Skip to main content

System Biology of Gene Regulation

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 569))

Summary

A famous joke story that exhibits the traditionally awkward alliance between theory and experiment and showing the differences between experimental biologists and theoretical modelers is when a University sends a biologist, a mathematician, a physicist, and a computer scientist to a walking trip in an attempt to stimulate interdisciplinary research. During a break, they watch a cow in a field nearby and the leader of the group asks, “I wonder how one could decide on the size of a cow?” Since a cow is a biological object, the biologist responded first: “I have seen many cows in this area and know it is a big cow.” The mathematician argued, “The true volume is determined by integrating the mathematical function that describes the outer surface of the cow’s body.” The physicist suggested: “Let’s assume the cow is a sphere.…” Finally the computer scientist became nervous and said that he didn’t bring his computer because there is no Internet connection up there on the hill.

In this humorous but explanatory story suggestions proposed by theorists can be taken to reflect the view of many experimental biologists that computer scientists and theorists are too far removed from biological reality and therefore their theories and approaches are not of much immediate usefulness. Conversely, the statement of the biologist mirrors the view of many traditional theoretical and computational scientists that biological experiments are for the most part simply descriptive, lack rigor, and that much of the resulting biological data are of questionable functional relevance.

One of the goals of current biology as a multidisciplinary science is to bring people from different scientific areas together on the same “hill” and teach them to speak the same “language.” In fact, of course, when presenting their data, most experimentalist biologists do provide an interpretation and explanation for the results, and many theorists/computer scientists aim to answer (or at least to fully describe) questions of biological relevance. Thus systems biology could be treated as such a socioscientific phenomenon and a new approach to both experiments and theory that is defined by the strategy of pursuing integration of complex data about the interactions in biological systems from diverse experimental sources using interdisciplinary tools and personnel.

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

Buying options

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
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

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Small S, Blair A, and Levine M. (1992). Regulation of even-skipped stripe 2 in the Drosophila embryo. EMBO J 11(11):4047–4057.

    PubMed  CAS  Google Scholar 

  2. Small S, Blair A, and Levine M. (1996). Regulation of two pair-rule stripes by a single enhancer in the Drosophila embryo. Dev Biol 175:314–324.

    Article  PubMed  CAS  Google Scholar 

  3. Yuh CH, Bolouri H, and Davidson EH. (1998). Genomic cis-regulatory logic: Experimental and computational analysis of a sea urchin gene. Science 279:1896–1902.

    Article  PubMed  CAS  Google Scholar 

  4. Madhani HD, and Fink GR. (1998). The riddle of MAP kinase signaling specificity. Trends Genet 14:4.

    Article  Google Scholar 

  5. Davidson EH. (1986). Gene Activity in Early Development. Academic, Orlando, FL.

    Google Scholar 

  6. Kanehisa M, Goto S. (1999). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 27:29–34. http://www.genome.ad.jp/kegg/.

  7. Goto S, Bono H, Ogata H, Fujibuchi W, Nishioka T, Sato K, Kanehisha M. (1997). Organizing and computing metabolic pathway data in terms of binary relations. In Pacific Symposium on Biocomputing’97, pp. 175–186.

    Google Scholar 

  8. Blanchard JL, Bulmore DL, Farmer AD, Gonzales M, Steadman PA, Waugh ME, Wlodek ST, and Mendes P. (2000). Pathdb: A second generation metabolic database. In Hofmeyr JH, Rohwer J, Snoep J. (eds.), Animating the cellular map, pp. 207–212. Stellenbosch University Press, Stellenbosch.

    Google Scholar 

  9. Krishnamurthy L, Nadeau J, Ozsoyoglu G, Ozsoyoglu M, Schaeffer G, Tasan M, Xu W. (2003). Pathways database system: An integrated system for biological pathways. Bioinformatics 19:930–937.

    Article  PubMed  CAS  Google Scholar 

  10. Ochs RA, Qureschi A, Sycz A, Vorbach J. (1996). A computerized metabolic map 2. relational structure, extended modeling and a graphical interface. J Chem Inf Comput Sci 36:594–601.

    Article  CAS  Google Scholar 

  11. Bhalla US. (2002). The chemical organization of signaling interactions. Bioinformatics 18:855–863.

    Article  PubMed  CAS  Google Scholar 

  12. Baitaluk M, Qian X, Godbole S, Raval A, Ray A, and Gupta A. (2006). PathSys: Integrating molecular interaction graphs for systems biology. BMC Bioinformatics 7:55.

    Article  PubMed  Google Scholar 

  13. Baitaluk M, Sedova M, Ray A, and Gupta A. (2006). BiologicalNetworks: Visualization and analysis tool for systems biology. Nucleic Acids Res 34:W466–W471; doi:10.1093/nar/gkl308.

    Article  PubMed  CAS  Google Scholar 

  14. Lukashin AV, Lukashev ME, and Fuchs R. (2003). Topology of gene expression networks as revealed by data mining and modeling. Bioinformatics 19:1909–1916.

    Article  PubMed  CAS  Google Scholar 

  15. Klamt S, and Gilles ED. (2004). Minimal cut sets in biochemical reaction networks. Bioinformatics 20:226–234.

    Article  PubMed  CAS  Google Scholar 

  16. Wuchty S, Oltvai ZN, and Barabasi AL. (2003). Evolutionary conservation of motif constituents in the yeast protein interaction network. Nat Genet 35:176–179.

    Article  PubMed  CAS  Google Scholar 

  17. Han JD, Bertin N, Hao T, Goldberg DS, Berriz GF, et al. (2004). Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 430:88–93.

    Article  PubMed  CAS  Google Scholar 

  18. Albert R. (2005). Scale-free networks in cell biology. J Cell Sci 118:4947–4957.

    Article  PubMed  CAS  Google Scholar 

  19. Barabási AL, and Oltvai ZN. (2004). Network biology: Understanding the cell’s functional organization. Nat Rev Genet 5:101–113.

    Article  PubMed  Google Scholar 

  20. Lappe M, and Holm L. (2004). Unraveling protein interaction networks with near-optimal efficiency. Nat Biotechnol 22:98–103.

    Article  PubMed  CAS  Google Scholar 

  21. Stumpf MP, Wiuf C, and May RM. (2005). Subnets of scale-free networks are not scale-free: Sampling properties of networks. Proc Natl Acad Sci USA 102:4221–4224.

    Article  PubMed  CAS  Google Scholar 

  22. Przulj N, Corneil DG, and Jurisica I. (2004). Modeling interactome: Scale-free or geometric? Bioinformatics 20:3508–3515.

    Article  PubMed  CAS  Google Scholar 

  23. Han J-DJ, Dupuy D, Bertin N, et al. (2005). Effect of sampling on topology predictions of protein-protein interaction networks. Nat Biotechnol 23:839–844.

    Article  PubMed  CAS  Google Scholar 

  24. Estrada E. (2006). Virtual identification of essential proteins within the protein interaction network of yeast. Proteomics 6:35–40.

    Article  PubMed  CAS  Google Scholar 

  25. Samal A, Singh S, Giri V, et al. (2006). Low degree metabolites explain essential reactions and enhance modularity in biological networks. BMC Bioinformatics 7:118.

    Article  PubMed  Google Scholar 

  26. Palumbo MC, Colosimo A, Giuliani A, et al. (2005). Functional essentiality from topology features in metabolic networks: A case study in yeast. FEBS Lett 579:4642–4646.

    Article  PubMed  CAS  Google Scholar 

  27. Croes D, Couche F, Wodak SJ, et al. (2005). Metabolic PathFinding: Inferring relevant pathways in biochemical networks. Nucleic Acids Res 33:W326–W330.

    Article  PubMed  CAS  Google Scholar 

  28. Shlomi T, Segal D, Ruppin E, et al. (2006). QPath: A method for querying pathways in a protein-protein interaction network. BMC Bioinformatics 7:199.

    Article  PubMed  Google Scholar 

  29. Guo X, Liu R, Shriver CD, et al. (2006). Assessing semantic similarity measures for the characterization of human regulatory pathways. Bioinformatics 22:967–973.

    Article  PubMed  CAS  Google Scholar 

  30. Scott J, Ideker T, Karp RM, et al. (2006). Efficient algorithms for detecting signaling pathways in protein interaction networks. J Comput Biol 13:133–144.

    Article  PubMed  CAS  Google Scholar 

  31. Hartwell LH, Hopfield JJ, Leibler S, et al. (1999). From molecular to modular cell biology. Nature 402(6761 Suppl):C47–C52.

    Article  PubMed  CAS  Google Scholar 

  32. Ideker T, Ozier O, Schwikowski B, et al. (2002). Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18(Suppl 1):S233–S240.

    Article  PubMed  Google Scholar 

  33. Milo R, Shen-Orr S, Itzkovitz S, et al. (2002). Network motifs: Simple building blocks of complex networks. Science 298:824–827.

    Article  PubMed  CAS  Google Scholar 

  34. Kashtan N, Itzkovitz S, and Milo R. (2004). Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20(11):1746–1758.

    Article  PubMed  CAS  Google Scholar 

  35. Wernicke S, and Rasche F. (2006). FANMOD: A tool for fast network motif detection. Bioinformatics 22:1152–1153.

    Article  PubMed  CAS  Google Scholar 

  36. Schreiber F, and Schwobbermeyer H. (2005). MAVisto: A tool for the exploration of network motifs. Bioinformatics 21:3572–3574.

    Article  PubMed  CAS  Google Scholar 

  37. Kuang R, Weston J, Noble WS, and Leslie C. (2005). Motif-based protein ranking by network propagation. Bioinformatics 21:3711–3718.

    Article  PubMed  CAS  Google Scholar 

  38. Berg J, and Lässig M. (2004). Local graph alignment and motif search in biological networks. Proc Natl Acad Sci USA 101:14689–14694.

    Article  PubMed  CAS  Google Scholar 

  39. D’haeseleer P. (2005). How does gene expression clustering work? Nat Biotechnol 23:1499–1501.

    Article  PubMed  Google Scholar 

  40. Brun C, Herrmann C, and Guenoche A. (2004). Clustering proteins from interaction networks for the prediction of cellular functions. BMC Bioinformatics 5:95.

    Article  PubMed  Google Scholar 

  41. King AD, Przulj N, and Jurisica I. (2004). Protein complex prediction via cost-based clustering. Bioinformatics 20:3013–3020.

    Article  PubMed  CAS  Google Scholar 

  42. Dunn R, Dudbridge F, and Sanderson CM. (2005). The use of edge-betweenness clustering to investigate biological function in protein interaction networks. BMC Bioinformatics 6:39.

    Article  PubMed  Google Scholar 

  43. Farutin V, Robison K, Lightcap E, et al. (2006). Edge-count probabilities for the identification of local protein communities and their organization. Proteins 62:800–818.

    Article  PubMed  CAS  Google Scholar 

  44. Pereira-Leal JB, Enright AJ, and Ouzounis CA. (2004). Detection of functional modules from protein interaction networks. Proteins 54:49–57.

    Article  PubMed  CAS  Google Scholar 

  45. Adamcsek B, Palla G, Farkas IJ, et al. (2006). CFinder: Locating cliques and overlapping modules in biological networks. Bioinformatics 22(8):1021–1023.

    Article  PubMed  CAS  Google Scholar 

  46. Rives AW, and Galitski T. (2003). Modular organization of cellular networks. Proc Natl Acad Sci USA 100:1128–1133.

    Article  PubMed  CAS  Google Scholar 

  47. Arnau V, Mars S, and Marin I. (2005). Iterative cluster analysis of protein interaction data. Bioinformatics 21:364–378.

    Article  PubMed  CAS  Google Scholar 

  48. Ma HW, Zhao XM, Yuan YJ, et al. (2004). Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph. Bioinformatics 20:1870–1876.

    Article  PubMed  CAS  Google Scholar 

  49. Gupta A, and Ludäscher B. (2003). The many faces of process interaction graphs: A data management perspective. OMICS 7:105–108.

    Article  PubMed  CAS  Google Scholar 

  50. Li S, Armstrong CM, Bertin N, Ge H, Milstein S, Boxem M, Vidalain PO, Han JD, Chesneau A, Hao T, Goldberg DS, Li N, Martinez M, Rual JF, Lamesch P, Xu L, Tewari M, Wong SL, Zhang LV, Berriz GF, Jacotot L, Vaglio P, Reboul J, Hirozane-Kishikawa T, Li Q, Gabel HW, Elewa A, Baumgartner B, Rose DJ, Yu H, Bosak S, Sequerra R, Fraser A, Mango SE, Saxton WM, Strome S, Van Den Heuvel S, Piano F, Vandenhaute J, Sardet C, Gerstein M, Doucette-Stamm L, Gunsalus KC, Harper JW, Cusick ME, Roth FP, Hill DE, and Vidal M. (2004). A map of the interactome network of the metazoan C. elegans. Science 303:540–543.

    Article  PubMed  CAS  Google Scholar 

  51. Vert JP, and Kanehisa M. (2003). Extracting active pathways from gene expression data. Bioinformatics 19:238–244.

    Article  Google Scholar 

  52. Famili I, and Palsson BO. (2003). Systemic metabolic reactions are obtained by singular value decomposition of genome-scale stoichiometric matrices. J Theor Biol 224:8796.

    Article  Google Scholar 

  53. Rives AW, and Galitski T. (2003). Modular organization of cellular networks. Proc Natl Acad Sci USA 100:1128–1133.

    Article  PubMed  CAS  Google Scholar 

  54. Fukuda K, and Takagi T. (2001). Knowledge representation of signal transduction pathways. Bioinformatics 17:829–837.

    Article  PubMed  CAS  Google Scholar 

  55. Joyce AR, and Palsson BO. (2006). The model organism as a system: Integrating ‘omics’ data sets. Nat Rev Mol Cell Biol 7:198–210.

    Article  PubMed  CAS  Google Scholar 

  56. Shannon P, Markiel A, Ozier O, et al. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504.

    Article  PubMed  CAS  Google Scholar 

  57. Reiss DJ, Avila-Campillo I, Thorsson V, et al. (2005). Tools enabling the elucidation of molecular pathways active in human disease: Application to hepatitis C virus infection. BMC Bioinformatics 6:154.

    Article  PubMed  Google Scholar 

  58. Albrecht M, Huthmacher C, Tosatto SC, et al. (2005). Decomposing protein networks into domain-domain interactions. Bioinformatics 21(Suppl 2):ii220–ii221.

    Article  PubMed  CAS  Google Scholar 

  59. Gopalacharyulu PV, Lindfors E, Bounsaythip C, et al. (2005). Data integration and visualization system for enabling conceptual biology. Bioinformatics 21(Suppl 1):i177–i185.

    Article  PubMed  CAS  Google Scholar 

  60. Myers CL, Robson D, Wible A, et al. (2005). Discovery of biological networks from diverse functional genomic data. Genome Biol 6:R114.

    Article  PubMed  Google Scholar 

  61. Hwang D, Rust AG, Ramsey S, et al. (2005). A data integration methodology for systems biology. Proc Natl Acad Sci USA 102:17296–17301.

    Article  PubMed  CAS  Google Scholar 

  62. Hwang D, Smith JJ, Leslie DM, et al. (2005). A data integration methodology for systems biology: Experimental verification. Proc Natl Acad Sci USA 102:17302–17307.

    Article  PubMed  CAS  Google Scholar 

  63. Aragues R, Jaeggi D, and Oliva B. (2006). PIANA: Protein interactions and network analysis. Bioinformatics 22:1015–1017.

    Article  PubMed  CAS  Google Scholar 

  64. Han K, Ju BH, and Jung H. (2004). WebInterViewer: Visualizing and analyzing molecular interaction networks. Nucleic Acids Res 32:W89–W95.

    Article  PubMed  CAS  Google Scholar 

  65. Li W, and Kurata H. (2005). A grid layout algorithm for automatic drawing of biochemical networks. Bioinformatics 21:2036–2042.

    Article  PubMed  CAS  Google Scholar 

  66. Carey VJ, Gentry J, Whalen E, et al. (2005). Network structures and algorithms in Bioconductor. Bioinformatics 21:135–136.

    Article  PubMed  CAS  Google Scholar 

  67. Scholtens D, Vidal M, and Gentleman R. (2005). Local modeling of global interactome networks. Bioinformatics 21:3548–3557.

    Article  PubMed  CAS  Google Scholar 

  68. Balasubramanian R, LaFramboise T, Scholtens D, et al. (2004). A graph-theoretic approach to testing associations between disparate sources of functional genomics data. Bioinformatics 20:3353–3362.

    Article  PubMed  CAS  Google Scholar 

  69. Zhu D, Hero AO, Cheng H, et al. (2005). Network constrained clustering for gene microarray data. Bioinformatics 21:4014–4020.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Humana Press, a part of Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Baitaluk, M. (2009). System Biology of Gene Regulation. In: Astakhov, V. (eds) Biomedical Informatics. Methods in Molecular Biology™, vol 569. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-59745-524-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-59745-524-4_4

  • Published:

  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-934115-63-3

  • Online ISBN: 978-1-59745-524-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics