Interlinked population balance and cybernetic models for the simultaneous saccharification and fermentation of natural polymers

Abstract

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.

Effect of oxygen transfer on yeast growth — Growth kinetic and reactor model to estimate scale-up effects in bioreactors

“…This is expected, as detailed metabolic routes were not included in the model. Structured modelling approaches such as cybernetic modelling (Ho et al, 2015;Ramkrishna and Song, 2012), genome-scale models (GEMs) or structured kinetic models (Heijnen, 2005;Kerkhoven et al, 2015) may enable more rigorous description of the observed phenomena. However, the goal here is to describe a model with low computational load to be applied in bioreactor simulations.…”

Modelling simultaneous chain‐end and random scissions using the fixed pivot technique

“…With appropriate initial conditions, the above ODEs can be solved to obtain C i ( t )'s. In general, ( j = α or γ) is constant, but there are cases where is DP‐dependent or it may assume forms that are related to the nature of the process . For the sake of comparison with the continuous distribution, molar concentration ( C i ) in the discrete equations above can be used interchangeably with molar concentration density ( c i ) as the latter equals C i divided by a unit DP interval.…”

“…$O(10 5 )) can be solved by using commercial ODE integrators for the purpose of validation. [38] In general, Equations (1)-(3) form a linear system of ODEs which can be written in the following form:…”

“…Having this deficiency would certainly impair the accuracy of the resulting combined predictions. In cases where the evolution of the complete chain‐length distribution is important, the capability of the method of moments and its variants to produce such information is “at best small.”…”

Modeling coordinated enzymatic control of saccharification and fermentation by Clostridium thermocellum during consolidated bioprocessing of cellulose

“…These unstructured models are often unable to provide a mechanistic understanding of coupled multi‐physics phenomena in cellulose bioconversion, including SSF and CBP. By contrast, Ho et al (2015a, 2015b) made a significant step forward in this regard by accounting for the coupled dynamics of polymeric saccharification and regulation of cellular metabolism during fermentation by interlinking population balances and cybernetic models. Despite improved predictions compared to existing approaches, this model still has limited capability of providing mechanistic details due to: (1) oversimplification of fermentation pathways into a single‐step reaction and (2) lack of consideration of extensive recalcitrance through heterogeneous structure and properties of cellulose.…”