Kcat levels of enzymes are important for the study of metabolic systems. However, the current use of kcat poses great difficulties, as the values of most enzymes have not been measured experimentally and the available experimental values are often measured under non-physiological conditions, raising doubts about the relevance of kcat under in vivo conditions. We present an approach that uses Omics data to quantitatively analyze in vivo the relationship between kcat values in vitro and the maximum catalytic rate of enzymes. Our approach offers a high throughput method to obtain enzymatic kinetic constants that reflect in vivo conditions and are useful for more accurate and complete cellular metabolic models. The kmaxvivo range relative to kcat measurements. The pFBA solution was subjected to a flow variability analysis for all reactions (N = 132). The data points correspond to Figure 1. Two in the main text. The line y = x is shown in black; Dotted brown line represents the best orthogonal regression adaptation in log10; Error bars (usually so small that they are in the size of the points themselves) represent the area between the upper and lower kmaxvivo estimates. In summary, we made a unique enzymatic response with a bubble-based micromixer. This device could easily and quickly characterize enzymatic reaction constants.
Each recording was collected in 1 s. A rapid mixing of the enzyme and substrate in less than 100 ms was achieved thanks to an acoustic pulse bladder, anchored in a horseshoe structure. Our design overcame the low mixing speed and efficiency of previous microfluidic designs. The single-shot detection system does not require the preparation of additional samples for each parallel experiment, as the adaptation of the flow rates comfortably changes the concentration ratio. The continuous flow reaction made it possible to record all the concentration information of the product in the first high-resolution enzymatic reaction. The catalyzed hydrolysis β-galactosidase of resorufine β-D-Galactopyranoside was tested as a model system and km and kcat in this reaction were measured at 333 μM with a standard deviation of 130 μM and 64 s-1 with a standard deviation of 8 s-1. These values are in perfect harmony with the published results. Our approach reduced mixing time in the millisecond range and significantly increased efficiency in characterizing enzymatic reaction constants. It represents the ability to study the kinetics of high-speed enzymatic reactions with small amounts of enzymes, substrates or inhibitors. Overall, our results were fairly well compared to data reported by Hadd35et al. and Jambovane51et al.
There are several reasons for the discrepancies between our results and the published data. First, although the enzyme and substrate have been the same in each job, the enzyme activity may vary in different batches and the properties of the reaction buffer may not be consistent. Enzyme activity depends on its environment and these small differences can alter enzyme activity. Second, the flow model we used was an ideal plug-flow, which means that every part of the liquid has the same residence time. However, the flow conditions in the experiments were more complex, including a parabolic velocity profile and slight flow instabilities by injection pumps and channel inlets. Third, the lack of rapid mixing in previous work had an impact on the final results. Fourth, factors in the data analysis method and sample processing, such as the line analysis interval, influence the results. In our case, the values of Km and kcat depended on the response time. We also mentioned this trend in Figure 5b. Jambovane also mentioned that the use of alternative methods of data analysis could result in variations of up to 13% for km and 24% for kcat.51 4.
With the sample data, Prism reports that Km = 5,886 with a 95% confidence interval of 3,933 to 7,839. . . .