Abstract
Owing to the growing knowledge about the cellular molecular network and its alterations in diseases, most of the diseases become considered as “systems distortion of the cellular molecular network”. This view of diseases, which we call “systems pathology”, has brought about a new usage of the disease Omics, that is, to identify the altered molecular network underlying the disease. In this chapter, we discuss the technologies and clinical applications for Omics-based identification of pathophysiological process. In doing so, we classify the methods into two classes: one is a “data-inductive approach” which infers gene regulatory and transcriptional networks by gene expression data from DNA microarrays, and the other is a “knowledge-referenced approach” which combines the differentially expressed genes from gene expression profiles with existing protein interaction networks or literature-curated pathways. Several typical methods such as ARACNe and eQTL are described with their recent clinical applications.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Schena M, Shalon D, Davis RW, and Brown PO. (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–70.
Fodor SP, Rava RP, Huang XC, Pease AC, Holmes CP, and Adams CL. (1993) Multiplexed biochemical assays with biological chips. Nature 364, 555–6.
Xing Y, Kapur K, and Wong WH. (2006) Probe selection and expression index computation of Affymetrix Exon Arrays. PLoS ONE 1, e88.
Tanaka K, Waki H, Ido Y, Akita S, Yoshida Y, and Yoshida T. (1988) Protein and polymer analyses up to m/z 100000 by laser ionization time-of flight mass spectrometry. Rapid Commun Mass Spectrom 2, 151–3.
Hutchens TW, and Yip TT. (1993) New desorption strategies for the mass spectrometric analysis of macromolecules. Rapid Commun Mass Spectrom 7, 576–80.
Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, and Lander ES. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–7.
Sørlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S, Demeter J, Perou CM, Lønning PE, Brown PO, Børresen-Dale AL, and Botstein D. (2003) Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 100, 8418–23.
Tanaka H. (2010) Omics-based medicine and systems pathology Meth Informat Med 49, 173–185.
Liang S, Fuhrman S, and Somogyi R. (1998) Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac Symp Biocomput 3, 18–29.
Friedman N, Linial M, Nachman I, and Pe’er D. (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7, 601–20.
Gardner TS, di Bernardo D, Lorenz D, and Collins JJ. (2003) Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301, 102–5.
Edwards DG. (2000) Introduction to Graphical Modelling. Springer Verlag, Heidelberg.
Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, and Califano A. (2005) Reverse engineering of regulatory networks in human B cells. Nat Genet 37, 382–90.
Cooper GF, and Herskovits E. (1992) A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9, 309–47.
Huang DW, Sherman BT, and Lempicki RA. (2009) Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat Protoc 4, 44–57.
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, and Mesirov JP. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102, 15545–50.
Ota MS, Kaneko Y, Kondo K, Ogishima S, Tanaka H, Eto K, and Kondo T. (2009) Combined in silico and in vivo analyses reveal role of Hes1 in taste cell differentiation. PLoS Genet 5, e1000443.
Ishiwata RR, Morioka MS, Ogishima S, and Tanaka H. (2009) BioCichlid: central dogma-based 3D visualization system of time-course microarray data on a hierarchical biological network. Bioinformatics 25, 543–4.
Gilad Y, Rifkin SA, and Pritchar JK. (2003) Revealing the architecture of gene regulation: the promise of eQTL studies. Cell 114, 323–32.
Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, and Califano A. (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7, S7.
Chuang HY, Lee E, Liu YT, Lee D, and Ideker T. (2007) Network-based classification of breast cancer metastasis. Mol Sys Biol 3, 140.
Tanaka S, Mogushi K, Yasen M, Noguchi N, Kudo A, Kurokawa T, Nakamura N, Inazawa J, Tanaka H, and Arii S. (2009) Surgical contribution to recurrence-free survival in patients with macrovascular-invasion-negative hepatocellular carcinoma. J Am Coll Surg 208, 368–74.
Lamb J, Ramaswamy S, Ford HL, Contreras B, Martinez RV, Kittrell FS, Zahnow CA, Patterson N, Golub TR, and Ewen ME. (2003) A mechanism of cyclin D1 action encoded in the patterns of gene expression in human cancer. Cell 114, 323–34.
Goring HH, Curran JE, Johnson MP, Dyer TD, Charlesworth J, Cole SA, Jowett JB, Abraham LJ, Rainwater DL, Comuzzie AG, Mahaney MC, Almasy L, MacCluer JW, Kissebah AH, Collier GR, Moses EK, and Blangero J. (2007) Discovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes. Nat Genet 39, 1208–16.
Carro MS, Lim WK, Alvarez MJ, Bollo RJ, Zhao X, Snyder EY, Sulman EP, Anne SL, Doetsch F, Colman H, Lasorella A, Aldape K, Califano A, and Iavarone A. (2010) The transcriptional network for mesenchymal transformation of brain tumours. Nature 463, 318–25.
Margolin AA, Wang K, Lim WK, Kustagi M, Nemenman I, and Califano A. (2006) Reverse engineering cellular networks. Nat Protoc 1, 662–71.
Robinson RW. (1973) Counting labeled acyclic digraphs, in ‘New directions in the theory of graphs’, F. Haray ed., Academic Press, New York.
Ott S, Imoto S, and Miyano S. (2004) Finding optimal models for small gene networks. Pac Symp Biocomput 9, 557–67.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Tanaka, H., Ogishima, S. (2011). Omics-Based Identification of Pathophysiological Processes. In: Mayer, B. (eds) Bioinformatics for Omics Data. Methods in Molecular Biology, vol 719. Humana Press. https://doi.org/10.1007/978-1-61779-027-0_23
Download citation
DOI: https://doi.org/10.1007/978-1-61779-027-0_23
Published:
Publisher Name: Humana Press
Print ISBN: 978-1-61779-026-3
Online ISBN: 978-1-61779-027-0
eBook Packages: Springer Protocols