a b s t r a c tWheat straw was submitted to a pre-treatment by the basidiomycetous fungi Euc-1 and Irpex lacteus, aiming to improve the accessibility of cellulose towards enzymatic hydrolysis via previous selective bio-delignification. This allowed the increase of substrate saccharification nearly four and three times while applying the basidiomycetes Euc-1 and I. lacteus, respectively. The cellulose/lignin ratio increased from 2.7 in the untreated wheat straw to 5.9 and 4.6 after the bio-treatment by the basidiomycetes Euc-1 and I. lacteus, respectively, thus evidencing the highly selective lignin biodegradation. The enzymatic profile of both fungi upon bio-treatment of wheat straw have been assessed including laccase, manganese-dependent peroxidase, lignin peroxidase, carboxymethylcellulase, xylanase, avicelase and feruloyl esterase activities. The difference in efficiency and selectivity of delignification within the two fungi treatments was interpreted in terms of specific lignolytic enzyme profiles and moderate xylanase and cellulolytic activities.

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In enzyme kinetic studies, linear transformations of the Michaelis–Menten equation, such as the Lineweaver–Burk double-reciprocal transformation, present some constraints. The linear transformation distorts the experimental error and the relationship between x and y axes; consequently, linear regression of transformed data is less accurate when compared with methodologies that use nonlinear regression. However, linear transformations are widely used. Explanations for this are the facility to determine model parameters by hand calculations, and until recently, the use of nonlinear regression was difficult as specialized software was not readily available to most scientists and students. Because utilization of personal computers is widespread, these constraints are no longer applicable. This work describes how to perform nonlinear regression with the Solver supplement of Microsoft Office Excel. It is easy to use and to view the results graphically. The F-test was applied to discriminate between models. These methodologies are important in any biochemistry syllabus and can be used to create an active-learning environment where students discriminate between different kinetic models and explore their own experimental results based on several hypotheses.

In order to establish which are the contribution of linear (total), hyperbolic (partial) or parabolic inhibitions by cellobiose, and also a special case of substrate inhibition, the kinetics of cellobiohydrolase Cel7A obtained from Trichoderma reesei was investigated. Values of kinetic parameters were estimated employing integrated forms of Michaelis-Menten equations through the use of non-linear regression, and criteria for selecting inhibition models are discussed. With cellobiose added at the beginning of the reaction, it was found that cellulose hydrolysis follows a kinetic model, which takes into account a mixed hyperbolic inhibition, by cellobiose with the following parameter values: K (m) 5.0 mM, K (ic) 0.029 mM, K (iu) 1.1 mM, k (cat) 3.6 h(-1) and k (cat') 0.2 h(-1). Cellulose hydrolysis without initial cellobiose added also follows the same inhibition model with similar values (4.7, 0.029 and 1.5 mM and 3.2 and 0.2 h(-1), respectively). According to Akaike information criterion, more complex models that take into account substrate and parabolic inhibitions do not increase the modulation performance of cellulose hydrolysis.

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