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A Survey of Current Integrative Network Algorithms for Systems Biology

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Systems Biology

Abstract

The goal of systems biology is to gain a more complete understanding of biological systems by viewing all of their components and the interactions between them simultaneously. Until recently, the most complete global view of a biological system was through the use of gene expression or protein-protein interaction data. With the increasing number of high-throughput technologies for measuring genomic, proteomic, and metabolomic data, scientists now have the opportunity to create complex network-based models for drug discovery, protein function annotation, and many other problems. Each technology used to measure a biological system inherently presents a limited view of the system. However, the combination of multiple technologies can provide a more complete picture. Much recent work has studied integrating these heterogeneous data types into single networks. Here we provide a survey of integrative network-based approaches to problems in systems biology. We focus on describing the variety of algorithms used in integrative network inference. Ultimately, the survey of current approaches leads us to the conclusion that there is an urgent need for a standard set of evaluation metrics and data sets in this field.

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Abbreviations

PPI:

Protein-protein interaction

GO:

Gene ontology

TF:

Transcription factor

TFBS:

Transcription factor binding site

eQTL:

Expression quantitative trait locus

References

  1. Schadt EE, Friend SH, Shaywitz DA (2009) A network view of disease and compound screening. Nat Rev Drug Discov 8:286–295

    Article  CAS  PubMed  Google Scholar 

  2. Friedman N, Linial M, Nachman I, Pe’er D (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7:601–620

    Article  CAS  PubMed  Google Scholar 

  3. Rao A, Hero AO, States DJ, Engel JD (2007) Using directed information to build biologically relevant influence networks. Comput Syst Bioinform/Life Sci Soc Comput Syst Bioinform Conf 6:145–156

    Google Scholar 

  4. De Smet R, Marchal K (2010) Advantages and limitations of current network inference methods. Nat Rev Micro 8:717–729

    Google Scholar 

  5. Sharan R, Ulitsky I, Shamir R (2007) Network-based prediction of protein function. Mol Syst Biol 3

    Google Scholar 

  6. Hecker M, Lambeck S, Toepfer S, Van Someren E, Guthke R (2009) Gene regulatory network inference: data integration in dynamic models: a review. Biosystems 96:86–103

    Article  CAS  PubMed  Google Scholar 

  7. Gitter A, Siegfried Z, Klutstein M, Fornes O, Oliva B et al (2009) Backup in gene regulatory networks explains differences between binding and knockout results. Mol Syst Biol 5

    Google Scholar 

  8. Califano A, Butte A, Friend S, Ideker T, Schadt EE (2011) Integrative network-based association studies: leveraging cell regulatory models in the post-GWAS era. Nat Precedings 10

    Google Scholar 

  9. Bebek G, Koyutürk M, Price ND, Chance MR (2012) Network biology methods integrating biological data for translational science. Briefings Bioinform

    Google Scholar 

  10. Canales R, Luo Y, Willey J, Austermiller B, Barbacioru C et al (2006) Evaluation of dna microarray results with quantitative gene expression platforms. Nat Biotechnol 24:1115–1122

    Article  CAS  PubMed  Google Scholar 

  11. Quackenbush J (2002) Microarray data normalization and transformation. Nat Genet 32:496

    Article  CAS  PubMed  Google Scholar 

  12. Christie KR, Hong EL, Cherry JM (2009) Functional annotations for the Saccharomyces cerevisiae genome: the knowns and the known unknowns. Trends Microbiol 17:286–294

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  13. Hanisch D, Zien A, Zimmer R, Lengauer T (2002) Co-clustering of biological networks and gene expression data. Bioinformatics 18:S145–S154

    Article  PubMed  Google Scholar 

  14. Datta S, Datta S (2003) Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19:459–466

    Article  CAS  PubMed  Google Scholar 

  15. Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci 95:14863–14868

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. Margolin A, Nemenman I, Basso K, Wiggins C, Stolovitzky G et al (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform 7:S7

    Google Scholar 

  17. Meyer P, Lafitte F, Bontempi G (2008) Minet: AR/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinform 9:461

    Google Scholar 

  18. Sen T, Kloczkowski A, Jernigan R (2006) Functional clustering of yeast proteins from the protein-protein interaction network. BMC Bioinform 7:355

    Google Scholar 

  19. Aparicio O, Geisberg JV, Sekinger E, Yang A, Moqtaderi Z et al (2005) Chromatin immunoprecipitation for determining the association of proteins with specific genomic sequences in vivo. In: Ausubel FM et al Current protocols in molecular biology. Chapter 21

    Google Scholar 

  20. Jansen R (2001) Genetical genomics: the added value from segregation. Trends Genet 17:388–391

    Article  CAS  PubMed  Google Scholar 

  21. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H et al (2000) Gene Ontology: tool for the unification of biology. Nat Genet 25:25–29

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acid Res 40:D109–D114

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Keseler IM, Collado-Vides J, Gama-Castro S, Ingraham J, Paley S et al (2005) EcoCyc: a comprehensive database resource for Escherichia coli. Nucleic Acids Res 33

    Google Scholar 

  24. Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R et al (2000) Functional discovery via a compendium of expression profiles. Cell 102:109–126

    Article  CAS  PubMed  Google Scholar 

  25. Steuer R, Kurths J, Daub CO, Weise J, Selbig J (2002) The mutual information: detecting and evaluating dependencies between variables. Bioinformatics 18:S231–S240

    Article  PubMed  Google Scholar 

  26. de Matos Simoes R, Emmert-Streib F (2011) Influence of statistical estimators of mutual information and data heterogeneity on the inference of gene regulatory networks. PLoS ONE 6:e29279

    Google Scholar 

  27. Mason M, Fan G, Plath K, Zhou Q, Horvath S (2009) Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells. BMC Genomics 10:327

    Google Scholar 

  28. Zhou X, Kao MCC, Hung W (2002) Transitive functional annotation by shortest-path analysis of gene expression data. Proc Natl Acad Sci U S A 99:12783–12788

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  29. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Series B (Methodol):267–288

    Google Scholar 

  30. Li C, Li H (2008) Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics 24:1175–1182

    Article  CAS  PubMed  Google Scholar 

  31. Friedman J, Hastie T, Tibshirani R (2008) Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9:432–441

    Article  PubMed Central  PubMed  Google Scholar 

  32. Shimamura T, Imoto S, Yamaguchi R, Miyano S (2007) Weighted lasso in graphical gaussian modeling for large gene network estimation based on microarray data. Genome Inform 19:142–153

    Article  CAS  PubMed  Google Scholar 

  33. Gustafsson M, Hornquist M, Lombardi A (2005) Constructing and analyzing a large-scale gene-to-gene regulatory network lasso-constrained inference and biological validation. IEEE/ACM Trans Comput Biol Bioinform 2:254–261

    Article  CAS  PubMed  Google Scholar 

  34. Li W, Zhang S, Liu C, Zhou X (2012) Identifying multi-layer gene regulatory modules from multi-dimensional genomic data. Bioinformatics on line

    Google Scholar 

  35. Li S, Hsu L, Peng J, Wang P (2011) Bootstrap inference for network construction. Arxiv, preprint arXiv:11115028

    Google Scholar 

  36. Needham CJ, Bradford JR, Bulpitt AJ, Westhead DR (2007) A primer on learning in Bayesian networks for computational biology. PLoS Comput Biol 3:e129

    Google Scholar 

  37. Maxwell Chickering D, Heckerman D (1997) Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables. Mach Learn 29:181–212

    Article  Google Scholar 

  38. Heckerman D (2008) A tutorial on learning with Bayesian networks. Innovations in Bayesian networks, pp 33–82

    Google Scholar 

  39. Zhu J, Zhang B, Smith EN, Drees B, Brem RB et al (2008) Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat Genet 40:854–861

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  40. Hartemink AJ, Gifford DK, Jaakkola TS, Young RA (2002) Combining location and expression data for principled discovery of genetic regulatory network models. Pacific Symp Biocomput:437–449

    Google Scholar 

  41. Tamada Y, Kim S, Bannai H, Imoto S, Tashiro K et al (2003) Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection. Bioinformatics 19:2

    Article  Google Scholar 

  42. Imoto S, Higuchi T, Goto T, Tashiro K, Kuhara S et al (2003) Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. Proc IEEE Comput Soc Bioinform Conf 2:104–113

    PubMed  Google Scholar 

  43. Doss S, Schadt EE, Drake TA, Lusis AJ (2005) Cis-acting expression quantitative trait loci in mice. Genome Res 15:681–691

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  44. Wainwright M, Ravikumar P, Lafferty J (2007) High-dimensional graphical model selection using \(l~\)1-regularized logistic regression. In: Advances in neural information processing systems vol 19. p 1465

    Google Scholar 

  45. Choi M, Tan V, Anandkumar A, Willsky A (2011) Learning latent tree graphical models. J Mach Learn Res 12:1729–1770

    Google Scholar 

  46. Srebro N (2001) Maximum likelihood bounded tree-width markov networks. In: Proceedings of the 17th conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc, pp 504–511

    Google Scholar 

  47. Friedman N, Nachman I (2000) Gaussian process networks. In: Proceedings of the 16th conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc, pp 211–219

    Google Scholar 

  48. Tu Z, Wang L, Arbeitman MN, Chen T, Sun F (2006) An integrative approach for causal gene identification and gene regulatory pathway inference. Bioinformatics 22:e489–e496

    Article  CAS  PubMed  Google Scholar 

  49. Lee I, Date SV, Adai AT, Marcotte EM (2004) A probabilistic functional network of yeast genes. Science 306:1555–1558

    Article  CAS  PubMed  Google Scholar 

  50. Ernst J, Vainas O, Harbison CT, Simon I, Bar-Joseph Z (2007) Reconstructing dynamic regulatory maps. Mol Syst Biol 3

    Google Scholar 

  51. Deng M, Chen T, Sun F (2004) An integrated probabilistic model for functional prediction of proteins. J Comput Biol 11:463–475

    Article  CAS  PubMed  Google Scholar 

  52. Ucar D, Beyer A, Parthasarathy S, Workman CT (2009) Predicting functionality of protein-DNA interactions by integrating diverse evidence. Bioinformatics 25:i137–144

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  53. Ernst J, Beg QK, Kay KA, Balázsi G, Oltvai ZN et al (2008) A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli. PLoS Comput Biol 4:e1000044

    Google Scholar 

  54. Hwang D, Rust AG, Ramsey S, Smith JJ, Leslie DM et al (2005) A data integration methodology for systems biology. Proc Natl Acad Sci U S A 102:17296

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  55. modENCODE Consortium, Roy S, Ernst J, Kharchenko PV, Kheradpour P et al (2010) Identification of functional elements and regulatory circuits by Drosophila modENCODE. Science (New York) 330:1787–1797

    Google Scholar 

  56. Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A et al (2010) The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acid Res 39:D561–D568

    Article  PubMed Central  PubMed  Google Scholar 

  57. Davis DA, Chawla NV (2011) Exploring and exploiting disease interactions from multi-relational gene and phenotype networks. PLoS ONE 6:e22670

    Google Scholar 

  58. Segal MR, Dahlquist KD, Conklin BR (2003) Regression approaches for microarray data analysis. J Comput Biol 10:961–980

    Article  CAS  PubMed  Google Scholar 

  59. Kim H, Hu W, Kluger Y (2006) Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae. BMC Bioinform 7:165

    Google Scholar 

  60. Gao F, Foat B, Bussemaker H (2004) Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data. BMC Bioinform 5:31

    Google Scholar 

  61. Luscombe NM, Madan Babu M et al (2004) Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431:308–312

    Article  CAS  PubMed  Google Scholar 

  62. Tanay A, Sharan R, Kupiec M, Shamir R (2004) Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Proc Natl Acad Sci U S A 101:2981–2986

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  63. Lemmens K, De Bie T, Dhollander T, De Keersmaecker S, Thijs I et al (2009) DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli. Genome Biol 10:R27

    Google Scholar 

  64. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabási AL (2000) The large-scale organization of metabolic networks. Nature 407:651–654

    Article  CAS  PubMed  Google Scholar 

  65. van Noort V, Snel B, Huynen MA (2004) The yeast coexpression network has a small-world, scale-free architecture and can be explained by a simple model. EMBO Rep 5:280–284

    Article  PubMed Central  PubMed  Google Scholar 

  66. Yip A, Horvath S (2007) Gene network interconnectedness and the generalized topological overlap measure. BMC Bioinform 8:22

    Google Scholar 

  67. Clauset A, Shalizi CR, Newman MEJ (2009) Power-law distributions in empirical data. SIAM Rev 51:661

    Google Scholar 

  68. Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D et al (2010) Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci 107:6286–6291

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA

    Andrew K. Rider, Nitesh V. Chawla & Scott J. Emrich

Authors
  1. Andrew K. Rider
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  2. Nitesh V. Chawla
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  3. Scott J. Emrich
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Corresponding author

Correspondence to Nitesh V. Chawla .

Editor information

Editors and Affiliations

  1. Chemical and Biological Engineering, Vanderbilt University, Nashville, TN, USA

    Aleš Prokop

  2. Research Group on Process Network Engineering, Kaposvár University, Kaposvár, Hungary

    Béla Csukás

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Rider, A.K., Chawla, N.V., Emrich, S.J. (2013). A Survey of Current Integrative Network Algorithms for Systems Biology. In: Prokop, A., Csukás, B. (eds) Systems Biology. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6803-1_17

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