Parameter determination and validation for a mechanistic model of the enzymatic saccharification of cellulose-Iβ

PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall

Enzymatic digestion of lignocellulosic plant biomass is a key step in bio-refinery approaches for the production of biofuels and other valuable chemicals. However, the recalcitrance of this material in conjunction with its variability and heterogeneity strongly hampers the economic viability and profitability of biofuel production. To complement both academic and industrial experimental research in the field, we designed an advanced web application that encapsulates our in-house developed complex biophysical model of enzymatic plant cell wall degradation. PREDIG (https://predig.cs.hhu.de/) is a user-friendly, free, and fully open-source web application that allows the user to perform in silico experiments. Specifically, it uses a Gillespie algorithm to run stochastic simulations of the enzymatic saccharification of a lignocellulose microfibril, at the mesoscale, in three dimensions. Such simulations can for instance be used to test the action of distinct enzyme cocktails on the substrate. Additionally, PREDIG can fit the model parameters to uploaded experimental time-course data, thereby returning values that are intrinsically difficult to measure experimentally. This gives the user the possibility to learn which factors quantitatively explain the recalcitrance to saccharification of their specific biomass material.

A two-phase substrate model for enzymatic hydrolysis of lignocellulose: application to batch and continuous reactors

BACKGROUND

Enzymatic hydrolysis continues to have a significant projected production cost for the biological conversion of biomass to fuels and chemicals, motivating research into improved enzyme and reactor technologies in order to reduce enzyme usage and equipment costs. However, technology development is stymied by a lack of accurate and computationally accessible enzymatic-hydrolysis reaction models. Enzymatic deconstruction of cellulosic materials is an exceedingly complex physico-chemical process. Models which elucidate specific mechanisms of deconstruction are often too computationally intensive to be accessible in process or multi-physics simulations, and empirical models are often too inflexible to be effectively applied outside of their batch contexts. In this paper, we employ a phenomenological modeling approach to represent rate slowdown due to substrate structure (implemented as two substrate phases) and feedback inhibition, and apply the model to a continuous reactor system.

RESULTS

A phenomenological model was developed in order to predict glucose and solids concentrations in batch and continuous enzymatic-hydrolysis reactors from which liquor is continuously removed by ultrafiltration. A series of batch experiments were performed, varying initial conditions (solids, enzyme, and sugar concentrations), and best-fit model parameters were determined using constrained nonlinear least-squares methods. The model achieved a good fit for overall sugar yield and insoluble solids concentration, as well as for the reduced rate of sugar production over time. Additionally, without refitting model coefficients, good quantitative agreement was observed between results from continuous enzymatic-hydrolysis experiments and model predictions. Finally, the sensitivity of the model to its parameters is explored and discussed.

CONCLUSIONS

Although the phenomena represented by the model correspond to behaviors that emerge from clusters of mechanisms, and hence a set of model coefficients are unique to the substrate and the enzyme system, the model is efficient to solve and may be applied to novel reactor schema and implemented in computational fluid dynamics (CFD) simulations. Hence, this modeling approach finds the right balance between model complexity and computational efficiency. These capabilities have broad application to reactor design, scale-up, and process optimization.

Analysis of the Long Time Behavior of Enzymatic Cellulose Hydrolysis Kinetics

Enzymatic hydrolysis of biomass to produce sugars that can be converted to fuels and other valuable chemicals, is viewed as the prime technology for utilization of this renewable resource. To accelerate technology development, models are needed that are able to accurately predict the hydrolysis rate so that reactors can be tailored to the multitude of processing conditions and substrates that can be used. Of particular interest is the ability to predict the long time conversion in the hydrolysis reaction which dictates the maximum possible sugar concentration. It is our aim in this article to develop a simple model which is able to predict the long-term conversion of cellulose to soluble sugars. Drawing on the analogy from the theory of reactions in continuous mixtures, it is shown that analysis of the long time kinetics of hydrolysis by examining the behavior of the “lump” of the reacting material results in a simple expression which is capable of predicting the kinetics. Many features of actual enzyme systems can be included in the development of the hydrolysis model, such as the large size of the enzyme molecules, adsorption onto substrate, inhibition by different factors (solvent, glucose etc.), but, when the analysis is carried out to calculate the total sugar concentration, it is shown that the equations reduce to a simple expression. Analysis of this model is given with comparison to other models and experimental data available in the literature. In addition to predicting the long-term kinetics, it is shown that the model does a surprising job of predicting the initial hydrolysis rates as well.

Mechanistic kinetic models of enzymatic cellulose hydrolysis-A review

Bioconversion of lignocellulose forms the basis for renewable, advanced biofuels, and bioproducts. Mechanisms of hydrolysis of cellulose by cellulases have been actively studied for nearly 70 years with significant gains in understanding of the cellulolytic enzymes. Yet, a full mechanistic understanding of the hydrolysis reaction has been elusive. We present a review to highlight new insights gained since the most recent comprehensive review of cellulose hydrolysis kinetic models by Bansal et al. (2009) Biotechnol Adv 27:833-848. Recent models have taken a two-pronged approach to tackle the challenge of modeling the complex heterogeneous reaction-an enzyme-centric modeling approach centered on the molecularity of the cellulase-cellulose interactions to examine rate limiting elementary steps and a substrate-centric modeling approach aimed at capturing the limiting property of the insoluble cellulose substrate. Collectively, modeling results suggest that at the molecular-scale, how rapidly cellulases can bind productively (complexation) and release from cellulose (decomplexation) is limiting, while the overall hydrolysis rate is largely insensitive to the catalytic rate constant. The surface area of the insoluble substrate and the degrees of polymerization of the cellulose molecules in the reaction both limit initial hydrolysis rates only. Neither enzyme-centric models nor substrate-centric models can consistently capture hydrolysis time course at extended reaction times. Thus, questions of the true reaction limiting factors at extended reaction times and the role of complexation and decomplexation in rate limitation remain unresolved. Biotechnol. Bioeng. 2017;114: 1369-1385. © 2017 Wiley Periodicals, Inc.

Temperature profile optimization: potential for multi-enzymatic biopolymer depolymerization processes

Optimal control of temperature was applied to a population balance model of enzymatically catalyzed depolymerization of a soluble polymer coupled with denaturation of enzyme. The reaction time required to reach a desired yield was predicted to be reduced by more than 10[Formula: see text] compared with isothermal operation. Also the yield within a given time could be increased by more than 5[Formula: see text] points. It was also possible to increase the yield and reduce the reaction time if a time-varying temperature profile was used. Furthermore, a simple-to-implement linear increasing temperature profile was shown to realize most of the saving potential. Rigorous optimization of the enzyme mixture and composition was predicted to have an even greater potential for improving the economic feasibility of the process. Optimization coupled with optimal control can be performed quickly in silico using the algorithm developed in this study if a validated and parameterized population balance model is available.