Abstract
This paper presents a data-driven methodology to infer a macroscopic reaction scheme with stoichiometric parameters from a bioprocess database. The data sets consist of measurements of a few extracellular species, i.e., biomass, substrates, and products of interest. The proposed original procedure is based on implicit sparse identification. The methodology is illustrated with two case studies: (i) data generated by a two-step anaerobic digestion model and (ii) an experimental data set from the production of therapeutic proteins using mammalian cell cultures. Finally, the results of the latter application are compared with a standard data-driven algorithm, e.g., maximum-likelihood principal component analysis.
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