Evolutionary Engineering for Industrial Microbiology

Abstract

Superficially, evolutionary engineering is a paradoxical field that balances competing interests. In natural settings, evolution iteratively selects and enriches subpopulations that are best adapted to a particular ecological niche using random processes such as genetic mutation. In engineering desired approaches utilize rational prospective design to address targeted problems. When considering details of evolutionary and engineering processes, more commonality can be found. Engineering relies on detailed knowledge of the problem parameters and design properties in order to predict design outcomes that would be an optimized solution. When detailed knowledge of a system is lacking, engineers often employ algorithmic search strategies to identify empirical solutions. Evolution epitomizes this iterative optimization by continuously diversifying design options from a parental design, and then selecting the progeny designs that represent satisfactory solutions. In this chapter, the technique of applying the natural principles of evolution to engineer microbes for industrial applications is discussed to highlight the challenges and principles of evolutionary engineering.

Evolutionary Engineering in Chemostat Cultures for Improved Maltotriose Fermentation Kinetics in Saccharomyces pastorianus Lager Brewing Yeast

“…In addition to the genetic complexity of maltose and maltotriose metabolism in S. pastorianus , consumer acceptance issues preclude the use of targeted metabolic engineering approaches for improving sugar fermentation kinetics. Evolutionary engineering, also referred to as adaptive laboratory evolution (ALE) (Atwood et al, 1951 ), uses laboratory evolution to improve industrially relevant phenotypic characteristics and is a powerful non-GM approach for strain improvement (Vanee et al, 2012 ; Bachmann et al, 2015 ). Moreover, resequencing of the resulting evolved genomes can provide important insights into the genetic basis for the acquired improved performance (Oud et al, 2012 ).…”