Skip to main content
EURASIP Journal on Bioinformatics and Systems Biology
Download PDF
  • Research Article
  • Open access
  • Published:

Inferring Time-Varying Network Topologies from Gene Expression Data

EURASIP Journal on Bioinformatics and Systems Biology volume 2007, Article number: 51947 (2007) Cite this article

Abstract

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster—to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.

[123456789101112131415161718192021222324252627282930313233343536]

References

  1. Rangel C, Angus J, Ghahramani Z, et al.: Modeling T-cell activation using gene expression profiling and state-space models. Bioinformatics 2004, 20(9):1361-1372. 10.1093/bioinformatics/bth093

    Article  Google Scholar 

  2. Perrin B-E, Ralaivola L, Mazurie A, Bottani S, Mallet J, D'Alché-Buc F: Gene networks inference using dynamic Bayesian networks. Bioinformatics 2003, 19(2):II138-II148. 10.1093/bioinformatics/btg1071

    Google Scholar 

  3. Luscombe NM, Babu MM, Yu H, Snyder M, Teichmann SA, Gerstein M: Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 2004, 431(7006):308-312. 10.1038/nature02782

    Article  Google Scholar 

  4. Sontag E, Kiyatkin A, Kholodenko BN: Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data. Bioinformatics 2004, 20(12):1877-1886. 10.1093/bioinformatics/bth173

    Article  Google Scholar 

  5. Kim S, Li H, Russ D, et al.: Context-sensitive probabilistic Boolean networks to mimic biological regulation. Proceedings of Oncogenomics, Phoenix, Ariz, USA, January-February 2003

    Google Scholar 

  6. Li H, Wood CL, Liu Y, Getchell TV, Getchell ML, Stromberg AJ: Identification of gene expression patterns using planned linear contrasts. BMC Bioinformatics 2006, 7: 245. 10.1186/1471-2105-7-245

    Article  Google Scholar 

  7. Figueiredo MAT, Jain AK: Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002, 24(3):381-396. 10.1109/34.990138

    Article  Google Scholar 

  8. Stuart RO, Bush KT, Nigam SK: Changes in gene expression patterns in the ureteric bud and metanephric mesenchyme in models of kidney development. Kidney International 2003, 64(6):1997-2008. 10.1046/j.1523-1755.2003.00383.x

    Article  Google Scholar 

  9. Khandekar M, Suzuki N, Lewton J, Yamamoto M, Engel JD: Multiple, distant Gata2 enhancers specify temporally and tissue-specific patterning in the developing urogenital system. Molecular and Cellular Biology 2004, 24(23):10263-10276. 10.1128/MCB.24.23.10263-10276.2004

    Article  Google Scholar 

  10. Golyandina N, Nekrutkin V, Zhigljavsky A: Analysis of Time Series Structure—SSA and Related Techniques. Chapman & Hall/CRC, New York, NY, USA; 2001.

    Book  MATH  Google Scholar 

  11. Moskvina V, Zhigljavsky A: An algorithm based on singular spectrum analysis for change-point detection. Communications in Statistics Part B: Simulation and Computation 2003, 32(2):319-352. 10.1081/SAC-120017494

    Article  MathSciNet  MATH  Google Scholar 

  12. Schwab K, Patterson LT, Aronow BJ, Luckas R, Liang H-C, Potter SS: A catalogue of gene expression in the developing kidney. Kidney International 2003, 64(5):1588-1604. 10.1046/j.1523-1755.2003.00276.x

    Article  Google Scholar 

  13. Zhou Y, Lim K-C, Onodera K, et al.: Rescue of the embryonic lethal hematopoietic defect reveals a critical role for GATA-2 in urogenital development. The EMBO Journal 1998, 17(22):6689-6700. 10.1093/emboj/17.22.6689

    Article  Google Scholar 

  14. Challen GA, Martinez G, Davis MJ, et al.: Identifying the molecular phenotype of renal progenitor cells. Journal of the American Society of Nephrology 2004, 15(9):2344-2357. 10.1097/01.ASN.0000136779.17837.8F

    Article  Google Scholar 

  15. NCBI Pubmed[http://www.ncbi.nlm.nih.gov/entrez/query.fcgi]

  16. Zadeh HH, Tanavoli S, Haines DD, Kreutzer DL: Despite large-scale T cell activation, only a minor subset of T cells responding in vitro to Actinobacillus actinomycetemcomitans differentiate into effector T cells. Journal of Periodontal Research 2000, 35(3):127-136. 10.1034/j.1600-0765.2000.035003127.x

    Article  Google Scholar 

  17. Ghahramani Z, Hinton GE: Parameter estimation for linear dynamical systems. University of Toronto, Toronto, Ontario, Canada; 1996.

    Google Scholar 

  18. Shumway RH, Stoffer DS: Time Series Analysis and Applications, Springer Texts in Statistics. Springer, New York, NY, USA; 2000.

    Book  Google Scholar 

  19. Effron B: An Introduction to the Bootstrap. Chapman & Hall/CRC, New York, NY, USA; 1993.

    Book  Google Scholar 

  20. Dougherty ER, Kim S, Chen Y: Coefficient of determination in nonlinear signal processing. Signal Processing 2000, 80(10):2219-2235. 10.1016/S0165-1684(00)00079-7

    Article  MATH  Google Scholar 

  21. Kim S, Dougherty ER, Bittner ML, et al.: General nonlinear framework for the analysis of gene interaction via multivariate expression arrays. Journal of Biomedical Optics 2000, 5(4):411-424. 10.1117/1.1289142

    Article  Google Scholar 

  22. Opgen-Rhein R, Strimmer K: Using regularized dynamic correlation to infer gene dependency networks from time-series microarray data. Proceedings of the 4th International Workshop on Computational Systems Biology (WCSB '06), Tampere, Finland, June 2006

    Google Scholar 

  23. Hero AO III, Fleury G, Mears AJ, Swaroop A: Multicriteria gene screening for analysis of differential expression with DNA microarrays. EURASIP Journal on Applied Signal Processing 2004, 2004(1):43-52. special issue on genomic signal processing 10.1155/S1110865704310036

    Article  MATH  Google Scholar 

  24. Bar-Joseph Z: Analyzing time series gene expression data. Bioinformatics 2004, 20(16):2493-2503. 10.1093/bioinformatics/bth283

    Article  Google Scholar 

  25. Kundaje A, Antar O, Jebara T, Leslie C: Learning regulatory networks from sparsely sampled time series expression data. Columbia University, New York, NY, USA; 2002.

    Google Scholar 

  26. Balmer JE, Blomhoff R: Gene expression regulation by retinoic acid. Journal of Lipid Research 2002, 43(11):1773-1808. 10.1194/jlr.R100015-JLR200

    Article  Google Scholar 

  27. Esquela AF, Lee SE-J: Regulation of metanephric kidney development by growth/differentiation factor 11. Developmental Biology 2003, 257(2):356-370. 10.1016/S0012-1606(03)00100-3

    Article  Google Scholar 

  28. Maeshima A, Yamashita S, Maeshima K, Kojima I, Nojima Y: Activin a produced by ureteric bud is a differentiation factor for metanephric mesenchyme. Journal of the American Society of Nephrology 2003, 14(6):1523-1534. 10.1097/01.ASN.0000067419.86611.21

    Article  Google Scholar 

  29. Mori M, Ghyselinck NB, Chambon P, Mark M: Systematic immunolocalization of retinoid receptors in developing and adult mouse eyes. Investigative Ophthalmology and Visual Science 2001, 42(6):1312-1318.

    Google Scholar 

  30. Lim K-C, Lakshmanan G, Crawford SE, Gu Y, Grosveld F, Engel JD: Gata3 loss leads to embryonic lethality due to noradrenaline deficiency of the sympathetic nervous system. Nature Genetics 2000, 25(2):209-212. 10.1038/76080

    Article  Google Scholar 

  31. Mizutani H, May LT, Sehgal PB, Kupper TS: Synergistic interactions of IL-1 and IL-6 in T cell activation. Mitogen but not antigen receptor-induced proliferation of a cloned T helper cell line is enhanced by exogenous IL-6. Journal of Immunology 1989, 143(3):896-901.

    Google Scholar 

  32. Lin J-X, Leonard WJ:The immediate-early gene product Egr-1 regulates the human interleukin- 2 receptor -chain promoter through noncanonical Egr and Sp1 binding sites. Molecular and Cellular Biology 1997, 17(7):3714-3722.

    Article  Google Scholar 

  33. Herrgård MJ, Covert MW, Palsson BØ: Reconciling gene expression data with known genome-scale regulatory network structures. Genome Research 2003, 13(11):2423-2434. 10.1101/gr.1330003

    Article  Google Scholar 

  34. Li C, Wong WH: Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proceedings of the National Academy of Sciences of the United States of America 2001, 98(1):31-36. 10.1073/pnas.011404098

    Article  MATH  Google Scholar 

  35. Schäfer J, Strimmer K: An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 2005, 21(6):754-764. 10.1093/bioinformatics/bti062

    Article  Google Scholar 

  36. Rao A, Hero AO III, States DJ, Engel JD: Inference of biologically relevant gene influence networks using the directed information criterion. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '06), Toulouse, France, May 2006 2: 1028-1031.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109-2122, USA

    Arvind Rao & Alfred O Hero III

  2. Bioinformatics Graduate Program, Center for Computational Medicine and Biology, School of Medicine, University of Michigan, Ann Arbor, MI, 48109-2218, USA

    Arvind Rao, Alfred O Hero III & David J States

  3. Department of Human Genetics, School of Medicine, University of Michigan, Ann Arbor, MI, 48109-0618, USA

    David J States

  4. Department of Cell and Developmental Biology, School of Medicine, University of Michigan, Ann Arbor, MI, 48109-2200, USA

    James Douglas Engel

Authors
  1. Arvind Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alfred O Hero III
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David J States
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. James Douglas Engel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arvind Rao.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Rao, A., Hero, A.O., States, D.J. et al. Inferring Time-Varying Network Topologies from Gene Expression Data. J Bioinform Sys Biology 2007, 51947 (2007). https://doi.org/10.1155/2007/51947

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1155/2007/51947

Keywords