Despite an increased knowledge of microbial xylanolytic systems in the past few years, further studies are required to achieve a complete understanding of the mechanism of xylan degradation by microorganisms and their enzymes. The enzyme system used by microbes for the metabolism of xylan is the most important tool for investigating the use of the second most abundant polysaccharide (xylan) in nature. Recent studies on microbial xylanolytic systems have generally focussed on induction of enzyme production under different conditions, purification, characterization, molecular cloning and expression, and use of enzyme predominantly for pulp bleaching. Rationale approaches to achieve these goals require a detailed knowledge of the regulatory mechanism governing enzyme production. This review will focus on complex xylan structure and the microbial enzyme complex involved in its complete breakdown, studies on xylanase regulation and production and their potential industrial applications, with special reference to biobleaching.

The influence of the high intracellular concentration of macromolecules on cell physiology is increasingly appreciated, but its impact on system-level cellular functions remains poorly quantified. To assess its potential effect, here we develop a flux balance model of Escherichia coli cell metabolism that takes into account a systemslevel constraint for the concentration of enzymes catalyzing the various metabolic reactions in the crowded cytoplasm. We demonstrate that the model's predictions for the relative maximum growth rate of wild-type and mutant E. coli cells in single substratelimited media, and the sequence and mode of substrate uptake and utilization from a complex medium are in good agreement with subsequent experimental observations. These results suggest that molecular crowding represents a bound on the achievable functional states of a metabolic network, and they indicate that models incorporating this constraint can systematically identify alterations in cellular metabolism activated in response to environmental change.flux balance analysis ͉ metabolic networks ͉ systems biology A n important aim of systems biology is the identification of the organizing principles and fundamental constraints that characterize the function of molecular interaction networks and the limits of an organism's phenotypic diversity (1-3). Flux balancebased modeling approaches, combining the constraints imposed by the metabolic network's structure with, e.g., mass-or energyconservation principles (3-6), are especially successful in providing experimentally testable predictions on an organism's metabolic flux state and growth rate. A relative shortcoming of these approaches, however, is that they do not take into account the physical and spatial constraints resulting from the cell's unique intracellular milieu (7-9). For example, Ϸ20-30% of the Escherichia coli cytoplasm is occupied by macromolecules, many of them enzymes, whose cytoplasmic concentration cannot be further increased without drastically affecting protein folding, protein-protein association rates, biochemical reaction kinetics, and transport dynamics within a cell (9, 10). This suggests that constraint-based modeling approaches, such as flux balance analysis (FBA) (3, 11), could be improved if we take into account that the enzymes catalyzing each reaction compete for the available cytoplasmic space (12, 13), potentially limiting the attainable flux rates.Current flux balance-based modeling approaches also have limited ability to predict substrate uptake from the environment. Extensive experimental data indicates that when grown in complex medium bacterial cells use the available substrates either preferentially or simultaneously depending on the growth condition (see, e.g., refs. 14-17). Efforts to model mixed-substrate growth have assumed specific kinetic expressions for substrate uptake and biomass growth rates, and their predictions are formulated in terms of known model parameters (15,18). Similarly, FBA predictions are based on previous knowledge of the...

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High yields (1939 U/ml) of an alkaline protease were obtained in batch fermentation of a Bacillus sp. using a response surface methodology. The interaction of four variables, viz., starch, peptone, incubation time, and inoculum density, suggested inoculum density to be an insignificant variable. However, incubation time had a profound effect on protease yields at all the concentrations of carbon and nitrogen used. The response surface raised and flattened with increase in time of incubation, and maximum protease production up to 1939 U/ml was obtained after 96 h of incubation. The model equation obtained was validated experimentally at maximum starch (15 mg/ml) and peptone (7.5 mg/ml) concentration with increased incubation time up to 144 h in the presence of minimum inoculum density (1%). An overall 2.6-fold increase in protease production was obtained as compared with mean observed response (750 U/ml) at zero level of all variables.