A System Biology Approach for the Steady-State Analysis of Gene Signaling Networks

  • Purvesh Khatri
  • Sorin Draghici
  • Adi L. Tarca
  • Sonia S. Hassan
  • Roberto Romero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

The existing approaches used to identify the relevant pathways in a given condition do not consider a number of important biological factors such as magnitude of each gene’s expression change, their position and interactions in the given pathways, etc. Recently, an impact analysis approach was proposed that considers these crucial biological factors to analyze regulatory pathways at systems biology level. This approach calculates perturbations induced by each gene in a pathway, and propagates them through the entire pathway to compute an impact factor for the given pathway. Here we propose an alternative approach that uses a linear system to compute the impact factor. Our proposed approach eliminates the possible stability problems when the perturbations are propagated through a pathway that contains positive feedback loops. Additionally, the proposed approach is able to consider the type of genes when calculating the impact factors.

Keywords

Impact Factor Impact Analysis System Biology Approach Cervical Dilation Perturbation Factor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Khatri, P., Drăghici, S., Ostermeier, G.C., Krawetz, S.A.: Profiling gene expression using Onto-Express. Genomics 79(2), 266–270 (2002)CrossRefGoogle Scholar
  2. 2.
    Drăghici, S., Khatri, P., Martins, R.P., Ostermeier, G.C., Krawetz, S.A.: Global functional profiling of gene expression. Genomics 81(2), 98–104 (2003)CrossRefGoogle Scholar
  3. 3.
    Khatri, P., Draghici, S.: Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics 21(18), 3587–3595 (2005)CrossRefGoogle Scholar
  4. 4.
    Pavlidis, P., Qin, J., Arango, V., Mann, J.J., Sibille, E.: Using the gene ontology for microarray data mining: A comparison of methods and application to age effects in human prefrontal cortex. Neurochemical Research 29(6), 1213–1222 (2004)CrossRefGoogle Scholar
  5. 5.
    Goeman, J.J., van de Geer, S.A., de Kort, F., van Houwelingen, H.C.: A global test for groups of genes: testing association with a clinical outcome. Bioinformatics 20(1), 93–99 (2004)CrossRefGoogle Scholar
  6. 6.
    Mootha, V.K., Lindgren, C.M., Eriksson, K.F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., Houstis, N., Daly, M.J., Patterson, N., Mesirov, J.P., Golub, T.R., Tamayo, P., Spiegelman, B., Lander, E.S., Hirschhorn, J.N., Altshuler, D., Groop, L.C.: Pgc-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature genetics 34(3), 267–273 (2003)CrossRefGoogle Scholar
  7. 7.
    Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., Mesirov, J.P.: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceeding of The National Academy of Sciences of the USA 102(43), 15545–15550 (2005)CrossRefGoogle Scholar
  8. 8.
    Tian, L., Greenberg, S.A., Kong, S.W., Altschuler, J., Kohane, I.S., Park, P.J.: Discovering statistically significant pathways in expression profiling studies. Proceeding of The National Academy of Sciences of the USA 102(38), 13544–13549 (2005)CrossRefGoogle Scholar
  9. 9.
    Stelling, J.: Mathematical models in microbial systems biology. Current opinion in microbiology 7(5), 513–518 (2004)CrossRefGoogle Scholar
  10. 10.
    Draghici, S., Khatri, P., Tarca, A.L., Amin, K., Done, A., Voichita, C., Georgescu, C., Romero, R.: A systems biology approach for pathway level analysis. Genome Research 17 (2007)Google Scholar
  11. 11.
    Doniger, S.W., Salomonis, N., Dahlquist, K.D., Vranizan, K., Lawlor, S.C., Conklin, B.R.: MAPPFinder: using Gene Ontology and GenMAPP to create a global gene expression profile from microarray data. Genome biology 4(1), R7 (2003)CrossRefGoogle Scholar
  12. 12.
    Pan, D., Sun, N., Cheung, K.H., Guan, Z., Ma, L., Holford, M., Deng, X., Zhao, H.: PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arbidopsis. BMC Bioinformatics 4(1), 56 (2003)CrossRefGoogle Scholar
  13. 13.
    Pandey, R., Guru, R.K., Mount, D.W.: Pathway Miner: extracting gene association networks from molecular pathways for predicting the biological significance of gene expression microarray data. Bioinformatics 20(13), 2156–2158 (2004)CrossRefGoogle Scholar
  14. 14.
    Breslin, T., Krogh, M., Peterson, C., Troein, C.: Signal transduction pathway profiling of individual tumor samples. BMC Bioinformatics 6, 1471–2105 (2005)CrossRefGoogle Scholar
  15. 15.
    Robinson, P.N., Wollstein, A., Bohme, U., Beattie, B.: Ontologizing gene-expression microarray data: characterizing clusters with gene ontology. Bioinformatics 20(6), 979–981 (2004)CrossRefGoogle Scholar
  16. 16.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems 30(1–7), 107–117 (1998)CrossRefGoogle Scholar
  17. 17.
    Haveliwala, T.: Efficient computation of PageRank. Technical Report 1999-31, Database Group, Computer Science Department, Stanford University (February 1999)Google Scholar
  18. 18.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report (1998)Google Scholar
  19. 19.
    Canales, R.D., Luo, Y., Willey, J.C., Austermiller, B., Barbacioru, C.C., Boysen, C., Hunkapiller, K., Jensen, R.V., Knight, C.R., Lee, K.Y., Ma, Y., Maqsodi, B., Papallo, A., Peters, E.H., Poulter, K., Ruppel, P.L., Samaha, R.R., Shi, L., Yang, W., Zhang, L., Goodsaid, F.M.: Evaluation of dna microarray results with quantitative gene expression platforms. Nat. Biotechnol. 24(9), 1115–1122 (2006)CrossRefGoogle Scholar
  20. 20.
    Draghici, S., Khatri, P., Eklund, A.C., Szallasi, Z.: Reliability and reproducibility issues in DNA microarray measurements. Trends Genet. 22(2), 101–109 (2006)CrossRefGoogle Scholar
  21. 21.
    Hassan, S.S., Romero, R., Haddad, R., Hendler, I., Khalek, N., Tromp, G., Diamond, M.P., Sorokin, Y., Malone, J.J.: The transcriptome of the uterine cervix before and after spontaneous term parturition. Am. J. Obstet. Gynecol. 195(3), 778–786 (2006)CrossRefGoogle Scholar
  22. 22.
    Hassan, S.S., Romero, R., Tarca, A.L., et al.: Signature pathways identified from gene expression profiles in the human uterine cervix before and after spontaneous term parturition. Am. J. Obstet. Gynecol. 197(3), 250.e1–250.e7 (2007)CrossRefGoogle Scholar
  23. 23.
    Irizarry, R.A., Hobbs, B., Collin, F., Beazer-Barclay, Y.D., Antonellis, K.J., Scherf, U., Speed, T.P.: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4(2), 249–264 (2003)MATHCrossRefGoogle Scholar
  24. 24.
    Tarca, A.L., Carey, V.J., Chen, X.W., Romero, R., Draghici, S.: Machine learning and its applications to biology. PLoS Comput. Biol. 3(6), e116 (2007)CrossRefGoogle Scholar
  25. 25.
    Smyth, G.K.: In: Limma: linear models for microarray data, pp. 397–420. Springer, New York (2005)Google Scholar
  26. 26.
    Saito, S., Shima, T., Nakashima, A., Shiozaki, A., Ito, M., Sasaki, Y.: What is the role of regulatory t cells in the success of implantation and early pregnancy? J. Assist Reprod. Genet. Epub. ahead of print (August 2007)Google Scholar
  27. 27.
    King, A., Kelly, R., Sallenave, J., Bocking, A., Challis, J.: Innate immune defences in the human uterus during pregnancy. Placenta Epub ahead of print (July 2007)Google Scholar
  28. 28.
    Tsatas, D., Baker, M.S., Rice, G.E.: Differential expression of proteases in human gestational tissues before, during and after spontaneous-onset labour at term. J. Reprod. Fertil. 116(1), 43–49 (1999)CrossRefGoogle Scholar
  29. 29.
    Koelbl, H., Kirchheimer, J., Tatra, G.: Influence of delivery on plasminogen activator inhibitor activity. J. Perinat. Med. 17(2), 107–111 (1989)CrossRefGoogle Scholar
  30. 30.
    Turpeenniemi-Hujanen, T., Feinberg, R.F., Kauppila, A., Puistola, U.: Extracellular matrix interactions in early human embryos: implications for normal implantation events. Fertil Steril 64(1), 132–138 (1995)Google Scholar
  31. 31.
    Xu, P., Wang, Y.L., Zhu, S.J., Luo, S.Y., Piao, Y.S., Zhuang, L.Z.: Expression of matrix metalloproteinase-2, -9, and -14, tissue inhibitors of metalloproteinase-1, and matrix proteins in human placenta during the first trimester. Biol. Reprod. 62(4), 988–994 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Purvesh Khatri
    • 1
  • Sorin Draghici
    • 1
  • Adi L. Tarca
    • 1
    • 2
  • Sonia S. Hassan
    • 2
  • Roberto Romero
    • 2
  1. 1.Department of Computer Science, Wayne State University 
  2. 2.Perinatology Research Branch, NIH/NICHD, Detroit, MI 48201 

Personalised recommendations