Bio-photovoltaic devices (BPVs) harness photosynthetic organisms to produce bioelectricity in an eco-friendly way. However, their low energy efficiency is still a challenge. A comprehension of metabolic constraints can result in finding strategies for efficiency enhancement. This study presents a systemic approach based on metabolic modeling to design a regulatory defined medium, reducing the intracellular constraints in bioelectricity generation of Synechocystis sp. PCC6803 through the cellular metabolism alteration. The approach identified key reactions that played a critical role in improving electricity generation in Synechocystis sp. PCC6803 by comparing multiple optimal solutions of minimal and maximal NADH generation using two criteria. Regulatory compounds, which controlled the enzyme activity of the key reactions, were obtained from the BRENDA database. The selected compounds were subsequently added to the culture media, and their effect on bioelectricity generation was experimentally assessed. The power density curves for different culture media showed the BPV fed by Synechocystis sp. PCC6803 suspension in BG-11 supplemented with NH4Cl achieved the maximum power density of 148.27 mW m-2. This produced power density was more than 40.5-fold of what was obtained for the BPV fed with cyanobacterial suspension in BG-11. The effect of the activators on BPV performance was also evaluated by comparing their overpotential, maximum produced power density, and biofilm morphology under different conditions. These findings demonstrated the crucial role of cellular metabolism in improving bioelectricity generation in BPVs.

Pichia pastoris as an efficient host for the production of recombinant proteins is mostly cultivated in fed-batch mode in which the cell’s environment is continuously changing. Therefore, to fine-tune bioreactor performance in respect to the associated metabolic changes of the microorganism, it is crucial to understand the influence of feeding strategy parameters on the intracellular reaction network. In this study, dynamic flux balance analysis (DFBA) integrated with transcriptomics data was used to simulate the recombinant P.pastoris (Muts) growth during induction phase for 3 fed-batch µ-stat strategies. The induction phase was divided into equal time intervals and the correlated reactions with protein yield were identified in the 3 fed-batch strategies using the Pearson correlation coefficient. Subsequently, Principal Component Analysis was applied to cluster induction phase time intervals and identify the role of correlated reactions on metabolic differentiation of time intervals. It was found that increasing fluxes through the methanol dissimilation pathway increased protein yield. By adding a methanol assimilation pathway inhibitor (HgCl2) to the shake flask medium containing 10% (v/v) glycerol, the protein titer increased by 60%. Using the DFBA revealed that the higher the dimensionless flux of methanol, the higher amounts of protein yield. Finally, a novel feeding strategy was developed so that the dimensionless methanol flux increased compared to the performed cultivations. Protein titer increased by 16% compared to the optimally performed cultivation, while production yield increased by 85%.

Ammonia is a toxic byproduct of CHO cell metabolism, which inhibits cell growth, reduces cell viability, alters glycosylation, and decreases recombinant protein productivity. In an attempt to minimize the ammonium accumulation in cell culture media, different amino acids were added individually to the culture medium before the production phase to alleviate the negative effects of ammonium on cell culture performance. Among all the amino acids examined in this study, valine showed the most positive impact on CHO cell culture performance. When the cultured CHO cells were fed with 5 mM valine, EPO titer was increased by 25% compared to the control medium, and ammonium and lactate production were decreased by 23 and 26%, respectively, relative to the control culture. Moreover, the sialic acid content of the EPO protein in valine-fed culture was higher than in the control culture, most likely because of the lower ammonium concentration. Flux balance analysis (FBA) results demonstrated that the citric acid cycle was enriched by valine feeding. The analysis revealed that there might be a link between promoting tricarboxylic acid (TCA) cycle metabolism in valine-fed culture and reduction in lactate and ammonia accumulation. Furthermore, in valine-fed culture, FBA outcomes showed that alanine was excreted into the medium as the primary mechanism for reducing ammonium concentration. It was predicted that the elevated TCA cycle metabolism was concurrent with an increment in recombinant protein production. Taken together, our data demonstrate that valine addition could be an effective strategy for mitigating the negative impacts of ammonium and enhancing glycoprotein production in both quality and quantity.

pH is an important factor affecting the growth and production of microorganisms; especially, it is effective on the efficiency of ethanologenic microorganisms. It can change the ionization state of metabolites via the change in the charge of their functional groups that may lead to metabolic alteration. Here, we estimated the ionization state of metabolites and balanced the charge of reactions in genome-scale metabolic models of Saccharomyces cerevisiae, Escherichia coli, and Zymomonas mobilis at pH levels 5, 6, and 7. The robustness analysis was first implemented to anticipate the effect of proton exchange flux on growth rates for the constructed metabolic models at various pH. In accordance with previous experimental reports, the models predict that Z. mobilis is more sensitive to pH rather than S. cerevisiae and the yeast is more regulated by pH rather than E. coli. Then, a systemic approach was proposed to predict the pH effect on metabolic change and to find effective reactions on ethanol production in S. cerevisiae. The correlated reactions with ethanol production at predicted optimal pH in a range of proton exchange rates determined by robustness analysis were identified using the Pearson correlation coefficient. Then, fluxes of these reactions were applied to cluster the various pHs by principal component analysis and to identify the role of these reactions on metabolic differentiation because of pH change. Finally, 12 reactions were selected for up and down-regulation to improve ethanol production. Enzyme Regulators of the selected reactions were identified using the Brenda database and 11 selected regulators were screened and optimized via Plackett-Burman and 2-level full factorial designs, respectively. The proposed approach has enhanced yields of ethanol from 0.18 to 0.36 mol/mol carbon. Hence, not only a comprehensive approach for understanding the effect of pH on metabolism was proposed in this work, but also it successfully introduced key manipulations for ethanol overproduction.