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Pathways, which are cascades of biochemical reactions catalyzed by enzymes, maintain the vitality of all living organisms. These biochemical routes have been exploited to produce numerous commodities since early civilization, such as beer, wine, and cheese. With the advance of biotechnology, various genetic tools have become available for construction and manipulation of pathways to efficiently convert renewable feedstock to value-added compounds such as specialty chemicals, pharmaceuticals, and biofuels [1]. Microbial production of these compounds is usually enabled by overexpressing endogenous or heterologous enzymes of the corresponding pathways. However, overexpression of pathway enzymes alone can be insufficient for optimal metabolite production due to an imbalanced flux through the pathway [1, 2]. A typical symptom of flux imbalance is the accumulation of unwanted and even toxic intermediates [3, 4], which can be detrimental to the productivity of desired compounds. There is seldom a straightforward strategy to resolve the non-product accumulation because enzymes within the pathway are not independent; instead the enzymes are intertwined and cross-regulated among the pathway enzymes and among the cell’s intricate metabolic networks. Due to this complexity, rationally engineering a pathway to improve its efficiency is a significant challenge. To this end, random approaches can be preferred over rational design in pathway engineering [5]. Random engineering approaches to optimize pathways generally screen through large and/or combinatorial pathway libraries. Pathway libraries have been constructed for diverse gene expression based on promoters of different strengths [6], varied intergenic regions affecting mRNA stability [4], or engineered ribosomal binding sites (RBSs) of diversified translational initiation rates [7]. In previous studies [4, 6–8], the pathway libraries were assembled by restriction digestion/ligation or overlap extension polymerase chain reaction (PCR). Applying Advanced DNA Assembly Methods to Generate Pathway Libraries Dawn T. Eriksen, Ran Chao, and Huimin Zhao