Systems Biology Approaches for Understanding Genome Architecture

  • Sven Sewitz
  • Karen LipkowEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1431)


The linear and three-dimensional arrangement and composition of chromatin in eukaryotic genomes underlies the mechanisms directing gene regulation. Understanding this organization requires the integration of many data types and experimental results. Here we describe the approach of integrating genome-wide protein–DNA binding data to determine chromatin states. To investigate spatial aspects of genome organization, we present a detailed description of how to run stochastic simulations of protein movements within a simulated nucleus in 3D. This systems level approach enables the development of novel questions aimed at understanding the basic mechanisms that regulate genome dynamics.

Key words

Genome organization Systems biology Stochastic spatial simulations Hidden Markov models Chromatin states 



We would like to thank Guillaume Filion for providing the HMMt package and for advice on using it, Jeremy Bancroft for work on the implementation of HMMt to work with ChIP-chip data, and Hugo Schmidt and Zsuzsanna Sükösd Etches for their Smoldyn models of the nucleus. We thank Steve Andrews for development and continued support of Smoldyn, some of the cited code snippets, and many fruitful discussions over the years.

SS was supported by the Biotechnology and Biological Sciences Research Council UK grant BBS/E/B/000C0405; KL was supported by a Royal Society University Research Fellowship and a Microsoft Research Faculty Fellowship.


  1. 1.
    Ernst J, Kellis M (2015) Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat Biotechnol 33(4):364–376CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Cavalli G, Misteli T (2013) Functional implications of genome topology. Nat Struct Mol Biol 20(3):290–299CrossRefPubMedGoogle Scholar
  3. 3.
    Polak P, Karlić R, Koren A, Thurman R, Sandstrom R, Lawrence MS et al (2015) Cell-of-origin chromatin organization shapes the mutational landscape of cancer. Nature 518(7539):360–364CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Farh KK-H, Marson A, Zhu J, Kleinewietfeld M, Housley WJ, Beik S et al (2015) Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518(7539):337–343CrossRefPubMedGoogle Scholar
  5. 5.
    Sanyal A, Lajoie BR, Jain G, Dekker J (2013) The long-range interaction landscape of gene promoters. Nature 489(7414):109–113CrossRefGoogle Scholar
  6. 6.
    Phillips-Cremins JE, Sauria MEG, Sanyal A, Gerasimova TI, Lajoie BR, Bell JSK et al (2013) Architectural protein subclasses shape 3D organization of genomes during lineage commitment. Cell 153(6):1281–1295CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    McCord RP, Nazario-Toole A, Zhang H, Chines PS, Zhan Y, Erdos MR et al (2013) Correlated alterations in genome organization, histone methylation, and DNA-lamin A/C interactions in Hutchinson-Gilford progeria syndrome. Genome Res 23(2):260–269CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Robinson PJJ, Fairall L, Huynh VAT, Rhodes D (2006) EM measurements define the dimensions of the “30-nm” chromatin fiber: evidence for a compact, interdigitated structure. Proc Natl Acad Sci U S A 103(17):6506–6511CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Nishino Y, Eltsov M, Joti Y, Ito K, Takata H, Takahashi Y et al (2012) Human mitotic chromosomes consist predominantly of irregularly folded nucleosome fibres without a 30-nm chromatin structure. EMBO J 31(7):1644–1653CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Cairns BR (2009) The logic of chromatin architecture and remodelling at promoters. Nature 461(7261):193–198CrossRefPubMedGoogle Scholar
  11. 11.
    modENCODE Consortium, Roy S, Ernst J, Kharchenko PV, Kheradpour P, Nègre N et al (2010) Identification of functional elements and regulatory circuits by Drosophila modENCODE. Science 330(6012):1787–1797CrossRefGoogle Scholar
  12. 12.
    Kharchenko PV, Alekseyenko AA, Schwartz YB, Minoda A, Riddle NC, Ernst J et al (2011) Comprehensive analysis of the chromatin landscape in Drosophila melanogaster. Nature 471(7339):480–485CrossRefPubMedGoogle Scholar
  13. 13.
    Ernst J, Kheradpour P, Mikkelsen TS, Shoresh N, Ward LD, Epstein CB et al (2011) Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473(7345):43–49CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Ram O, Goren A, Amit I, Shoresh N, Yosef N, Ernst J et al (2011) Combinatorial patterning of chromatin regulators uncovered by genome-wide location analysis in human cells. Cell 147(7):1628–1639CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Larson JL, Yuan G-C (2012) Chromatin states accurately classify cell differentiation stages. PLoS One 7(2), e31414CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Zerbino DR, Wilder SP, Johnson N, Juettemann T, Flicek PR (2015) The Ensembl regulatory build. Genome Biol 16:56CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    de Graaf CA, van Steensel B (2013) Chromatin organization: form to function. Curr Opin Genet Dev 23(2):185–190CrossRefPubMedGoogle Scholar
  18. 18.
    Filion GJ, van Bemmel JG, Braunschweig U, Talhout W, Kind J, Ward LD et al (2010) Systematic protein location mapping reveals five principal chromatin types in drosophila cells. Cell 143(2):212–224CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Ernst J, Kellis M (2010) Discovery and characterization of chromatin states for systematic annotation of the human genome. Nat Biotechnol 28(8):817–825CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Schoenfelder S, Clay I, Fraser P (2010) The transcriptional interactome: gene expression in 3D. Curr Opin Genet Dev 20(2):127–133CrossRefPubMedGoogle Scholar
  21. 21.
    Schoenfelder S, Sexton T, Chakalova L, Cope NF, Horton A, Andrews S et al (2010) Preferential associations between co-regulated genes reveal a transcriptional interactome in erythroid cells. Nat Genet 42(1):53–61CrossRefPubMedGoogle Scholar
  22. 22.
    Dekker J, Marti-Renom MA, Mirny LA (2013) Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat Rev Genet 14(6):390–403CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Gowers DM, Wilson GG, Halford SE (2005) Measurement of the contributions of 1D and 3D pathways to the translocation of a protein along DNA. Proc Natl Acad Sci U S A 102(44):15883–15888CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Gowers DM, Halford SE (2003) Protein motion from non-specific to specific DNA by three-dimensional routes aided by supercoiling. EMBO J 22(6):1410–1418CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Elf J, Li G-W, Xie XS (2007) Probing transcription factor dynamics at the single-molecule level in a living cell. Science 316(5828):1191–1194CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Berg OG, von Hippel PH (1985) Diffusion-controlled macromolecular interactions. Annu Rev Biophys Biophys Chem 14:131–160CrossRefPubMedGoogle Scholar
  27. 27.
    Isaacson SA, Larabell CA, Le Gros MA, McQueen DM, Peskin CS (2013) The influence of spatial variation in chromatin density determined by X-ray tomograms on the time to find DNA binding sites. Bull Math Biol 75(11):2093–2117CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Ando T, Skolnick J (2014) Sliding of proteins non-specifically bound to DNA: Brownian dynamics studies with coarse-grained protein and DNA models. PLoS Comp Biol 10(12), e1003990CrossRefGoogle Scholar
  29. 29.
    Schmidt HG, Sewitz S, Andrews SS, Lipkow K (2014) An integrated model of transcription factor diffusion shows the importance of intersegmental transfer and quaternary protein structure for target site finding. PLoS One 9(10), e108575CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Veksler A, Kolomeisky AB (2013) Speed-selectivity paradox in the protein search for targets on DNA: is it real or not? J Phys Chem B 117(42):12695–12701CrossRefPubMedGoogle Scholar
  31. 31.
    Berg OG, Winter RB, von Hippel PH (1981) Diffusion-driven mechanisms of protein translocation on nucleic acids. 1. Models and theory. Biochemistry 20(24):6929–6948CrossRefPubMedGoogle Scholar
  32. 32.
    Seksek O, Biwersi J, Verkman AS (1997) Translational diffusion of macromolecule-sized solutes in cytoplasm and nucleus. J Cell Biol 138(1):131–142CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Dross N, Spriet C, Zwerger M, Müller G, Waldeck W, Langowski J (2009) Mapping eGFP oligomer mobility in living cell nuclei. PLoS One 4(4), e5041CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W et al (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43(7):47CrossRefGoogle Scholar
  35. 35.
    Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10):R80CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Smyth GK (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:Art. 3, 29 pagesGoogle Scholar
  37. 37.
    Baum LE (1972) An equality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes. Inequalities 3:1–8Google Scholar
  38. 38.
    Miklós I, Meyer IM (2005) A linear memory algorithm for Baum-Welch training. BMC Bioinformatics 6:231CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Viterbi A (1967) Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans Inform Theory 13(2):260–269CrossRefGoogle Scholar
  40. 40.
    Andrews SS, Bray D (2004) Stochastic simulation of chemical reactions with spatial resolution and single molecule detail. Phys Biol 1(3-4):137–151CrossRefPubMedGoogle Scholar
  41. 41.
    Andrews SS, Addy NJ, Brent R, Arkin AP (2010) Detailed simulations of cell biology with Smoldyn 2.1. PLoS Comp Biol 6(3):e1000705CrossRefGoogle Scholar
  42. 42.
    Andrews SS (2012) Spatial and stochastic cellular modeling with the Smoldyn simulator. In: van Helden et al (eds) Bacterial molecular networks: methods and protocols. Methods Mol Biol 804:519–542CrossRefPubMedGoogle Scholar
  43. 43.
    DePristo MA, Chang L, Vale RD, Khan SM, Lipkow K (2009) Introducing simulated cellular architecture to the quantitative analysis of fluorescent microscopy. Prog Biophys Mol Biol 100(1-3):25–32CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Sewitz S, Lipkow K (2013) Simulating bacterial chemotaxis at high spatio-temporal detail. Curr Chem Biol 7(3):214–223CrossRefGoogle Scholar
  45. 45.
    Lipkow K, Andrews SS, Bray D (2005) Simulated diffusion of phosphorylated CheY through the cytoplasm of Escherichia coli. J Bacteriol 187:45–53CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Hoffmann M, Schwarz US (2014) Oscillations of Min-proteins in micropatterned environments: a three-dimensional particle-based stochastic simulation approach. Soft Matter 10:2388CrossRefPubMedGoogle Scholar
  47. 47.
    Zavala E, Marquez-Lago TT (2014) The long and viscous road: uncovering nuclear diffusion barriers in closed mitosis. PLoS Comp Biol 10, e1003725CrossRefGoogle Scholar
  48. 48.
    Singh P, Hockenberry AJ, Tiruvadi V, Meaney DF (2011) Computational investigation of the changing patterns of subtype specific NMDA receptor activation during physiological glutamatergic neurotransmission. PLoS Comp Biol 7:1002106CrossRefGoogle Scholar
  49. 49.
    Robinson M, Andrews SS, Erban R (2015) Multiscale reaction-diffusion simulations with Smoldyn. Bioinformatics 31:2406–2408CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Babraham InstituteNuclear Dynamics ProgrammeCambridgeUK
  2. 2.Cambridge Systems Biology CentreUniversity of CambridgeCambridgeUK

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