The Thermodynamic Links between Substrate, Enzyme, and Microbial Dynamics in Michaelis-Menten-Monod Kinetics

DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates

The enzyme turnover rate, ${k}_{cat}$, quantifies enzyme kinetics by indicating the maximum efficiency of enzyme catalysis. Despite its importance, ${k}_{cat}$ values remain scarce in databases for most organisms, primarily because of the cost of experimental measurements. To predict ${k}_{cat}$ and account for its strong temperature dependence, DLTKcat was developed in this study and demonstrated superior performance (log10-scale root mean squared error = 0.88, R-squared = 0.66) than previously published models. Through two case studies, DLTKcat showed its ability to predict the effects of protein sequence mutations and temperature changes on ${k}_{cat}$ values. Although its quantitative accuracy is not high enough yet to model the responses of cellular metabolism to temperature changes, DLTKcat has the potential to eventually become a computational tool to describe the temperature dependence of biological systems.

Electrodialysis ion-exchange membrane bioreactor (EDIMB) to remove nitrate from water: Optimization of operating conditions and kinetics analysis

Nitrate pollution has become a worldwide problem. In this study, we remove nitrate from water by electrodialysis ion-exchange membrane bioreactor (EDIMB) and enabling simultaneous nitrate enrichment and denitrification. In this reactor, nitrate migrated from the water chamber to the biological chamber via electrodialysis and was degraded by microorganisms. The effects of voltage and biomass concentration on the reactor performance were examined, and the kinetics data of the water chamber and biological chamber were fitted. The experimental results showed that the migration of nitrate in the water chamber conformed to the first-order model, and the constructed zero-Michaelis-Menten model described changes in nitrate concentration in the biological chamber. Furthermore, when the inflow nitrate concentration was 40 mg N/L, 5 V was the best voltage, and 3.00 g VSS/L was the best biomass concentration. The nitrate removal rate in the water chamber was 98.94%, and there was no accumulation of nitrate or nitrite in the biological chamber. Compared with traditional ED processes, the nitrate removal efficiency was 8.86% higher, and the current efficiency was 22.14% higher. The total organic carbon (TOC) of the water chamber was only 1.43 mg C/L, which proves that the structure of the EDIMB confined the denitrifying bacteria and organic carbon donors in the biological chamber and avoided secondary pollution in the water chamber. Microbial community analysis showed that Thauera (66.06%) was the dominant bacterium in the EDIMB system, and Azoarcus (9.81%) was a minor denitrifying genus.