Selection of elementary modes for bioprocess control

Abstract

The lack of model-based information in bioreactor monitoring and control can have a profound impact on biological systems. We therefore aim to develop a model using elementary modes (EMs) that represents the observed phenotype in given environmental conditions suited for bioprocess control. Challenges in the model development were the high number of possible phenotypes of stoichiometric models and the high computational intensity. Two methods were compared to reduce the number of EMs to match the observed cellular phenotype. The first method is based on ranking modes and the second is a controlled random search (CRS) algorithm. Since we wish to obtain a biologically realistic subset of EMs, the objective function to be minimized is a trade-off between the error, efficiency of the modes, and model size. The case study considered the central carbon metabolism of Escherichia coli. The original model containing 2706 modes for case 1 and 11718 for case 2 was reduced to a system of one for case 1 and three modes for case 2 giving a good correlation with the measured data. Furthermore, considering also intracellular besides extracellular metabolites, results in a better fit of the measured rates. Finally, the CRS outperformed the ranking algorithm.