Fig. 4: Application of METIS for optimization of a transcription & translation unit.

a The cell-free expression of sfGfp (super-folder Gfp) using plasmid, linear DNA (PCR) and linear DNA plus GamS protein, a nuclease inhibitor that protects linear DNA from degradation. The bars and the error bars are the average and standard deviation of triplicates (n = 3 independent experiments), respectively. b Design of a transcription & translation unit controlled by variants of a T7 promoter, ribosome binding site (RBS), N-terminal amino acids 3, 4, and 5, and the last two C-terminal amino acids. The combinatorial transcription & translation units are expressed from linear DNA in the TXTL system consisting of the E. coli lysate, buffer and energy mix, as well as purified GamS and T7 RNA polymerase. c The plot representing the average of triplicates (n = 3 independent experiments) as the result of 4 rounds of active learning, with 50 transcription & translation units tested per round. The yield is the Gfp fluorescence readout after 6 hours at 30 °C normalized by the same value from the reference constructs commonly used in the lab (Methods). The gray lines show the median. d A list of 20 most informative combinations of 4-day active learning performed in the cell-free system (c) was downloaded and the combinations were cloned in a vector and transformed into E. coli DH10β harboring a plasmid expressing auto-regulated T7 RNA polymerase (Methods). e Cell-free versus in vivo yields (average and standard deviation of triplicates, n = 3 independent experiments) for the 20 most informative combinations. f In vivo yield results (average of triplicates, n = 3 independent experiments) of Day 0 (20 most informative combinations) and Day 1 (suggested by the workflow). The gray lines show the median. The Google Colab Python notebook and all active learning data (combinations and yields) in this figure are available at https://github.com/amirpandi/METIS. Source data for a, e are provided as a Source Data file.