https://faculty.washington.edu/renatab/courses/PSE490/downloads/pretreatment.pdf

Features of promising technologies for pretreatment of lignocellulosic biomass.

Various agricultural residues, such as corn fiber, corn stover, wheat straw, rice straw, and sugarcane bagasse, contain about 20–40% hemicellulose, the second most abundant polysaccharide in nature. The conversion of hemicellulose to fuels and chemicals is problematic. In this paper, various pretreatment options as well as enzymatic saccharification of lignocellulosic biomass to fermentable sugars is reviewed. Our research dealing with the pretreatment and enzymatic saccharification of corn fiber and development of novel and improved enzymes such as endo-xylanase, β-xylosidase, and α-l-arabinofuranosidase for hemicellulose bioconversion is described. The barriers, progress, and prospects of developing an environmentally benign bioprocess for large-scale conversion of hemicellulose to fuel ethanol, xylitol, 2,3-butanediol, and other value-added fermentation products are highlighted.

Hemicellulose bioconversion

A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Working from the annotated genome sequence, as well as biochemical and physiological information, it is possible to reconstruct complete metabolic networks. Furthermore, computational methods have been developed to interpret and predict the optimal performance of a metabolic network under a range of growth conditions. We have tested the hypothesis that Escherichia coli uses its metabolism to grow at a maximal rate using the E. coli MG1655 metabolic reconstruction. Based on this hypothesis, we formulated experiments that describe the quantitative relationship between a primary carbon source (acetate or succinate) uptake rate, oxygen uptake rate, and maximal cellular growth rate. We found that the experimental data were consistent with the stated hypothesis, namely that the E. coli metabolic network is optimized to maximize growth under the experimental conditions considered. This study thus demonstrates how the combination of in silico and experimental biology can be used to obtain a quantitative genotype-phenotype relationship for metabolism in bacterial cells.

In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data

The Escherichia coli MG1655 genome has been completely sequenced. The annotated sequence, biochemical information, and other information were used to reconstruct the E. coli metabolic map. The stoichiometric coefficients for each metabolic enzyme in the E. coli metabolic map were assembled to construct a genome-specific stoichiometric matrix. The E. coli stoichiometric matrix was used to define the system's characteristics and the capabilities of E. coli metabolism. The effects of gene deletions in the central metabolic pathways on the ability of the in silico metabolic network to support growth were assessed, and the in silico predictions were compared with experimental observations. It was shown that based on stoichiometric and capacity constraints the in silico analysis was able to qualitatively predict the growth potential of mutant strains in 86% of the cases examined. Herein, it is demonstrated that the synthesis of in silico metabolic genotypes based on genomic, biochemical, and strain-specific information is possible, and that systems analysis methods are available to analyze and interpret the metabolic phenotype.

/pdf/the-escherichia-coli-mg1655-in-silico-metabolic-genotype-its-q3ay1kx2k5.pdf

The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities

Introduction. Cellular Growth Reactions. Analysis of Reaction Rates. Modelling of Reaction Kinetics. Morphologically Structured Models. Population Balances Based on Cell Number. Mass Transfer. Ideal Bioreactors. Bioreactor Modelling. Index.

Bioreaction Engineering Principles

Cybernetic models, developed earlier by the authors, have been evaluated experimentally for the growth of Klebsiella oxytoca in batch cultures using mixed substrates from glucose, xylose, arabinose, lactose, and fructose. Based entirely on information procured from batch growth on single substrates, the models accurately predict without further parameter fitting, diauxic growth on mixed substrates, automatically predicting the order in which the substrates are consumed. Even triauxic growth on a mixture of glucose, xylose, and lactose is predicted by the model based on single substrate data. Growth on glucose-fructose mixtures appears to need a slightly modified strategy for cybernetic variables.

Investigation of bacterial growth on mixed substrates: experimental evaluation of cybernetic models

It is known that 2,3-butanediol is a potentially valuable chemical feedstock that can be produced from the sugars present in hemicellulose and celluose hydrolysates. Klebsiella oxytoca is able to ferment most pentoses, hexoses, and disaccharides. Butanediol appears to be a primary metabolite, excreted as a product of energy methabolism. The theoretical maximum yield of butanediol from monosaccharides is 0.50 g/g. This article describes the effects of pH, xylose concentration, and the oxygen transfer rate on the bioconversion of D-xylose to 2,3-butanediol. Product inhibition by butanediol is also examined. The most important variable affecting the kinetics of this system appears to be the oxygen transfer rate. A higher oxygen supply favors the formation of cell mass at the expense of butanediol. Decreasing the oxygen supply rate increases the butanediol yield, but decreases the overall conversion rate due to a lower cell concentration.

Production of 2,3-butanediol from D-xylose by Klebsiella oxytoca ATCC 8724.

Bacillus polymyxa produces (R, R)-2,3-butanediol from a variety of carbohydrates. Other metabolites are also produced including acetoin, acetate, lactate, and ethanol. The excretion of each metabolite was found to depend on the relative availability of oxygen to the culture. When the relative oxygen uptake rate was high, enhanced yields of acetate and acetoin were noted. At an intermediate oxygen availability, the butanediol yield was maximal. When the availability of oxygen was more restricted, higher yields of lactate and ethanol occurred. The cells appeared to regulate themselves such that energy generation is optimal subject to the constraint that the cells do not produce more reducing equivalents than can be oxidized by the electron transport system. The dependence of each product yield on the relative oxygen availability was determined, and this knowledge was used to carry out a fed-batch fermentation that attained a final butanediol concentration of over 40 g/L in 50 h.

Production of optically active 2,3‐butanediol by Bacillus polymyxa

A cybernetic model to predict the low-growth-rate behavior of bacteria in mixed-substrate environment is presented. Using only growth and maintenance parameters from single-substrate experiments, the model accurately predicts the simultaneous substrate utilization and maintenance energy effects in constant fed-batch cultures of Klebsiella oxytoca. The robustness of the model was examined more rigorously by perturbing glucose-limited fed-batch cultures with additions of arabinose, xylose, and fructose. In all cases, reasonable agreement of the model prediction with the experimental data was observed.

Cybernetic modeling of bacterial cultures at low growth rates: mixed-substrate systems

The fermentation of D-xylose by theCandida sp. mutant XF217 was studied at different oxygen uptake rates by controlling dissolved oxygen tension or agitation rate. The ethanol yield increases as the oxygen uptake rate decreases. The ethanol production rate is maximized at an oxygen uptake rate of 9–12 millimole/L/hour. Xylitol and cell mass production and cell morphology are also affected by oxygen level.

Norman B. Jansen

Papers

Investigation of bacterial growth on mixed substrates: experimental evaluation of cybernetic models

Production of 2,3-butanediol from D-xylose by Klebsiella oxytoca ATCC 8724.

Production of optically active 2,3‐butanediol by Bacillus polymyxa

Cybernetic modeling of bacterial cultures at low growth rates: mixed-substrate systems

Effect of oxygen uptake rate on ethanol production by a xylose-fermenting yeast mutant,Candida sp. XF217