SMB2023 Archie M. Grifin West Ballroom in The Ohio Union
Better approximations can’t make up for poor experiments: Enzyme kinetics as case study
Arthur Winfree Prize
Monday, July 17
Plenary-01 : Arthur Winfree Prize
Dean of the College of Science; Professor of Biological Sciences; Professor of Applied and Computational Mathematics and Statistics
University of Notre Dame, USA
Enzymes are essential for life, catalyzing the chemical reactions that allow our cells to function. However, the experimental uncertainty of physical constants for enzyme-catalyzed reactions can lead to irreproducible measurements and inaccurate models. In this study, we estimated the experimental uncertainty of the Michaelis constant, one of the most used enzyme constants in mathematical models, for enzyme catalyzed reactions measured under 'identical' experimental conditions. We found that the mean error of Michaelis constant measurements is estimated to be up to 10.8 fold units. Our analysis reveals that the experimental uncertainty is not due to mathematical approximations derived by mathematical biologists, but rather to biases in experiment design and data reporting. These findings highlight the need for greater standardization of measurements for physical constants in the life sciences, as this will improve our ability to develop and implement predictive models. If mathematical biologists want to develop predictive models that are accurate and reliable, they need to play a more active role in the standardization of measurements in the life sciences.