4.6 Article

Hybrid Dynamic Models of Bioprocesses Based on Elementary Flux Modes and Multilayer Perceptrons

Journal

PROCESSES
Volume 10, Issue 10, Pages -

Publisher

MDPI
DOI: 10.3390/pr10102084

Keywords

hybrid modeling; model reduction; dynamic models; metabolic network; elementary flux modes; identification; neural networks; multilayer perceptron; pruning; biotechnology; reaction systems

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The derivation of minimal bioreaction models is crucial for monitoring and controlling the production of cell/microorganism cultures. These models are obtained by selecting elementary flux modes and utilizing multilayer perceptrons for dynamic modeling, resulting in promising prediction results for culture production.
The derivation of minimal bioreaction models is of primary importance to develop monitoring and control strategies of cell/microorganism culture production. These minimal bioreaction models can be obtained based on the selection of a basis of elementary flux modes (EFMs) using an algorithm starting from a relatively large set of EFMs and progressively reducing their numbers based on geometric and least-squares residual criteria. The reaction rates associated with the selected EFMs usually have complex features resulting from the combination of different activation, inhibition and saturation effects from several culture species. Multilayer perceptrons (MLPs) are used in order to undertake the representation of these rates, resulting in a hybrid dynamic model combining the mass-balance equations provided by the EFMs to the rate equations described by the MLPs. To further reduce the number of kinetic parameters of the model, pruning algorithms for the MLPs are also considered. The whole procedure ends up with reduced-order macroscopic models that show promising prediction results, as illustrated with data of perfusion cultures of hybridoma cell line HB-58.

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