs) forms the basis for the dynamical analysis of gene interactions and related effects on cellular control pathways. Various approaches of inferring GRNs from gene expression profiles and biological information, including machine learning approaches, have been reviewed, with a brief introduction of DNA microarray experiments as typical tools for measuring levels of messenger ribonucleic acid (mRNA) expression. In particular, the inference methods are classified according to the required input information, and the main idea of each method is elucidated by comparing its advantages and disadvantages with respect to the other methods. In addition, recent developments in this field are introduced and discussions on the challenges and opportunities for future research are provided.

References

    1. 1)
      • J. Yu , V.A. Smith , P.P. Wang , A.J. Hartemink , E.D. Jarvis . Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics , 3594 - 3603
    2. 2)
      • B.N. Kholodenko , A. Kiyatkin , F.J. Bruggeman , E. Sontag , H.V. Westerhoff , J.B. Hoek . Untangling the wires: a strategy to trace functional interactions in signaling and gene networks. Proc. Natl. Acad. Sci. USA , 12841 - 12846
    3. 3)
      • H. Binder , S. Preibisch , T. Kirsten . Base pair interactions and hybridization isotherms of matched and mismatched oligonucleotide probes on microarrays. Langmuir , 9287 - 9302
    4. 4)
      • B.A. Sokhansanj , J.P. Fitch , J.N. Quong , A.A. Quong . Linear fuzzy gene network models obtained from microarray data by exhaustive search. BMC Bioinform.
    5. 5)
      • P. Toronen , M. Kolehmainen , G. Wong , E. Castren . Analysis of gene expression data using self-organizing maps. FEBS Lett. , 142 - 146
    6. 6)
      • X. Wang , S. Ghosh , S.W. Guo . Quantitative quality control in microarray image processing and data acquisition. Nucleic Acids Res. , pp. E75 - E85
    7. 7)
      • H. De Jong , J. Gouzé , C. Hernandez , M. Page , T. Sari , J. Geiselmann . Qualitative simulation of genetic regulatory networks using piecewise-linear models. Bull. Math. Biol. , 301 - 340
    8. 8)
      • S.R. Eddy . What is Bayesian statistics?. Nat. Biotechnol. , 1177 - 1178
    9. 9)
      • N.L. van Hal , O. Vorst , A.M. van Houwelingen , E.J. Kok , A. Peijnenburg , A. Aharoni , A.J. van Tunen , J. Keijer . The application of DNA microarrays in gene expression analysis. J. Biotechnol. , 271 - 280
    10. 10)
      • E. Segal , M. Shapira , A. Regev , D. Pe'er , D. Botstein , D. Koller , N. Friedman . Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. , 166 - 176
    11. 11)
    12. 12)
      • F. Gao , B.C. Foat , H.J. Bussemaker . Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data. BMC Bioinform.
    13. 13)
      • B. Efron , R.J. Tibshirani . (1993) An introduction to the bootstrap.
    14. 14)
      • M.L. Bulyk . Computational prediction of transcription-factor binding site locations. Genome Biol.
    15. 15)
      • R. Laubenbacher , B. Stigler . A computational algebra approach to the reverse engineering of gene regulatory networks. J. Theor. Biol. , 523 - 537
    16. 16)
      • H.J. Bussemaker , H. Li , E.D. Siggia . Building a dictionary for genomes: identification of presumptive regulatory sites by statistical analysis. Proc. Natl. Acad. Sci. USA , 10096 - 10100
    17. 17)
      • G.D. Stormo , T.D. Schneider , L. Gold , A. Ehrenfeucht . Use of the ‘Perceptron’ algorithm to distinguish translational initiation sites. E. coli', Nucleic Acids Res. , 2997 - 3011
    18. 18)
      • M. Gustafsson , M. Hornquist , A. Lombardi . Constructing and analyzing a large-scale gene-to-gene regulatory network – lasso-constrained inference and biological validation. IEEE/ACM Trans. Comput. Biol. Bioinform. , 254 - 261
    19. 19)
      • P. D'Haeseleer , S. Liang , R. Somogyi . Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics , 707 - 726
    20. 20)
    21. 21)
    22. 22)
      • J. Kim , D.G. Bates , I. Postlethwaite , P. Heslop-Harrison , K.H. Cho . Least-squares methods for identifying biochemical regulatory networks from noisy measurements. BMC Bioinform.
    23. 23)
      • S.H. Jung , K.-H. Cho . (2005) Identification of gene interaction networks based on evolutionary computation.
    24. 24)
      • Göransson, L., Koski, T.: `Using a dynamic Bayesian network to learn genetic interactions', Technical Report, 2002, available online at: http://www.mai.liu.se/~tikos/dynbayesian.pdf. Accessed 16th April 2007.
    25. 25)
      • j. Faith , T.S. Gardner . Reverse-engineering transcription control networks. Phys. Life Rev. , 65 - 88
    26. 26)
      • T.S. Gardner , D. di Bernardo , D. Lorenz , J.J. Collins . Inferring genetic networks and identifying compound mode of action via expression profiling. Science , 102 - 105
    27. 27)
      • J.H. Holland . (1975) Adaptation in natural and artificial systems.
    28. 28)
      • M. Andrec , B.N. Kholodenko , R.M. Levy , E. Sontag . Inference of signaling and gene regulatory networks by steady-state perturbation experiments: structure and accuracy. J. Theor. Biol. , 427 - 441
    29. 29)
      • A. Silvescu , V. Honavar . Temporal Boolean network models of genetic networks and their inference from gene expression time series. Complex Syst. , 54 - 75
    30. 30)
      • S. Ando , E. Sakamoto , H. Iba . Evolutionary modeling and inference of gene network. Inform. Sci. , 237 - 259
    31. 31)
      • B. Ren , F. Robert , J.J. Wyrick , O. Aparicio , E.G. Jennings , I. Simon , J. Zeitlinger , J. Schreiber , N. Hannett , E. Kanin , T.L. Volkert , C.J. Wilson , S.P. Bell , R.A. Young . Genome-wide location and function of DNA binding proteins. Science , 2306 - 2309
    32. 32)
      • K.-H. Cho , H.-S. Choi , S.-M. Choo . Unraveling the functional interaction structure of a biomolecular network through alternate perturbation of initial conditions. J. Biochem. Biophys. Methods
    33. 33)
      • H. de Jong . Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. , 67 - 103
    34. 34)
      • I.M. Ong , J.D. Glasner , D. Page . Modelling regulatory pathways in E. coli from time series expression profiles. Bioinformatics , pp. S241 - S248
    35. 35)
      • E.P. van Someren , L.F. Wessels , E. Backer , M.J. Reinders . Genetic network modeling. Pharmacogenomics , 507 - 525
    36. 36)
      • J. Ihmels , G. FrieDlander , S. Bergmann . Revealing modular organization in the yeast transcriptional network. Nat. Genet. , 370 - 377
    37. 37)
      • T.E. Ideker , V. Thorsson , R.M. Karp . Discovery of regulatory interactions through perturbation: inference and experimental design. Pac. Symp. Biocomput. , 305 - 316
    38. 38)
      • E.P. van Someren , L.F. Wessels , M.J. Reinders . Linear modeling of genetic networks from experimental data. Proc. Int. Conf. Intell. Syst. Mol. Biol. , 355 - 366
    39. 39)
      • D.C. Weaver , C.T. Workman , G.D. Stormo . Modeling regulatory networks with weight matrices. Pac. Symp. Biocomput. , 112 - 123
    40. 40)
    41. 41)
      • J. Vohradsky . Neural model of the genetic network. J. Biol. Chem. , 36168 - 36173
    42. 42)
      • P. D'Haeseleer , X. Wen , S. Fuhrman , R. Somogyi . Linear modeling of mRNA expression levels during CNS development and injury. Pac. Symp. Biocomput. , 41 - 52
    43. 43)
      • T. Chen , H.L. He , G.M. Church . Modeling gene expression with differential equations. Pac. Symp. Biocomput. , 29 - 40
    44. 44)
      • X.W. Chen , G. Anantha , X. Wang . An effective structure learning method for constructing gene networks. Bioinformatics , 1367 - 1374
    45. 45)
      • Z. Li , C. Chan . Inferring pathways and networks with a Bayesian framework. FASEB J. , 746 - 748
    46. 46)
      • W. Ching , E. Fung , M. Ng , T. Akutsu . On construction of stochastic genetic networks based on gene expression sequences. Int. J. Neural Syst. , 297 - 310
    47. 47)
      • M.J. de Hoon , S. Imoto , K. Kobayashi , N. Ogasawara , S. Miyano . Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations. Pac. Symp. Biocomput. , 17 - 28
    48. 48)
      • Y.F. Leung , D. Cavalieri . Fundamentals of cDNA microarray data analysis. Trends Genet. , 649 - 659
    49. 49)
      • Liu, T.-F., Sung, W.-K., Mittal, A.: `Learning multi-time delay gene network using Bayesian network framework', Proc. 16th IEEE Int. Conf. on Tools with Artificial Intelligence, 2004, p. 640–645.
    50. 50)
      • Y.H. Yang , T. Speed . Design issues for cDNA microarray experiments. Nat. Rev. Genet. , 579 - 588
    51. 51)
      • J. Qian , J. Lin , N.M. Luscombe , H. Yu , M. Gerstein . Prediction of regulatory networks: genome-wide identification of transcription factor targets from gene expression data. Bioinformatics , 1917 - 1926
    52. 52)
      • H. Schmidt , K.-H. Cho , E.W. Jacobsen . Identification of small scale biochemical networks based on general type system perturbations. FEBS J. , 2141 - 2151
    53. 53)
      • A. Brazma , I. Jonassen , J. Vilo , E. Ukkonen . Predicting gene regulatory elements in silico on a genomic scale. Genome Res. , 1202 - 1215
    54. 54)
      • I. Shmulevich , E.R. Dougherty , S. Kim , W. Zhang . Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics , 261 - 274
    55. 55)
      • R.W. Yeung . (2002) A first course in information theory.
    56. 56)
      • L.R. Yu , L.B. Luo . The generalization of the Chinese remainder theorem. Acta Math. Sin. , 531 - 538
    57. 57)
      • van Someren, E.P., Wessels, L.F., Reinders, M.J., Backer, E.: `Searching for limited connectivity in genetic network models', Paper Presented at the Proceedings of the International Conf. on Systems Biology, 2001, Pasadena, CA.
    58. 58)
    59. 59)
      • S. Kimura , K. Ide , A. Kashihara , M. Kano , M. Hatakeyama , R. Masui , N. Nakagawa , S. Yokoyama , S. Kuramitsu , A. Konagaya . Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm. Bioinformatics , 1154 - 1163
    60. 60)
      • L. Davis . (1991) Handbook of genetic algorithms.
    61. 61)
      • Y. Chen , E.R. Dougherty , M. Bittner . Ratio-based decisions and quantitative analysis of cDNA microarrays images. Biomed. Opt. , 313 - 314
    62. 62)
      • F.P. Roth , J.D. Hughes , P.W. Estep , G.M. Church . Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat. Biotechnol. , 939 - 945
    63. 63)
      • N.K. Kasabov . Knowledge-based neural networks for gene expression data analysis modelling and profile discovery. Biosilico , 253 - 261
    64. 64)
      • B.S. Stigler . (2005) Algebra approach to reverse engineering with application to biochemical networks.
    65. 65)
      • K.H. Cho , S.Y. Shin , S.M. Choo . Unravelling the functional interaction structure of a cellular network from temporal slope information of experimental data. FEBS J. , 3950 - 3959
    66. 66)
      • D. Heckerman . (1998) A tutorial on learning with Bayesian networks.
    67. 67)
      • R.J. Lipschutz , S.P.A. Fodor , T.R. Gingeras , D.J. Lockhart . High density synthetic oligonucleotide arrays. Nat. Genet. , 20 - 24
    68. 68)
      • N. Friedman , M. Linial , I. Nachman , D. Pe'er . Using Bayesian networks to analyze expression data. J. Comput. Biol. , 601 - 620
    69. 69)
      • M.S. Scott , T. Perkins , S. Bunnell , F. Pepin , D.Y. Thomas , M. Hallett . Identifying regulatory subnetworks for a set of genes. Mol. Cell Proteomics , 683 - 692
    70. 70)
      • H.J. Bussemaker , H. Li , E.D. Siggia . Regulatory element detection using a probabilistic segmentation model. Proc. Int. Conf. Intell. Syst. Mol. Biol. , 67 - 74
    71. 71)
      • S. Tavazoie , J.D. Hughes , M.J. Campbell , R.J. Cho , G.M. Church . Systematic determination of genetic network architecture. Nat. Genet. , 281 - 285
    72. 72)
      • C. Soule . Mathematical approaches to differentiation and gene regulation. CR Biol. , 13 - 20
    73. 73)
    74. 74)
    75. 75)
      • A. de la Fuente , D.P. Makhecha . Unravelling gene networks from noisy under-determined experimental perturbation data. Syst. Biol. (Stevenage) , 257 - 262
    76. 76)
      • S.R. Eddy . Profile hidden Markov models. Bioinformatics , 755 - 763
    77. 77)
      • Z. Bar-Joseph , G.K. Gerber , T.I. Lee , N.J. Rinaldi , J.Y. Yoo , F. Robert , D.B. Gordon , E. Fraenkel , T.S. Jaakkola , R.A. Young , D.K. Gifford . Computational discovery of gene modules and regulatory networks. Nat. Biotechnol. , 1337 - 1342
    78. 78)
      • Zheng, Y., Kwoh, C.K.: `Reconstruction Boolean networks from noisy gene expression data', Paper Presented at the International Conf. on Control, Automation, Robotics and Vision, 2004.
    79. 79)
      • D. Di Bernardo , T.S. Gardner , J.J. Collins . Robust identification of large genetic networks. Pac. Symp. Biocomput. , 486 - 497
    80. 80)
      • M. Sugimoto , S. Kikuchi , M. Tomita . Reverse engineering of biochemical equations from time-course data by means of genetic programming. Biosystems , 155 - 164
    81. 81)
      • R. Eriksson , B. Olsson . Adapting genetic regulatory models by genetic programming. Biosystems , 217 - 227
    82. 82)
      • G.D. Stormo . DNA binding sites: representation and discovery. Bioinformatics , 16 - 23
    83. 83)
      • M.K. Kerr , G.A. Churchill . Statistical design and the analysis of gene expression microarray data. Genet. Res. , 123 - 128
    84. 84)
      • J. Huang , H. Shimizu , S. Shioya . Clustering gene expression pattern and extracting relationship in gene network based on artificial neural networks. J. Biosci. Bioeng. , 421 - 428
    85. 85)
      • T. Akutsu , S. Miyano , S. Kuhara . Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics , 727 - 734
    86. 86)
      • S. Liang , S. Fuhrman , R. Somogyi . Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac. Symp. Biocomput. , 18 - 29
    87. 87)
      • A. Remenyi , H.R. Scholer , M. Wilmanns . Combinatorial control of gene expression. Nat. Struct. Mol. Biol. , 812 - 815
    88. 88)
      • M. Wahde , J. Hertz . Coarse-grained reverse engineering of genetic regulatory networks. Biosystems , 129 - 136
    89. 89)
      • G.F. Cooper , E. Herskovits . A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. , 309 - 347
    90. 90)
      • J.C. Alwine , D.J. Kemp , G.R. Stark . Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes. Proc. Natl. Acad. Sci. USA , 5350 - 5354
    91. 91)
      • R. Guthke , U. Moller , M. Hoffman , F. Thies , S. Topfer . Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection. Bioinformatics , 1626 - 1634
    92. 92)
    93. 93)
      • C.J. Needham , J.R. Bradford , A.J. Bulpitt , D.R. Westhead . Inference in Bayesian networks. Nat. Biotechnol. , 51 - 53
    94. 94)
      • D. Repsilber , H. Liljenstrom , S.G. Andersson . Reverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypotheses. Biosystems , 31 - 41
    95. 95)
      • J. Tegner , M.K. Yeung , J. Hasty , J.J. Collins . Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc. Natl. Acad. Sci. USA , 5944 - 5949
    96. 96)
    97. 97)
      • M. Zou , S.D. Conzen . A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics , 71 - 79
    98. 98)
      • D.E. Goldberg . (1989) Genetic algorithms in search, optimization, and machine learning.
    99. 99)
      • L.F. Wessels , E.P. van Someren , M.J. Reinders . A comparison of genetic network models. Pac. Symp. Biocomput. , 508 - 519
    100. 100)
      • P. Brazhnik , A. de la Fuente , P. Mendes . Gene networks: how to put the function in genomics. Trends Biotechnol. , 467 - 472
    101. 101)
      • K. Sachs , O. Perez , D. Pe'er , D.A. Lauffenburger , G.P. Nolan . Causal protein-signaling networks derived from multiparameter single-cell data. Science , 523 - 529
    102. 102)
      • K.H. Cho , S.M. Choo , P. Wellstead , O. Wolkenhauer . A unified framework for unravelling the functional interaction structure of a biomolecular network based on stimulus-response experimental data. FEBS Lett. , 4520 - 4528
    103. 103)
      • M.A. Black , R.W. Doerge . Calculation of the minimum number of replicate spots required for detection of significant gene expression fold change in microarray experiments. Bioinformatics , 1609 - 1616
    104. 104)
      • N.S. Holter , A. Maritan , M. Cieplak , N.V. Fedoroff , J.R. Banavar . Dynamic modeling of gene expression data. Proc. Natl. Acad. Sci. USA , 1693 - 1698
    105. 105)
      • N. Friedman . Inferring cellular networks using probabilistic graphical models. Science , 799 - 805
    106. 106)
      • M. Takane . (2003) Inference of gene regulatory networks from large scale gene expression data.
    107. 107)
      • L. Gold , D. Brown , Y. He , T. Shtatland , B.S. Singer , Y. Wu . From oligonucleotide shapes to genomic SELEX: novel biological regulatory loops. Proc. Natl. Acad. Sci. USA , 59 - 64
    108. 108)
      • K.H. Cho , J.R. Kim , S. Baek , H.S. Choi , S.M. Choo . Inferring biomolecular regulatory networks from phase portraits of time-series expression profiles. FEBS Lett. , 3511 - 3518
    109. 109)
      • D.J. Duggan , M. Bittner , Y. Chen , P. Meltzer , J.M. Trent . Expression profiling using cDNA microarrays. Nat. Genet. , 10 - 14
    110. 110)
      • M.A. Beer , S. Tavazoie . Predicting gene expression from sequence. Cell , 185 - 198
    111. 111)
      • C. von Mering , M. Huynen , D. Jaeggi , S. Schmidt , P. Bork , B. Snel . STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. , 258 - 261
    112. 112)
      • S.J. Klug , M. Famulok . All you wanted to know about SELEX. Mol. Biol. Rep. , 97 - 107
    113. 113)
      • X. Deng , H. Geng , H. Ali . EXAMINE: a computational approach to reconstructing gene regulatory networks. Biosystems , 125 - 136
    114. 114)
      • G.C. Tseng , M.K. Oh , L. Rohlin , J.C. Liao , W.H. Wong . Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Res. , 2549 - 2557
    115. 115)
      • J. Reinitz , D.H. Sharp . Mechanism of eve stripe formation. Mech. Dev. , 133 - 158
    116. 116)
      • Ciccarese, P., Mazzocchi, S., Ferrazzi, F., Sacchi, L.: `GENIUS: a new tool for gene networks visualization', Paper Presented at the European Conf. on Artificial Intelligence, 2004.
    117. 117)
      • M.K. Stephen Yeung , Tegnér , J.J. Collins . Reverse engineering gene networks using singular value decomposition and robust regression. Proc. Natl. Acad. Sci. USA , 6163 - 6168
    118. 118)
      • R. Elkon , C. Linhart , R. Sharan , R. Shamir , Y. Shiloh . Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells. Genome Res. , 773 - 780
    119. 119)
      • T. Akutsu , S. Miyano , S. Kuhara . Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. Pac. Symp. Biocomput. , 17 - 28
    120. 120)
      • M.S. Dasika , A. Gupta , C.D. Maranas . A mixed integer linear programming (MILP) framework for inferring time delay in gene regulatory networks. Pac. Symp. Biocomput. , 474 - 485
    121. 121)
      • E. Sontag , A. Kiyatkin , B. Kholodenko . Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data. Bioinformatics , 1877 - 1886
    122. 122)
      • J.P. de Magalhaes , O. Toussaint . How bioinformatics can help reverse engineer human aging. Ageing Res. Rev. , 125 - 141
    123. 123)
      • P. Baldi , G.W. Hatfield . (2002) DNA microarrays and gene expression.
    124. 124)
      • M. Xiong , J. Li , X. Fang . Identification of genetic networks. Genetics , 1037 - 1052
    125. 125)
      • H. Li , V. Rhodius , C. Gross , E.D. Siggia . Identification of the binding sites of regulatory proteins in bacterial genomes. Proc. Natl. Acad. Sci. USA , 11772 - 11777
    126. 126)
      • W. Wang , J.M. Cherry , Y. Nochomovitz , E. Jolly , D. Botstein , H. Li . Inference of combinatorial regulation in yeast transcriptional networks: a case study of sporulation. Proc. Natl. Acad. Sci. USA , 1998 - 2003
    127. 127)
      • N. Banerjee , M.Q. Zhang . Identifying cooperativity among transcription factors controlling the cell cycle in yeast. Nucleic Acids Res. , 7024 - 7031
    128. 128)
      • M. Wahde , J. Hertz . Modeling genetic regulatory dynamics in neural development. J. Comput. Biol. , 429 - 442
    129. 129)
      • S. Kim , S. Imoto , S. Miyano . Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Biosystems , 57 - 65
    130. 130)
      • K. Basso , A.A. Margolin , G. Stolovitzky . Reverse engineering of regulatory networks in human B cells. Nat. Genet. , 382 - 390
    131. 131)
      • Y. Pilpel , P. Sudarsanam , G.M. Church . Identifying regulatory networks by combinatorial analysis of promoter elements. Nat. Genet. , 153 - 159
    132. 132)
      • Friedman, N., Murphy, K., Russell, S.: `Learning the structure of dynamic probabilistic networks', Paper Presented at the Fourteenth Conf. on Uncertainty in Artificial Intelligence, 1998, San Francisco, CA.
    133. 133)
      • A.P. Quayle , S. Bullock . Modelling the evolution of genetic regulatory networks. J. Theor. Biol. , 737 - 753
    134. 134)
      • M.A. Savageau . Biochemical systems analysis. II. The steady-state solutions for an n-pool system using a power-law approximation. J. Theor. Biol. , 370 - 379
    135. 135)
      • S.G. Bøtcher , C. Dethlefsen . deal: a package for learning Bayesian networks. J. Stat. Software
    136. 136)
      • A. de la Fuente , P. Brazhnik , P. Mendes . Linking the genes: inferring quantitative gene networks from microarray data. Trends Genet. , 395 - 398
    137. 137)
      • K. Murphy . (2001) An introduction to graphical models.
    138. 138)
      • E.J. Crampin , S. Schnell , P.E. McSharry . Mathematical and computational techniques to deduce complex biochemical reaction mechanisms. Prog. Biophys. Mol. Biol. , 77 - 112
    139. 139)
      • T.I. Lee , N.J. Rinaldi , F. Robert . Transcriptional regulatory networks in Saccharomyces cerevisiae. Science , 799 - 804
    140. 140)
      • J.R. Koza . (1992) Genetic programming: on the programming of computers by means of natural selection.
    141. 141)
      • D. Pe'er , A. Regev , G. Elidan , N. Friedman . Inferring subnetworks from perturbed expression profiles. Bioinformatics , S215 - S224
    142. 142)
      • B.P. Berman , Y. Nibu , B.D. Pfeiffer , P. Tomancak , S.E. Celniker , M. Levine , G.M. Rubin , M.B. Eisen . Exploiting transcription factor binding site clustering to identify cis-regulatory modules involved in pattern formation in the Drosophila genome. Proc. Natl. Acad. Sci. USA , 757 - 762
    143. 143)
      • T. Ideker , V. Thorsson , J.A. Ranish , R. Christmas , J. Buhler , J.K. Eng , R. Bumgarner , D.R. Goodlett , R. Aebersold , L. Hood . Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science , 929 - 934
    144. 144)
      • E. Mjolsness , D.H. Sharp , J. Reinitz . A connectionist model of development. J. Theor. Biol. , 429 - 453
    145. 145)
      • T. Park , S.G. Yi , S. Lee , J.K. Lee . Diagnostic plots for detecting outlying slides in a cDNA microarray experiment. Biotechniques , 463 - 471
    146. 146)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-syb_20060075
Loading

Related content

content/journals/10.1049/iet-syb_20060075
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address