In a recent article, Song and Ramkrishna (Song and Ramkrishna [2010]. Biotechnol Bioeng 106(2):271-284) proposed a lumped hybrid cybernetic model (L-HCM) towards extracting maximum information about metabolic function from a minimum of data. This approach views the total uptake flux as distributed among lumped elementary modes (L-EMs) so as to maximize a prescribed metabolic objective such as growth or uptake rate. L-EM is computed as a weighted average of EMs where the weights are related to the yields of vital products (i.e., biomass and ATP). In this article, we further enhance the predictive power of L-HCMs through modifications in lumping weights with additional parameters that can be tuned with data viewed to be critical. The resulting model is able to make predictions of diverse metabolic behaviors varying greatly with strain types as evidenced from case studies of anaerobic growth of various Escherichia coli strains. Incorporation of the new lumping formula into L-HCM remarkably improves model predictions with a few critical data, thus presenting L-HCM as a dynamic tool as being not only qualitatively correct but also quantitatively accurate.
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