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

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