Stochastic model of lignocellulosic material saccharification

A detailed sensitivity analysis identifies the key factors influencing the enzymatic saccharification of lignocellulosic biomass

Corn stover is the most abundant form of crop residue that can serve as a source of lignocellulosic biomass in biorefinery approaches, for instance for the production of bioethanol. In such biorefinery processes, the constituent polysaccharide biopolymers are typically broken down into simple monomeric sugars by enzymatic saccharification, for further downstream fermentation into bioethanol. However, the recalcitrance of this material to enzymatic saccharification invokes the need for innovative pre-treatment methods to increase sugar conversion yield. Here, we focus on experimental glucose conversion time-courses for corn stover lignocellulose that has been pre-treated with different acid-catalysed processes and intensities. We identify the key parameters that determine enzymatic saccharification dynamics by performing a Sobol's sensitivity analysis on the comparison between the simulation results from our complex stochastic biophysical model, and the experimental data that we accurately reproduce. We find that the parameters relating to cellulose crystallinity and those associated with the cellobiohydrolase activity are predominantly driving the enzymatic saccharification dynamics. We confirm our computational results using mathematical calculations for a purely cellulosic substrate. On the one hand, having identified that only five parameters drastically influence the saccharification dynamics allows us to reduce the dimensionality of the parameter space (from nineteen to five parameters), which we expect will significantly speed up our fitting algorithm for comparison of experimental and simulated saccharification time-courses. On the other hand, these parameters directly highlight key targets for experimental endeavours in the optimisation of pre-treatment and saccharification conditions. Finally, this systematic and two-fold theoretical study, based on both mathematical and computational approaches, provides experimentalists with key insights that will support them in rationalising their complex experimental results.

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.

Opportunities in the microbial valorization of sugar industrial organic waste to biodegradable smart food packaging materials

Many petroleum-derived plastics, including food packaging materials are non-biodegradable and designed for single-use applications. Annually, around 175 Mt. of plastic enters the land and ocean ecosystems due to mismanagement and lack of techno economically feasible plastic waste recycling technologies. Renewable sourced, biodegradable polymer-based food packaging materials can reduce this environmental pollution. Sugar production from sugarcane or sugar beet generates organic waste streams that contain fermentable substrates, including sugars, acids, and aromatics. Microbial metabolism can be leveraged to funnel those molecules to platform chemicals or biopolymers to generate biodegradable food packaging materials that have active or sensing molecules embedded in biopolymer matrices. The smart package can real-time monitor food quality, assure health safety, and provide economic and environmental benefits. Active packaging materials display functional properties such as antimicrobial, antioxidant, and light or gas barrier. This article provides an overview of potential biodegradable smart/active polymer packages for food applications by valorizing sugar industry-generated organic waste. We highlight the potential microbial pathways and metabolic engineering strategies to biofunnel the waste carbon efficiently into the targeted platform chemicals such as lactic, succinate, muconate, and biopolymers, including polyhydroxyalkanoates, and bacterial cellulose. The obtained platform chemicals can be used to produce biodegradable polymers such as poly (butylene adipate-co-terephthalate) (PBAT) that could replace incumbent polyethylene and polypropylene food packaging materials. When nanomaterials are added, these polymers can be active/smart. The process can remarkably lower the greenhouse gas emission and energy used to produce food-packaging material via sugar industrial waste carbon relative to the petroleum-based production. The proposed green routes enable the valorization of sugar processing organic waste into biodegradable materials and enable the circular economy.