Data integration across conditions improves turnover number estimates and metabolic predictions

Abstract

Turnover numbers characterize a key property of enzymes, and their usage in constraint-based metabolic modeling is expected to increase prediction accuracy of diverse cellular phenotypes. In vivo turnover numbers can be obtained by ranking of estimates obtained by integrating reaction rate and enzyme abundance measurements from individual experiments; yet, their contribution to improving predictions of condition-specific cellular phenotypes remains elusive. Here we show that available in vitro and in vivo turnover numbers lead to poor prediction of condition-specific growth rates with protein-constrained models of Escherichia coli and Saccharomyces cerevisiae, particularly in the ultimate test scenario when protein abundances are integrated in the model. We demonstrate that in vivo estimation of turnover number by simultaneous consideration of heterogeneous physiological data leads to improved prediction of condition-specific growth rates. Moreover, the obtained estimates are more precise than the available in vivo turnover numbers. Therefore, our approach provides the means to decrease the bias of in vivo turnover numbers and paves the way towards cataloguing in vivo kcatomes of other organisms.