Optimality principles in adaptive evolution.

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Optimality principles in adaptive evolution. Optimality principles in adaptive evolution. (A) A smooth fitness landscape consisting of a single peak. Circles represent points on the landscape and arrows indicate the pathway of genetic change through the landscape. On a smooth landscape, there is a tendency for evolutionary convergence toward the single optimum, regardless of the starting point on the landscape. (B) A rough fitness landscape consists of multiple peaks. With multiple optima, there tends to be evolutionary divergence, sometimes even when starting from the same location on the landscape. (C) A phenotypic phase plane is a representation of how two fluxes in a metabolic network relate to each other and affect in silico‐predicted optimal growth. Distinct planes are represented by several colors. Here, the line of optimality (LO, yellow) defines the ratio of glycerol uptake rate to oxygen uptake rate that leads to optimal biomass production. On glycerol, wild‐type E. coli initially has a phenotype that maps to a suboptimal region of the portrait. After a growing for several hundred generations on glycerol, the E. coli phenotype migrates to the line of optimality. (D) Optimality principles can used to design strains of bacteria in which growth at the maximal rate requires the secretion of a product of interest. When subjected to ALE, the designed strains increase both their growth rate and product secretion rate. The two colored regions indicate accessible flux states before and after evolution. Tom M Conrad et al. Mol Syst Biol 2011;7:509 © as stated in the article, figure or figure legend