Simultaneous Saccharification and Fermentation (SSF) is a process where microbes have to first excrete extracellular enzymes to break polymeric substrates such as starch or cellulose into edible nutrients, followed by in situ conversion of those nutrients into more valuable metabolites via fermentation. As such, SSF is very attractive as a one-pot synthesis method of biological products. However, due to the co-existence of multiple biochemical steps, modeling SSF faces two major challenges. The first is to capture the successive chain-end and/or random scission of the polymeric substrates over time, which determines the rate of generation of various fermentable substrates. The second is to incorporate the response of microbes, including their preferential substrate utilization, to such a complex broth. Each of the above-mentioned challenges has manifested itself in many related areas, and has been competently but separately attacked with two diametrically different tools, i.e., the Population Balance Modeling (PBM) and the Cybernetic Modeling (CM), respectively. To date, they have yet to be applied in unison on SSF resulting in a general inadequacy or haphazard approaches to examine the dynamics and interactions of depolymerization and fermentation. To overcome this unsatisfactory state of affairs, here, the general linkage between PBM and CM is established to model SSF. A notable feature is the flexible linkage, which allows the individual PBM and CM models to be independently modified to the desired levels of detail. A more general treatment of the secretion of extracellular enzyme is also proposed in the CM model. Through a case study on the growth of a recombinant Saccharomyces cerevisiae capable of excreting a chain-end scission enzyme (glucoamylase) on starch, the interlinked model calibrated using data from the literature (Nakamura et al., Biotechnol. Bioeng. 53:21-25, 1997), captured features not attainable by existing approaches. In particular, the effect of various enzymatic actions on the temporal evolution of the polymer distribution and how the microbes respond to the diverse polymeric environment can be studied through this framework. Biotechnol. Bioeng. 2015;112: 2084-2105. (c) 2015 Wiley Periodicals, Inc.

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