Experimental data and computational simulations used for the work “A Bayesian Study to Optimally Unveil LDH Kinetic Mechanisms In Vivo”. In this work, we use hyperpolarised 13C nuclear magnetic resonance (combined with traditional biological assays) and mathematical modelling to characterise the enzymatic kinetics of lactate dehydrogenase (LDH). First, we characterise the properties of the system (e.g., relationship between signal and cell number, effect of substrate concentration, MCT membrane transporters, etc.). Then, we introduce mathematical modelling for the system, stressing the importance of developing mathematical expressions that can, structurally, represent the general experimental data for the system. To estimate the model parameters, here we use Bayesian statistics to infer parameter posteriors. To tackle real cases like this one, with high complexity of the best model developed and the slow speed of MCMC algorithms with high amounts of data, we developed a computationally feasible algorithm for Bayesian optimal experimental design (bOED). We prove the validity of this new algorithm, showcasing its advantages to reduce experimentation and computations by designing experiments that maximise the informative content with respect to a problem (here, inference of model parameter posteriors). Finally, we use bOED to efficiently infer model parameters for a general in vivo LDH model. To process the repository data or use it for Bayesian inferences, access the Julia code developed for this work at https://github.com/DavidGomezCabeza/MM_bOED_HP-NMR and change the necessary directory paths. (2026-03-24)