Deep-learning-assisted medium optimization improves hyaluronic acid production by Streptococcus zooepidemicus.

Abstract

To improve the efficiency of hyaluronic acid production by Streptococcus zooepidemicus, the growth medium was optimized with a pipeline involving a deep learning (DL) algorithm. To train the DL model, the initial training dataset (OA01-18) was designed with the L18 orthogonal array, and hyaluronic acid (HA) was produced in small-scale cultures in deepwell plates. The range of HA production was 0.09-1.39 g/L under these conditions. In searching for the optimal medium composition, 54 candidate optimized media (OM01-54) were proposed by the system. According to the confirming culture experiment, the best production of HA (1.66 g/L) was achieved with OM30. During confirmation in a stirred-tank reactor, the volumetric production of HA in OA30 was larger than that in the control medium. In fed batch culture, HA accumulated to 5.13 and 9.96 g/L_{initial volume} after 10 and 30 h in culture, respectively. To avoid the suppression of HA production by the high viscosity of the medium conferred by HA, repeated batch culture with OM30 was performed by replacing 90 % of the broth volume approximately every 6 h. As a result, 21.4 g of HA was produced in 46 h, and productivity reached 0.465 g/L_{initial volume}/h.