Fig 1.
Strain design optimization loop using reinforcement learning.
Enzyme levels corresponding to the strain i are denoted as ei, and yi and si, correspond to the response (used in reward) and output concentrations (used as state), respectively. The action (ai), corresponding to the difference of the enzyme levels in the two consecutive iterations, is given by the policy learned with MMR.
Fig 2.
Illustration of strain design optimization using the MARL approach for succinic acid.
The first 6 rows correspond to the enzyme levels and in the last row the response (product exchange flux * growth) is presented. Each column represents the enzyme levels design and the corresponding response which has been found in the iteration mentioned at the bottom.
Table 1.
List of the investigated products of interest and the corresponding enzymes whose levels were optimized, selected based on [34] and [36].
Fig 3.
The development of the median (solid lines) and 25th to the 75th percentile (shaded areas) of the response (growth*production, C-mmol biomass/(g CDW h)*mmol/(g CDW h)) in k-ecoli457 model using MARL, BO-GP and RAND, in acetate (top), ethanol (middle) and succinic acid (bottom) production.
Table 2.
Mean and standard deviations of the strain stability measure (RSD) obtained with algorithms.
For each product and each method mean and standard deviation among 20 computed RSDs are shown.
Fig 4.
Average percentage of median response decrements at iteration 40 over 3 studied products, with respect to the baseline with no noise.
Fig 5.
Mean and 25th to 75th percentile (shaded areas) of the best GFP synthesis rate (MFI/h), using MARL, BO-GP and RAND.