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Local network-based measures to assess the inferability of different regulatory networks

Local network-based measures to assess the inferability of different regulatory networks

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© The Institution of Engineering and Technology
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The purpose of this study is to compare the inferability of various synthetic as well as real biological regulatory networks. In order to assess differences we apply local network-based measures. That means, instead of applying global measures, we investigate and assess an inference algorithm locally, on the level of individual edges and subnetworks. We demonstrate the behaviour of our local network-based measures with respect to different regulatory networks by conducting large-scale simulations. As inference algorithm we use exemplarily ARACNE. The results from our exploratory analysis allow us not only to gain new insights into the strength and weakness of an inference algorithm with respect to characteristics of different regulatory networks, but also to obtain information that could be used to design novel problem-specific statistical estimators. [Includes supplementary material]

Inspec keywords: bioinformatics; genomics; genetics; inference mechanisms

Other keywords: ARACNE; large-scale simulation; inference algorithm; statistical estimator; inferability assessment; synthetic regulatory network; biological regulatory networks; local network-based measures; exploratory analysis

Subjects: Biology and medical computing; Knowledge engineering techniques; Genomic techniques; Biomolecular interactions, charge transfer complexes

References

    1. 1)
      • M.J. Callow , S. Dudoit , E.L. Gong , T.P. Speed , E.M. Rubin . Microarray expression profiling identifies genes with altered expression in HDL-deficient mice. Genome Res. , 12 , 2022 - 2029
    2. 2)
      • T.V. Van den Bulcke , K. Van Leemput , N.P. van Remortel . SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC Bioinf. , 7 - 43
    3. 3)
      • A. Fersht . (1985) Enzyme structure and mechanism.
    4. 4)
      • D. Hwang , A.G. Rust , S. Ramsey . A data integration methodology for systems biology. Proc. Natl. Acad. Sci. USA , 48 , 17296 - 17301
    5. 5)
      • A.A. Margolin , I. Nemenman , K. Basso . ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinf.
    6. 6)
      • S. Liang , S. Fuhrman , R. Somogyi . Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pacific Symp. Biocomputing , 18 - 29
    7. 7)
      • G. Stolovitzky , D. Monroe , A. Califano . Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference. Ann. N. Y. Acad. Sci. , 1 - 22
    8. 8)
      • N. Friedman . Inferring cellular networks using probabilistic graphical models. Science , 5659 , 799 - 805
    9. 9)
      • H. Hache , H. Lehrach , R. Herwig . Reverse engineering of gene regulatory networks: a comparative study. EURASIP J. Bioinf. Syst. Biol.
    10. 10)
      • G. Altay , F. Emmert-Streib . Revealing differences in gene network inference algorithms on the network-level by ensemble methods. Bioinformatics , 14 , 1738 - 1744
    11. 11)
      • J.H. Hofmeyr , A. Cornish-Bowden . The reversible Hill equation: how to incorporate cooperative enzymes into metabolic models. Comput. Appl. Biosci. , 377 - 385
    12. 12)
      • P.E. Meyer , F. Lafitte , G. Bontempi . Minet: a R/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinf.
    13. 13)
      • N. Soranzo , G. Bianconi , C. Altafini . Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data. Bioinformatics , 13 , 1640 - 1647
    14. 14)
      • J.S. Hamid , P. Hu , N.M. Roslin , V. Ling , C.M.T. Greenwood , J. Beyene . Data integration in genetics and genomics: methods and challenges. Hum. Genomics Proteomics
    15. 15)
      • T.R. Lezon , J.R. Banavar , M. Cieplak , A. Maritan , N.V. Fedoroff . Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns. Proc. Natl. Acad. Sci. USA , 50 , 19033 - 19038
    16. 16)
      • (2008) A language and environment for statistical computing.
    17. 17)
      • C. Olsen , P.E. Meyer , G. Bontempi . On the impact of entropy estimation on transcriptional regulatory network inference based on mutual information. EUROSIP J. Bioinf. Syst. Biol.
    18. 18)
      • Butte, A.J., Kohane, I.S.: `Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements', Pacific Symp. Biocomputing, 2000, 5, p. 415–426.
    19. 19)
      • F. Emmert-Streib . The chronic fatigue syndrome: a comparative pathway analysis. J. Comput. Biol. , 7 , 961 - 972
    20. 20)
      • R. Milo , S. Shen-Orr , S. Itzkovitz , N. Kashtan , D. Chklovskii , U. Alon . Network motifs: simple building blocks of complex networks. Science , 5594 , 824 - 827
    21. 21)
      • M.B. Eisen , P.T. Spellman , P.O. Brown , D. Botstein . Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. , 25 , 14863 - 14868
    22. 22)
      • O.G. Troyanskaya , M.E. Garber , P.O. Brown , D. Botstein , R.B. Altman . Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics , 11 , 1454 - 1461
    23. 23)
      • L. Klebanov , G. Glazko , P. Salzman , A. Yakovlev , Y. Xiao . A multivariate extension of the gene set enrichment analysis. J. Bioinf. Comput Biol. , 5 , 1139 - 1153
    24. 24)
      • H. Hache , C. Wierling , H. Lehrach , R. Herwig . GeNGe: systematic generation of gene regulatory networks. Syst. Biol. , 1205 - 1207
    25. 25)
      • J. Pearl . (1988) Probabilistic reasoning in intelligent systems.
    26. 26)
      • P. Carmona-Saez , R.D. Pascual-Marqui , F. Tirado , J.M. Carazo , A. Pascual-Montano . Biclustering of gene expression data by non-smooth non-negative matrix factorization. BMC Bioinf.
    27. 27)
      • A.L. Barabasi , R. Albert . Emergence of scaling in random networks. Science , 509 - 512
    28. 28)
      • D. Camacho , Licona Paola Vera , P. Mendes , Reinhard Laubenbacher . Comparison of reverse-engineering methods using an in silico network. Ann. N. Y. Acad. Sci. , 73 - 89
    29. 29)
      • S. Shen-Orr , R. Milo , S. Mangan , U. Alon . Network motifs in the transcriptional regulatory network of Escherichia coli. Nat. Genet. , 64 - 68
    30. 30)
      • A.J. Butte , P. Tamayo , D. Slonim , T.R. Golub , I.S. Kohane . Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc. Natl. Acad. Sci. USA , 22 , 12182 - 12186
    31. 31)
      • D. Husmeier . Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics , 17 , 2271 - 2282
    32. 32)
      • A.V. Werhli , M. Grzegorczyk , D. Husmeier . Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks. Bioinformatics , 20 , 2523 - 2531
    33. 33)
      • P. Mendes , W. Sha , K. Ye . Artificial gene networks for objective comparison of analysis algorithms. Bioinformatics , ii122 - ii129
    34. 34)
      • I. Cantone , L. Marucci , F. Iorio . A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell , 172 - 181
    35. 35)
      • D.C. Hoaglin , F. Mosteller , J.W. Tukey . (1983) Understanding robust and exploratory data analysis.
    36. 36)
      • P. Erdös , A. Rényi . On random graphs. Publ. Math. , 290 - 297
    37. 37)
      • Csardi, G., Nepusz, T.: igraph-package for R, 2008.
    38. 38)
      • M. Xiong , J. Li , X. Fang . Identification of genetic networks. Genetics , 1037 - 1052
    39. 39)
      • F. Crick . Central dogma of molecular biology. Nature , 561 - 563
    40. 40)
      • P.E. Meyer , K. Kontos , G. Bontempi . Information-theoretic inference of large transcriptional regulatory networks. EUROSIP J. Bioinf. Syst. Biol.
    41. 41)
      • G. Glazko , F. Emmert-Streib . Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets. Bioinformatics , 18 , 2348 - 2354
    42. 42)
      • M. Bansal , D. di Bernardo . Inference of gene networks from temporal gene expression profiles. IET Syst. Biol. , 5 , 306 - 312
    43. 43)
      • K.C. Liang , X. Wang . Gene regulatory network reconstruction using conditional mutual information. EURASIP J. Bioinf. Syst. Biol.
    44. 44)
      • F. Emmert-Streib , M. Dehmer . (2008) Analysis of microarray data: a network based approach.
    45. 45)
      • T.M. Cover , J.A. Thomas . (1991) Information theory.
    46. 46)
      • D. Marbach , R.J. Prill , T. Schaffter , C. Mattiussi , D. Floreano , G. Stolovitzky . Revealing strengths and weaknesses of methods for gene network inference. Proc. Natl. Aca. Sci. , 14 , 6286 - 6291
    47. 47)
      • A. Subramanian , P. Tamayo , V.K. Mootha . Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA , 43 , 15545 - 15550
    48. 48)
      • H.W. Ma , B. Kumar , U. Ditges , F. Gunzer , J. Buer , A.P. Zeng . An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs. Nucleic Acids Res. , 6643 - 6649
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