Fig. 1. ART provides predictions and recommendations for the next cycle.

ART uses experimental data (input and responses in the left side) to (i) build a probabilistic predictive model that predicts response (e.g., production) from input variables (e.g., proteomics), and (ii) uses this model to provide a set of recommended inputs for the next experiment (new input) that will help reach the desired goal (e.g., increase response/production). The input phase space, in this case, is composed of all the possible combinations of protein expression levels (or transcription levels, promoters,... for other cases). The predicted response for the recommended inputs is characterized as a full probability distribution, effectively quantifying prediction uncertainty. Instances refer to each of the different examples of input and response used to train the algorithm (e.g., each of the different strains and/or conditions, that lead to different production levels because of different proteomics profiles). See Fig. 2 for details on the predictive model and Fig. 3 for details on the recommendation strategy. An example of the output can be found in Supplementary Fig. 5.