(418c) Prediction of Metabolic Function From Limited Data: Lumped Hybrid Cybernetic Modeling | AIChE

(418c) Prediction of Metabolic Function From Limited Data: Lumped Hybrid Cybernetic Modeling

Authors 

Song, H. - Presenter, Purdue University
Ramkrishna, D. - Presenter, Purdue University


Motivated by the need for a quick quantitative assessment of metabolic function without extensive data, we present an adaptation of the cybernetic framework, denoted as the lumped hybrid cybernetic model (or L-HCM), which combines the attributes of the classical lumped cybernetic model (LCM) and the recently developed HCM. The basic tenet of L-HCM and HCM is the same, i.e., they both view the uptake flux as being split among diverse pathways in an optimal way as a result of cellular regulation such that some chosen metabolic objective is realized. The L-HCM, however, portrays this flux distribution to occur in a hierarchical way, i.e., first among lumped pathways, and next among individual elementary modes in each lumped pathway. Both splits are described by the cybernetic control laws using operational and structural return-on-investments, respectively. That is, the distribution of uptake flux at the first split is dynamically regulated according to environmental conditions, while the subsequent split is based purely on the stoichiometry of EMs. The resulting model is conveniently represented in terms of lumped pathways which are fully identified with respect to yield coefficients of all products unlike classical LCMs based on instinctive lumping. These characteristics enable the model to account for the complete set of elementary modes for arbitrarily large metabolic networks despite containing only a small number of parameters which can be identified using minimal data.

In a case study involving aerobic batch growth of S. cerevisiae, L-HCM is compared with LCM. The former provides a much more satisfactory prediction than the latter when parameters are identified from a few primary metabolites. On the other hand, the classical model is more accurate than L-HCM when sufficient data sets are involved in parameter identification. In applying the two models to a chemostat scenario, L-HCM shows a reasonable prediction on metabolic shift from respiration to fermentation due to the Crabtree effect, which LCM predicts unsatisfactorily.

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