Skip to main content
SpringerLink
Log in

Rational Design and Methods of Analysis for the Study of Short- and Long-Term Dynamic Responses of Eukaryotic Systems

  • Protocol
  • First Online:
Yeast Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2049))

  • 1156 Accesses

Abstract

The dynamics of eukaryotic systems provide us with a signature of their response to stress, perturbations, or sustained, cyclic, or periodic variations and fluctuations. Studying the dynamic behavior of such systems is therefore elemental in achieving a mechanistic understanding of cellular behavior. This conceptual chapter discusses some of the key aspects that need to be considered in the study of dynamic responses of eukaryotic systems, in particular of eukaryotic networks. However, it does not aim to provide an exhaustive evaluation of the existing methodologies. The discussions in the chapter primarily relate to the cellular networks of eukaryotes and essentially leave higher dynamic community structures such as social networks, epidemic spreading, or ecological networks out of the scope of this argument.

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

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Komurov K, White M (2007) Revealing static and dynamic modular architecture of the eukaryotic protein interaction network. Mol Syst Biol 3:110. https://doi.org/10.1038/msb4100149

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Henson MA (2003) Dynamic modeling of microbial cell populations. Curr Opin Biotechnol 14:460–467

    Article  CAS  Google Scholar 

  3. van Riel NAW (2006) Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments. Brief Bioinform 7:364–374. https://doi.org/10.1093/bib/bbl040

    Article  PubMed  Google Scholar 

  4. Ellner S, Guckenheimer J (2013) Dynamic models in biology. Princeton University Press, Princeton, NJ

    Google Scholar 

  5. Bar-Joseph Z, Gitter A, Simon I (2012) Studying and modelling dynamic biological processes using time-series gene expression data. Nat Rev Genet 13:552–564. https://doi.org/10.1038/nrg3244

    Article  CAS  PubMed  Google Scholar 

  6. Holme P, Saramäki J (2011) Temporal networks. https://doi.org/10.1016/j.physrep.2012.03.001

  7. Nagler J, Levina A, Timme M (2011) Impact of single links in competitive percolation. Nat Phys 7:265–270. https://doi.org/10.1038/nphys1860

    Article  CAS  Google Scholar 

  8. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci U S A 79:2554–2558

    Article  CAS  Google Scholar 

  9. Albert R (2007) Network inference, analysis, and modeling in systems biology. Plant Cell 19:3327–3338. https://doi.org/10.1105/tpc.107.054700

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Majdandzic A, Podobnik B, Buldyrev SV, Kenett DY, Havlin S, Eugene Stanley H (2013) Spontaneous recovery in dynamical networks. Nat Phys 10:34–38. https://doi.org/10.1038/nphys2819

    Article  CAS  Google Scholar 

  11. Sekara V, Stopczynski A, Lehmann S (2016) Fundamental structures of dynamic social networks. Proc Natl Acad Sci U S A 113:9977–9982. https://doi.org/10.1073/pnas.1602803113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Alessandretti L, Sapiezynski P, Lehmann S, Baronchelli A (2017) Multi-scale spatio-temporal analysis of human mobility. PLoS One 12:e0171686. https://doi.org/10.1371/journal.pone.0171686

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Miele V, Matias C (2017) Revealing the hidden structure of dynamic ecological networks. R Soc Open Sci 4:170251. https://doi.org/10.1098/rsos.170251

    Article  PubMed  PubMed Central  Google Scholar 

  14. Zhang Y, Yang N, Lall U (2016) Modeling and simulation of the vulnerability of interdependent power-water infrastructure networks to cascading failures. J Syst Sci Syst Eng 25:102–118. https://doi.org/10.1007/s11518-016-5295-3

    Article  Google Scholar 

  15. Bansal S, Read J, Pourbohloul B, Meyers LA (2010) The dynamic nature of contact networks in infectious disease epidemiology. J Biol Dyn 4:478–489. https://doi.org/10.1080/17513758.2010.503376

    Article  PubMed  Google Scholar 

  16. Heath LS, Sioson AA (2009) Multimodal networks: structure and operations. IEEE/ACM Trans Comput Biol Bioinform 6:321–332. https://doi.org/10.1109/TCBB.2007.70243

    Article  PubMed  Google Scholar 

  17. Kivela M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA (2014) Multilayer networks. J Complex Netw 2:203–271. https://doi.org/10.1093/comnet/cnu016

    Article  Google Scholar 

  18. Carley KM (2014) ORA: a toolkit for dynamic network analysis and visualization. Encycl Soc Netw Anal Min. Springer, New York, NY, pp 1219–1228. https://doi.org/10.1007/978-1-4614-6170-8_309

    Book  Google Scholar 

  19. Rusk N (2008) A meta-network of -omics. Nat Methods 5:25–25. https://doi.org/10.1038/nmeth1165

    Article  CAS  Google Scholar 

  20. Blonder B, Wey TW, Dornhaus A, James R, Sih A (2012) Temporal dynamics and network analysis. Methods Ecol Evol 3:958–972. https://doi.org/10.1111/j.2041-210X.2012.00236.x

    Article  Google Scholar 

  21. Faisal FE, Milenković T (2014) Dynamic networks reveal key players in aging. Bioinformatics 30:1721–1729. https://doi.org/10.1093/bioinformatics/btu089

    Article  CAS  PubMed  Google Scholar 

  22. Zhang X, Moore C, Newman MEJ (2016) Random graph models for dynamic networks. Eur Phys J B 90:200

    Article  Google Scholar 

  23. Orsini C, Dankulov MM, Colomer-de-Simón P, Jamakovic A, Mahadevan P, Vahdat A, Bassler KE, Toroczkai Z, Boguñá M, Caldarelli G, Fortunato S, Krioukov D (2015) Quantifying randomness in real networks. Nat Commun 6:8627. https://doi.org/10.1038/ncomms9627

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Armbruster B, Carlsson JG (2011) Dynamic network models. https://arxiv.org/abs/1103.2843

  25. Sarkar P, Moore AW (2005) Dynamic social network analysis using latent space models. ACM SIGKDD Explor Newsl 7:31–40. https://doi.org/10.1145/1117454.1117459

    Article  Google Scholar 

  26. Legendi RO, Gulyás L (2014) Agent-based dynamic network models: validation on empirical data. Springer, Berlin, pp 49–60. https://doi.org/10.1007/978-3-642-39829-2_5

    Book  Google Scholar 

  27. Hoff PD, Raftery AE, Handcock MS (2002) Latent space approaches to social network analysis. J Am Stat Assoc 97:1090–1098. https://doi.org/10.1198/016214502388618906

    Article  Google Scholar 

  28. Keidar I, Keidar I, Kuhn F, Oshman R (2011) 81 Dynamic networks: models and algorithms. ACM SIGACT News 42(1):82–96

    Article  Google Scholar 

  29. Dikicioglu D, Karabekmez E, Rash B, Pir P, Kirdar B, Oliver SG (2011) How yeast re-programmes its transcriptional profile in response to different nutrient impulses. BMC Syst Biol 5:148. https://doi.org/10.1186/1752-0509-5-148

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Dikicioglu D, Oc S, Rash BM, Dunn WB, Pir P, Kell DB, Kirdar B, Oliver SG (2014) Yeast cells with impaired drug resistance accumulate glycerol and glucose. Mol BioSyst 10:93–102. https://doi.org/10.1039/C2MB25512j

    Article  CAS  PubMed  Google Scholar 

  31. Dikicioglu D, Dunn WB, Kell DB, Kirdar B, Oliver SG (2012) Short- and long-term dynamic responses of the metabolic network and gene expression in yeast to a transient change in the nutrient environment. Mol BioSyst 8:1760–1774. https://doi.org/10.1039/c2mb05443d

    Article  CAS  PubMed  Google Scholar 

  32. Poulin J-F, Tasic B, Hjerling-Leffler J, Trimarchi JM, Awatramani R (2016) Disentangling neural cell diversity using single-cell transcriptomics. Nat Neurosci 19:1131–1141. https://doi.org/10.1038/nn.4366

    Article  CAS  PubMed  Google Scholar 

  33. Pavličev M, Wagner GP, Chavan AR, Owens K, Maziarz J, Dunn-Fletcher C, Kallapur SG, Muglia L, Jones H (2017) Single-cell transcriptomics of the human placenta: inferring the cell communication network of the maternal-fetal interface. Genome Res 27:349–361. https://doi.org/10.1101/gr.207597.116

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Breker M, Schuldiner M (2014) The emergence of proteome-wide technologies: systematic analysis of proteins comes of age. Nat Rev Mol Cell Biol 15:453–464. https://doi.org/10.1038/nrm3821

    Article  CAS  PubMed  Google Scholar 

  35. Kuwada NJ, Traxler B, Wiggins PA (2015) Genome-scale quantitative characterization of bacterial protein localization dynamics throughout the cell cycle. Mol Microbiol 95:64–79. https://doi.org/10.1111/mmi.12841

    Article  CAS  PubMed  Google Scholar 

  36. Leek JT, Monsen E, Dabney AR, Storey JD (2006) EDGE: extraction and analysis of differential gene expression. Bioinformatics 22:507–508. https://doi.org/10.1093/bioinformatics/btk005

    Article  CAS  PubMed  Google Scholar 

  37. Aghabozorgi S, Seyed Shirkhorshidi A, Ying Wah T (2015) Time-series clustering – a decade review. Inf Syst 53:16–38. https://doi.org/10.1016/j.is.2015.04.007

    Article  Google Scholar 

  38. Keogh E, Lin J (2005) Clustering of time-series subsequences is meaningless: implications for previous and future research. Knowl Inf Syst 8:154–177. https://doi.org/10.1007/s10115-004-0172-7

    Article  Google Scholar 

  39. Fidaner IB, Cankorur-Cetinkaya A, Dikicioglu D, Kirdar B, Cemgil AT, Oliver SG (2016) CLUSTERnGO: a user-defined modelling platform for two-stage clustering of time-series data. Bioinformatics (Oxford) 32:388–397. https://doi.org/10.1093/bioinformatics/btv532

    Article  CAS  Google Scholar 

  40. Liu H, Tarima S, Borders AS, Getchell TV, Getchell ML, Stromberg AJ (2005) Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments. BMC Bioinformatics 6:106. https://doi.org/10.1186/1471-2105-6-106

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Möller-Levet CS, Cho K-H, Wolkenhauer O (2003) Microarray data clustering based on temporal variation: FCV with TSD preclustering. Appl Bioinforma 2:35–45

    Google Scholar 

  42. Ramoni MF, Sebastiani P, Kohane IS (2002) Cluster analysis of gene expression dynamics. Proc Natl Acad Sci U S A 99:9121–9126. https://doi.org/10.1073/pnas.132656399

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Taylor CF, Field D, Sansone S-A, Aerts J, Apweiler R, Ashburner M, Ball CA, Binz P-A, Bogue M, Booth T, Brazma A, Brinkman RR, Michael Clark A, Deutsch EW, Fiehn O, Fostel J, Ghazal P, Gibson F, Gray T, Grimes G, Hancock JM, Hardy NW, Hermjakob H, Julian RK, Kane M, Kettner C, Kinsinger C, Kolker E, Kuiper M, Le Novère N, Leebens-Mack J, Lewis SE, Lord P, Mallon A-M, Marthandan N, Masuya H, McNally R, Mehrle A, Morrison N, Orchard S, Quackenbush J, Reecy JM, Robertson DG, Rocca-Serra P, Rodriguez H, Rosenfelder H, Santoyo-Lopez J, Scheuermann RH, Schober D, Smith B, Snape J, Stoeckert CJ, Tipton K, Sterk P, Untergasser A, Vandesompele J, Wiemann S, Vandesompele J, Wiemann S (2008) Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project. Nat Biotechnol 26:889–896. https://doi.org/10.1038/nbt.1411

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M (2001) Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 29:365–371. https://doi.org/10.1038/ng1201-365

    Article  CAS  PubMed  Google Scholar 

  45. Taylor CF, Paton NW, Lilley KS, Binz P-A, Julian RK, Jones AR, Zhu W, Apweiler R, Aebersold R, Deutsch EW, Dunn MJ, Heck AJR, Leitner A, Macht M, Mann M, Martens L, Neubert TA, Patterson SD, Ping P, Seymour SL, Souda P, Tsugita A, Vandekerckhove J, Vondriska TM, Whitelegge JP, Wilkins MR, Xenarios I, Yates JR, Hermjakob H (2007) The minimum information about a proteomics experiment (MIAPE). Nat Biotechnol 25:887–893. https://doi.org/10.1038/nbt1329

    Article  CAS  PubMed  Google Scholar 

  46. Le Novère N, Finney A, Hucka M, Bhalla US, Campagne F, Collado-Vides J, Crampin EJ, Halstead M, Klipp E, Mendes P, Nielsen P, Sauro H, Shapiro B, Snoep JL, Spence HD, Wanner BL (2005) Minimum information requested in the annotation of biochemical models (MIRIAM). Nat Biotechnol 23:1509–1515. https://doi.org/10.1038/nbt1156

    Article  CAS  PubMed  Google Scholar 

  47. Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, Cuellar AA, Dronov S, Gilles ED, Ginkel M, Gor V, Goryanin II, Hedley WJ, Hodgman WC, Hofmeyr J-H, Hunter PJ, Juty NS, Kasberg JL, Kremling A, Kummer U, Le Novere N, Loew LM, Lucio D, Mendes P, Minch E, Mjolsness ED, Nakayama Y, Nelson MR, Nielsen PF, Sakurada T, Schaff JC, Shapiro BE, Shimizu TS, Spence HD, Stelling J, Takahashi K, Tomita M, Wang J, AP and the rest of the SBML Forum (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19:524–531. https://doi.org/10.1093/bioinformatics/btg015

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK

    Duygu Dikicioglu

  2. Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK

    Duygu Dikicioglu

Authors
  1. Duygu Dikicioglu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Duygu Dikicioglu .

Editor information

Editors and Affiliations

  1. Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK

    Stephen G. Oliver

  2. Genetadi Biotech, Parque Tecnológico de Bizkaia, Biscay, Spain

    Juan I. Castrillo

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Dikicioglu, D. (2019). Rational Design and Methods of Analysis for the Study of Short- and Long-Term Dynamic Responses of Eukaryotic Systems. In: Oliver, S.G., Castrillo, J.I. (eds) Yeast Systems Biology. Methods in Molecular Biology, vol 2049. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9736-7_18

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9736-7_18

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9735-0

  • Online ISBN: 978-1-4939-9736-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation