Optimization of pH and nitrogen for enhanced hydrogen production by Synechocystis sp. PCC 6803 via statistical and machine learning methods.

The nitrogen (N) concentration and pH of culture media were optimized for increased fermentative hydrogen (H(2)) production from the cyanobacterium, Synechocystis sp. PCC 6803. The optimization was conducted using two procedures, response surface methodology (RSM), which is commonly used, and a memory-based machine learning algorithm, Q2, which has not been used previously in biotechnology applications. Both RSM and Q2 were successful in predicting optimum conditions that yielded higher H(2) than the media reported by Burrows et al., Int J Hydrogen Energy. 2008;33:6092-6099 optimized for N, S, and C (called EHB-1 media hereafter), which itself yielded almost 150 times more H(2) than Synechocystis sp. PCC 6803 grown on sulfur-free BG-11 media. RSM predicted an optimum N concentration of 0.63 mM and pH of 7.77, which yielded 1.70 times more H(2) than EHB-1 media when normalized to chlorophyll concentration (0.68 +/- 0.43 micromol H(2) mg Chl(-1) h(-1)) and 1.35 times more when normalized to optical density (1.62 +/- 0.09 nmol H(2) OD(730) (-1) h(-1)). Q2 predicted an optimum of 0.36 mM N and pH of 7.88, which yielded 1.94 and 1.27 times more H(2) than EHB-1 media when normalized to chlorophyll concentration (0.77 +/- 0.44 micromol H(2) mg Chl(-1) h(-1)) and optical density (1.53 +/- 0.07 nmol H(2) OD(730) (-1) h(-1)), respectively. Both optimization methods have unique benefits and drawbacks that are identified and discussed in this study.