Abstract
We study gene expression data, derived from developing tissues, under multiple genetic backgrounds (mutations). Motivated by the perceived behavior under these background, our main goals are to explore time windows questions:
-
1
Find a large set of genes that have a similar behavior in two different genetic backgrounds, under an appropriate time shift.
-
2
Find a model that approximates the dynamics of a gene network in developing tissues at different continuous time windows.
We first explain the biological significance of these problems, and then explore their computational complexity, which ranges from polynomial to NP-hard. We developed algorithms and heuristics for the different problems, and ran those on synthetic and biological data, with very encouraging results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
GOÂ annotation guide, http://www.geneontology.org/go.annotation
Arbeitman, N.M., Furlong, M.E.E., Imam, F., Johnson, E., Null, H.B., Baker, S.B., Krasnow, A.M., Scott, P.M., Davis, W.R., White, P.K.: Gene expression during the life cycle of drosophila melanogaster. Science 297, 2270–2275 (2002)
Ben-Dor, A., Shamir, R., Yakhini, Z.: Clustering gene expression patterns. Journal of Computational Biology 6, 281–297 (1999)
Chamovitz, D.A., Yahalom, A.: A systems approach to the cop9 signalosome. Plant Physiol 132, 426–427 (2003)
Chang, W.C., Li, C.W., Chen, B.S.: Quantitative inference of dynamic regulatory pathways via microarray data. BMC Bioinformatics 6(44) (2005)
Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proc. ISMB 2000, pp. 93–103 (2000)
Chor, B., Tuller, T.: Adding hidden node to gene network. In: Jonassen, I., Kim, J. (eds.) WABI 2004. LNCS (LNBI), vol. 3240, pp. 123–134. Springer, Heidelberg (2004)
D’haeseleer, P., Fuhrman, S.: Gene network inference using a linear, additive regulation model. Bioinformatics (2000)
Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using bayesian network to analyze expression data. Journal of Computational Biology 7, 601–620 (2000)
McDonald, M.J., Rosbash, M.: Microarray analysis and organization of circadian gene expression in drosophila. Cell 107(5), 567–578 (2001)
Orian, A., Van Steensel, B., Delrow, J., Bussemaker, H.J., Li, L., Sawado, T., Williams, E., Loo, L.W., Cowley, S.M., Yost, C., Pierce, S., Edgar, B.A., Parkhurst, S.M., Eisenman, R.N.: Genomic binding by the drosophila myc, max, mad/mnt transcription factor network. Genes Dev 17, 1101–1114 (2003)
Rocke, D.M., Durbin, B.: Approximate variance-stabilizing transformations for gene-expression microarray data. Bioinformatics 19(8), 966–972 (2003)
Tao, T., Vu, V.: On the singularity probability of random bernoulli matrices. submited (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tuller, T., Oron, E., Makavy, E., Chamovitz, D.A., Chor, B. (2005). Time-Window Analysis of Developmental Gene Expression Data with Multiple Genetic Backgrounds. In: Casadio, R., Myers, G. (eds) Algorithms in Bioinformatics. WABI 2005. Lecture Notes in Computer Science(), vol 3692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11557067_5
Download citation
DOI: https://doi.org/10.1007/11557067_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29008-7
Online ISBN: 978-3-540-31812-5
eBook Packages: Computer ScienceComputer Science (R0)