High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering

Abstract

As synthetic biology enters the era of quantitative biology, mathematical information such as kinetic parameters of enzymes can offer us an accurate knowledge of metabolism and growth of cells, and further guidance on precision metabolic engineering. kcat, termed the turnover number, is a basic parameter of enzymes that describes the maximum number of substrates converted to products each active site per unit time. It reflects enzyme activity and is essential for quantitative understanding of biosystems. Usually, the kcat values are measured in vitro, thus may not be able to reflect the enzyme activity in vivo. In this case, Davidi et al. defined a surrogate kmaxvivo (kapp) for kcat and developed a high throughput method to acquire kmaxvivo from omics data. Heckmann et al. and Chen et al. proved that the surrogate parameter can be a good embodiment of the physiological state of enzymes and exhibit superior performance for enzyme-constrained metabolic model to the default one. These breakthroughs will fuel the development of system and synthetic biology.

Keywords

• Turnover number
• Genome scale models
• High throughput
• Metabolic reconstitution
• Machine learning