(289d) Modelling Simultaneous Saccharification and Fermentation of Natural Polymers: Population Balance Interlinked with Cybernetic Modelling

The generation of important and useful products (e.g. ethanol, lactic acid etc.) through microbial fermentation often involves the breakdown of complex polymeric feedstock such as starch and cellulose through enzymatic scissions followed by subsequent metabolic conversion. The interplay between the kinetics of enzymatic depolymerization and the response of the microbes towards changes in the abiotic phase is critical for the adequate description of such a complex process. In this work, two unrelated frameworks, i.e. the Population Balance Modelling (PBM) and the Cybernetic Modelling (CM) were interlinked to model such a system. Specifically, the PBM technique was used to describe the enzymatic depolymerization whereas the CM framework was used to model the microbial response toward complex environmental changes. The interlinked PBM and CM framework was implemented on two case studies involving the Simultaneous Saccharification and Fermentation (SSF) of starch by two recombinant yeast strains capable of excreting glucoamylase alone or together with α-amylase. The simulation results revealed that the proposed framework captured features not attainable by existing approaches. Examples of such include the ability of the model to indicate (in case study one) that an appropriate amount of glucose mixed with starch as initial substrates yielded an optimum productivity of ethanol. Not only that, the model showed (in case study two) that SSF is indifferent to the type of starch when both enzymes are present as opposed to when only glucoamylase is present. Thus, the effect of various enzymatic actions on the temporal evolution of the polymer distribution and how the microbes respond to the initial molecular distribution of the polymers can be studied. Such a framework also enables a more molecular and fundamental study of a complex SSF system, a feat which heretofore was unattainable by existing SSF models.