Application of Learning Classifier Systems to Gene Expression Analysis in Synthetic Biology

Learning classifier systems (LCS) are algorithms that incorporate genetic algorithms with reinforcement learning to produce adaptive systems described by if-then rules. As a new interdisciplinary branch of biology, synthetic biology pursues the design and construction of complex artificial biological systems from the bottom-up. A trend is growing in designing artificial metabolic pathways that show previously undescribed reactions produced by the assembly of enzymes from different sources in a single host. However, few researchers have succeeded thus far because of the difficulty in analyzing gene expression. To tackle this problem, data mining and knowledge discovery are essential. In this context, nature-inspired LCS are well suited to extracting knowledge from complex systems and thus can be exploited to investigate and utilize natural biological phenomena. This chapter focuses on applying LCS to gene expression analysis in synthetic biology. Specifically, it describes the optimization of artificial operon structure for the biosynthesis of metabolic pathway products in Escherichia coli. This optimization is achieved by manipulating the order of multiple genes within the artificial operons.