Application of the Direct Quadrature Method of Moments for the modelling of the enzymatic hydrolysis of cellulose: I. Case of soluble substrate

Exploring the diversity of β-glucosidase: Classification, catalytic mechanism, molecular characteristics, kinetic models, and applications

High-value chemicals and energy-related products can be produced from biomass. Biorefinery technology offers a sustainable and cost-effective method for this high-value conversion. β-glucosidase is one of the key enzymes in biorefinery processes, catalyzing the production of glucose from aryl-glycosides and cello-oligosaccharides via the hydrolysis of β-glycosidic bonds. Although β-glucosidase plays a critical catalytic role in the utilization of cellulosic biomass, its efficacy is often limited by substrate or product inhibitions, low thermostability, and/or insufficient catalytic activity. To provide a detailed overview of β-glucosidases and their benefits in certain desired applications, we collected and summarized extensive information from literature and public databases, covering β-glucosidases in different glycosidase hydrolase families and biological kingdoms. These β-glucosidases show differences in amino acid sequence, which are translated into varying degrees of the molecular properties critical in enzymatic applications. This review describes studies on the diversity of β-glucosidases related to the classification, catalytic mechanisms, key molecular characteristics, kinetics models, and applications, and highlights several β-glucosidases displaying high stability, activity, and resistance to glucose inhibition suitable for desired biotechnological applications.

Approximate Moment Methods for Population Balance Equations in Particulate and Bioengineering Processes

Population balance modeling is an established framework to describe the dynamics of particle populations in disperse phase systems found in a broad field of industrial, civil, and medical applications. The resulting population balance equations account for the dynamics of the number density distribution functions and represent (systems of) partial differential equations which require sophisticated numerical solution techniques due to the general lack of analytical solutions. A specific class of solution algorithms, so-called moment methods, is based on the reduction of complex models to a set of ordinary differential equations characterizing dynamics of integral quantities of the number density distribution function. However, in general, a closed set of moment equations is not found and one has to rely on approximate closure methods. In this contribution, a concise overview of the most prominent approximate moment methods is given.