A Bayesian Study to Optimally Unveil LDH Kinetic Mechanisms In Vivo

Abstract

Many life-threatening diseases present characteristic changes in metabolic profiles throughout their progression. For example, an increase in lactic fermentation is characteristic of cancer and is described by the Warburg effect. Yet, only a few methodologies allow for detecting these changes non-invasively and in real-time. Furthermore, the frequent oversight of the systems biochemical mechanisms and the elevated time and resources required for experimentation risk stagnating research. Here, we address both problems thanks to mathematical modelling and Bayesian statistics. Thanks to modelling, we guided understanding of the lactate dehydrogenase system, highlighting the importance of cellular microenvironment effects, membrane transport and enzymatic repression. To deal with increasing model complexity, we introduced and validated a novel Bayesian optimal experimental design approach to maximise the informative content of experiments. Hence, our approach generated fast and efficient lactate dehydrogenase kinetics characterisation, resulting in generalisable and reliable predictions in vitro and in vivo.