Skip to main content
SpringerLink
Log in

Computational Problems in Multi-tissue Models of Health and Disease

  • Review Article
  • Published:
Journal of the Indian Institute of Science Aims and scope

Abstract

A modern development at the interface of computer science and systems biology is being fostered by high-dimensional molecular data emerging on multiple tissues of the same individual collected across large groups of healthy/diseased individuals. We review computational and statistical problems that arise in analyzing such multi-tissue genomic datasets, specifically problems posing new challenges compared to their single-tissue counterparts, such as ones related to missing data imputation, statistical learning of high-dimensional network models capturing gene–gene correlations within/across tissues, and graph algorithms to identify genes clustering across many tissue networks. A recurring research theme is the potential to integrate or pool information from across tissues to enhance power of detecting signals shared across tissues while also accounting for tissue-specific differences. We show how methods harnessing this integrative potential to address multi-tissue problems ranging from correlation/causal network inference to graph algorithms are ushering in an era of integrated, whole-system modeling of life processes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1:
Figure 2:
Figure 3:
Figure 4:

Similar content being viewed by others

References

  1. Allen GI, Tibshirani R (2010) Transposable regularized covariance models with an application to missing data imputation. Ann Appl Stat 4(2):764–790

    Article  Google Scholar 

  2. Allen GI, Tibshirani R (2012) Inference with transposable data: modelling the effects of row and column correlations. J R Stat Soc Ser B (Statistical Methodology) 74(4):721–743

    Article  Google Scholar 

  3. Bickel PJ, Brown JB, Huang H, Li Q (2009) An overview of recent developments in genomics and associated statistical methods. Philos Trans Ser A Math Phys Eng Sci 367(1906):4313–4337

    Article  Google Scholar 

  4. Boccaletti S, Bianconi G, Criado R, del Genio CI, Gómez-Gardeñes J, Romance M, Sendiña-Nadal I, Wang Z, Zanin M (2014) The structure and dynamics of multilayer networks. Phys Rep 544(1):1–122

    Article  Google Scholar 

  5. Bordbar A, Feist AM, Usaite-Black R, Woodcock J, Palsson BO, Famili I (2011) A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology. BMC Syst Biol 5:180

    Article  Google Scholar 

  6. Bordbar A, McCloskey D, Zielinski DC, Sonnenschein N, Jamshidi N, Palsson BO (2015) Personalized Whole-Cell Kinetic Models of Metabolism for Discovery in Genomics and Pharmacodynamics. Cell Syst 1(4):283–292

    Article  Google Scholar 

  7. Coatrieux JL, Frangi AF, Peng GCY, D’Argenio DZ, Marmarelis VZ, Michailov A (2011) Editorial: TBME Letters special issue on multiscale modeling and analysis in computational biology and medicine–part-2. IEEE Trans BioMed Eng 58(12):3434–3439

    Article  Google Scholar 

  8. National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, and Committee on Mathematical Sciences Research for DOE’s Computational Biology (2005) Mathematics and 21st Century Biology. National Academies Press, Washington, DC

  9. Danaher P, Wang P, Witten DM (2014) The joint graphical lasso for inverse covariance estimation across multiple classes. J R Stat Soc Ser B Stat Methodol 76(2):373–397

    Article  Google Scholar 

  10. Didelez V, Sheehan N (2007) Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res 16(4):309–330

    Article  Google Scholar 

  11. Do KT, Kastenmüller G, Mook-Kanamori DO, Yousri NA, Theis FJ, Suhre K, Krumsiek J (2015) Network-based approach for analyzing intra- and interfluid metabolite associations in human blood, urine, and saliva. J Proteome Res 14(2):1183–1194

    Article  Google Scholar 

  12. Dobrin R, Zhu J, Molony C, Argman C, Parrish ML, Carlson S, Allan MF, Pomp D, Schadt EE (2009) Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease. Genome Biol 10(5):R55

    Article  Google Scholar 

  13. Droujinine IA, Perrimon N (2013) Defining the interorgan communication network: systemic coordination of organismal cellular processes under homeostasis and localized stress. Front Cell Infect Microbiol 3:82

    Article  Google Scholar 

  14. Flutre T, Wen X, Pritchard J, Stephens M (2013) A statistical framework for joint eQTL analysis in multiple tissues. PLoS Genet 9(5):e1003486

    Article  Google Scholar 

  15. Friedman J, Hastie T, Tibshirani R (2008) Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3):432–441

    Article  Google Scholar 

  16. de Oliveira Dal’Molin C CG, Quek LE, Saa PA, Nielsen LK (2015) A multi-tissue genome-scale metabolic modeling framework for the analysis of whole plant systems. Front Plant Sci 6:4

    Google Scholar 

  17. Grundberg E, Small KS, Hedman ÃK, Nica AC, Buil A, Keildson S, Bell JT, Yang TP, Meduri E, Barrett A, Nisbett J, Sekowska M, Wilk A, Shin SY, Glass D, Travers M, Min JL, Ring S, Ho K, Thorleifsson G, Kong A, Thorsteindottir U, Ainali C, Dimas AS, Hassanali N, Ingle C, Knowles D, Krestyaninova M, Lowe CE, Di Meglio P, Montgomery SB, Parts L, Potter S, Surdulescu G, Tsaprouni L, Tsoka S, Bataille V, Durbin R, Nestle FO, O’Rahilly S, Soranzo N, Lindgren CM, Zondervan KT, Ahmadi KR, Schadt EE, Stefansson K, Smith GD, McCarthy MI, Deloukas P, Dermitzakis ET, Spector TD (2012) Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat Genet 44(10):1084–1089

    Article  Google Scholar 

  18. GTEx Consortium (2015) Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348(6235):648–660

    Article  Google Scholar 

  19. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, Second Edition, volume (Chapter 17 on Graphical Models). Springer, New York

    Book  Google Scholar 

  20. Huang G-J, Shifman S, Valdar W, Johannesson M, Yalcin B, Taylor MS, Taylor JM, Mott R, Flint J (2009) High resolution mapping of expression QTLs in heterogeneous stock mice in multiple tissues. Genome Res 19(6):1133–1140

    Article  Google Scholar 

  21. Kannan R, Vempala S, Vetta A (2004) On clusterings: good, bad and spectral. J ACM 51(3):497–515

    Article  Google Scholar 

  22. King MR, Diamond SL (2012) Multiscale systems biology: a special issue devoted to understanding biology and medicine across multiple scales. Ann Biomed Eng 40(11):2293–2294

    Article  Google Scholar 

  23. Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA (2014) Multilayer networks. J Complex Netw 2(3):203–271

    Article  Google Scholar 

  24. Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, Geman D, Baggerly K, Irizarry RA (2010) Héctor Corrada Bravo, David Simcha, Benjamin Langmead, W. Evan Johnson, Donald Geman, Keith Baggerly, and Rafael A. Irizarry. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 11(10):733–739

    Article  Google Scholar 

  25. Long Q, Argmann C, Houten SM, Huang T, Peng S, Zhao Y, Tu Z, Zhu J (2016) Inter-tissue coexpression network analysis reveals DPP4 as an important gene in heart to blood communication. Genome Med 8(1):15

    Article  Google Scholar 

  26. Machado D, Herrgård M (2014) Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol 10(4):e1003580

    Article  Google Scholar 

  27. Mazumder R, Hastie T (2012) Exact covariance thresholding into connected components for large-scale graphical lasso. J Mach Learn Res 13:781–794

    Google Scholar 

  28. Mitra K, Carvunis AR, Ramesh SK, Ideker T (2013) Integrative approaches for finding modular structure in biological networks. Nat Rev Genet 14(10):719–732

    Article  Google Scholar 

  29. Narayanan M, Vetta A, Schadt EE, Zhu J (2010) Simultaneous clustering of multiple gene expression and physical interaction datasets. PLoS Comput Biol 6(4):e1000742

    Article  Google Scholar 

  30. Newman M (2010) Networks : an introduction. Oxford University, Oxford

    Book  Google Scholar 

  31. Nyman E, Brännmark C, Palmér R, Brugård J, Nyström FH, Strålfors P, Cedersund G (2011) A hierarchical whole-body modeling approach elucidates the link between in vitro insulin signaling and in vivo glucose homeostasis. J Biol Chem 286(29):26028–26041

    Article  Google Scholar 

  32. Ongen H, Brown AA, Delaneau O, Panousis N, Nica AC, GTEx Consortium, Dermitzakis ET (2016) Estimating the causal tissues for complex traits and diseases. bioRxiv, 074682

  33. Pierson E, Koller D, Battle A, Mostafavi S, Ardlie KG, Getz G, Wright FA, Kellis M, Volpi S, Dermitzakis ET, GTEx Consortium (2015) Sharing and specificity of co-expression networks across 35 human tissues. PLoS Comput Biol 11(5):e1004220

    Article  Google Scholar 

  34. Sharan R, Ideker T (2006) Modeling cellular machinery through biological network comparison. Nat Biotechnol 24(4):427–433

    Article  Google Scholar 

  35. Touloumis A, Marioni JC, Tavaré S (2016) HDTD: analyzing multi-tissue gene expression data. Bioinformatics (Oxford, England) 32(14):2193–2195

    Article  Google Scholar 

  36. Wang J, Gamazon ER, Pierce BL, Stranger BE, Im HK, Gibbons RD, Cox NJ, Nicolae DL, Chen LS (2016) Imputing gene expression in uncollected tissues within and beyond GTEx. Am J Hum Genet 98(4):697–708

    Article  Google Scholar 

  37. Yibo W, Williams EG, Dubuis S, Mottis A, Jovaisaite V, Houten SM, Argmann CA, Faridi P, Wolski W, Kutalik Z, Zamboni N, Auwerx J, Aebersold R (2014) Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population. Cell 158(6):1415–1430

    Article  Google Scholar 

  38. Yang J, Zaitlen NA, Goddard ME, Visscher PM, Price AL (2014) Advantages and pitfalls in the application of mixed model association methods. Nat Genet 46(2):100–106

    Article  Google Scholar 

  39. Yang X, Schadt EE, Wang S, Wang H, Arnold AP, Ingram-Drake L, Drake TA, Lusis AJ (2006) Tissue-specific expression and regulation of sexually dimorphic genes in mice. Genome Res 16(8):995–1004

    Article  Google Scholar 

  40. Zhang B, Tran L, Emilsson V, Zhu J (2016) Characterization of genetic networks associated with Alzheimer’s disease. Methods Mol Biol (Clifton, N.J.) 1303:459–477

    Article  Google Scholar 

  41. Zhang Y, Barocas VH, Berceli SA, Clancy CE, Eckmann DM, Garbey M, Kassab GS, Lochner DR, McCulloch AD, Tran-Son-Tay R, Trayanova NA (2016) Multi-scale modeling of the cardiovascular system: disease development, progression, and clinical intervention. Ann Biomed Eng 44(9):2642–2660

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported in part by the Intramural Research Program of the NIH, NIAID.

Author information

Authors and Affiliations

  1. Systems Genomics and Bioinformatics Unit, Laboratory of Systems Biology, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, 20892, USA

    Manikandan Narayanan

Authors
  1. Manikandan Narayanan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manikandan Narayanan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Narayanan, M. Computational Problems in Multi-tissue Models of Health and Disease. J Indian Inst Sci 97, 325–337 (2017). https://doi.org/10.1007/s41745-017-0040-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41745-017-0040-6

Keywords

Search

Navigation