Issue No. 01 - Jan.-Feb. (2013 vol. 10)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.169
Carlos H. A. Higa , Coll. of Comput., Fed. Univ. of Mato Grosso do Sul, Campo Grande, Brazil
Tales P. Andrade , Inst. of Math. & Stat., Univ. of Sao Paulo, Sao Paulo, Brazil
Ronaldo F. Hashimoto , Inst. of Math. & Stat., Univ. of Sao Paulo, Sao Paulo, Brazil
Models of gene regulatory networks (GRN) have been proposed along with algorithms for inferring their structure. By structure, we mean the relationships among the genes of the biological system under study. Despite the large number of genes found in the genome of an organism, it is believed that a small set of genes is responsible for maintaining a specific core regulatory mechanism (small subnetworks). We propose an algorithm for inference of subnetworks of genes from a small initial set of genes called seed and time series gene expression data. The algorithm has two main steps: First, it grows the seed of genes by adding genes to it, and second, it searches for subnetworks that can be biologically meaningful. The seed growing step is treated as a feature selection problem and we used a thresholded Boolean network with a perturbation model to design the criterion function that is used to select the features (genes). Given that the reverse engineering of GRN is a problem that does not necessarily have one unique solution, the proposed algorithm has as output a set of networks instead of one single network. The algorithm also analyzes the dynamics of the networks which can be time-consuming. Nevertheless, the algorithm is suitable when the number of genes is small. The results showed that the algorithm is capable of recovering an acceptable rate of gene interactions and to generate regulatory hypotheses that can be explored in the wet lab.
Biological system modeling, Inference algorithms, Computational modeling, Bioinformatics, Gene expression, Boolean functions, Time series analysis
C. H. Higa, T. P. Andrade and R. F. Hashimoto, "Growing Seed Genes from Time Series Data and Thresholded Boolean Networks with Perturbation," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 1, pp. 37-49, 2013.