Abstract
Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This paper proposes an efficient method to generate the genetic regulatory network inferred from time series data. Our method first encodes the data into levels. Next, it determines the set of potential parents for each gene based upon the probability of the gene’s expression increasing. After a subset of potential parents are selected, it determines if any genes in this set may have a combined effect. Finally, the potential sets of parents are competed against each other to determine the final set of parents. The result is a directed graph representation of the genetic network’s repression and activation connections. Our results on synthetic data generated from models for several genetic networks with tight feedback are promising.
This material is based upon work supported by the National Science Foundation under Grant No. 0331270.
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
Brown, P.A., Botstein, D.: Exploring the new world of the genome with DNA microarrays. Nature Genet. 21, 33–37 (1999)
Eisen, M.B., Spellman, P.T., Browndagger, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998)
Liang, S., Fuhrman, S., Somogyi, R.: REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. In: Pacific Symposium on Biocomputing, vol. 3, pp. 18–29 (1998)
Akutsu, T., Miyano, S., Kuhara, S.: Identification of genetic networks from a small number of gene expression patterns under the boolean network model (1999)
Ideker, T.E., Thorsson, V., Karp, R.M.: Discovery of regulatory interactions through perturbation: Inference and experimental design (2000)
Lähdesmäki, H., Shmulevich, I., Yli-Harja, O.: On learning gene regulatory networks under the boolean network model. Machine Learning 52, 147–167 (2003)
Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using bayesian networks to analyze expression data. Journal of Computational Biology 7(3–4), 601–620 (2000)
Sachs, K., Perez, O., Pe’er, D., Lauffenburger, D.A., Nolan, G.P.: Causal protein-signaling networks derived from multiparameter single-cell data. Science 22, 523–529 (2005)
Yu, J., Smith, V.A., Wang, P.P., Hartemink, A.J., Jarvis, E.D.: Advances to bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20, 3594–3603 (2004)
Ptashne, M.: A Genetic Switch. Cell Press & Blackwell Scientific Publishing (1992)
Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81(25), 2340–2361 (1977)
Guet, C.C., Elowitz, M.B., Hsing, W., Leibler, S.: Combinatorial synthesis of genetic networks. Science 296, 1466–1470 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Barker, N., Myers, C., Kuwahara, H. (2006). Learning Genetic Regulatory Network Connectivity from Time Series Data. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_103
Download citation
DOI: https://doi.org/10.1007/11779568_103
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35453-6
Online ISBN: 978-3-540-35454-3
eBook Packages: Computer ScienceComputer Science (R0)