Figure 3.

Optimization of metabolite dynamics in a fatty acid synthesis pathway. (A) Pathway diagrams with various control architectures implemented in Escherichia coli.³³ The metabolic loop employs a metabolite-responsive transcription factor, whereas the gene loop includes only a repressor expressed on the same promoter as the enzyme. (B) Representative run of BayesOpt with cost-benefit objective showing the best objective function value (black line). All samples are colored by their architecture. Pie charts of each quarter of the run show continued exploration of all architectures despite clear stratification in losses. (C) Optimal trade-off curve between overshoot and rise time. The objective weight α was swept from α = 0.01 to α = 10,000 and BayesOpt was run for 100 iterations at each α value. The optimal parameter values were used to compute the rise time and overshoot for visualization. The inset shows three sample trajectories illustrating how different optimal architectures navigate the trade-off between overshoot and rise time.