Gene clustering for time-series microarray with production outputs

Abstract

The identification of coexpressed genes from microarray data is a challenging problem in bioinformatics and computational biology. The objective of this study is to obtain knowledge about the most important genes and clusters related to production outputs of real-world time-series microarray data in the industrial microbiology area. Each sample in the microarray data experiment is complemented with the measurement of the corresponding production and growth values. A novel aspect of this research refers to considering the relation of coexpression patterns with the measured outputs to guide the biological interpretation of results. Shape-based clustering models are developed using the pattern of gene expression values over time and further incorporating knowledge about the correlation between the change in the gene expression level and the output value. Experiments are performed for time-series microarray of bacteria, and an analysis from a biological perspective is carried out. The obtained results confirm the existence of relationships between output variables and gene expressions. Moreover, the shape-based clustering methods show promising results, being able to guide metabolic engineering actions with the identification of potential targets.