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Biomass: Annotated Bibliography →

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In order to make several crucial biochemical reactions and counter reactions occur at viable rates, catalysis is quite helpful under physiological conditions (Bowden, 2003). For catalysis to occur, enzyme reactions are essential. In biomedical science, Km, the Michaelis constant is a dynamic invariable articulating the connection that exists between the real constant states of concentration instead of the equilibrium concentration. This measure depends on the specific substrate utilized, the ionic strength and amount of temperature as well as the pH (Ballicora et al., 2000). Supposing stability in the pH, the temperature, and the redox condition, the Km for a given enzyme is generally constant. This parameter gives an indication of the binding strength of the enzyme in discussion to its substrate. Michaelis–Menten kinetics makes the assumption that the kcat is extremely low in comparison to k1 and k–1 (Stryer, 2000). This therefore leads to the deduction that a high Km indicates a greater redissociation rate (k–1) than the association rate and that the enzyme at hand binds the substrate weakly. On the other hand, a low Km indicates a relatively higher affinity for the enzyme’s substrate (Bowden, 2003). In order to determine the Km of any given enzyme, several graphical methods may be used. However, these methods have their own strengths and weaknesses in the processes of estimation and calculation. This paper will explore such strengths and weaknesses that occur while using the graphical methods in the process of determining the Km of an enzyme in a biochemical reaction (Ballicora et al., 2000).

In biochemistry and molecular biology, many graphical methods have been created with an aim of improving the accuracy with which kinetic constants are determined and the most common estimation methods are; thedirect linear plotting (Eisenthal Cornish-Bowden), the double reciprocal plotting (Lineweaver-Burke), Langmuir plot, Eadie–Hofstee plots and also Hanes plots (As shown in the graph below). The methods are not that accurate though. Errors when determining variable v at low substrate concentration levels are more likely to occur in Lineweaver–Burke and Eadie–Hofstee plots and to a reduced degree in Hanes plots. The double reciprocal plot or the Lineweaver-Burke plot suffers a grave disadvantage that is commonly perceived harmless (Bowden, 2003). When taking the reciprocals, larger consideration is put on the rate(s) attained at concentrations of low substrates and these may result in a serious experimental error (Stryer, 2000). However, by use of appropriate weightage, the challenges with the double-reciprocal plot method can be avoided. In addition to its advantages, the method uses a straight line implying that no guess work is used in the processes.

In the Eisenthal method,  the curve is a hyperbola. Therefore, it becomes quite difficult to calculate the enzyme’s Km and the Vmax. Quite a large number of people make the assumption that Vmaxcan be determined from a v versus S graph by getting the point where v attains a limiting value. The main challenge here lies in the proper presentation of a curve that flattens out rapidly and then drawing an asymptote close to that curve. However, the direct linear plot is by far the most suitable method to use when determining the enzyme kinetic parameters. This is due to the fact that once the initial rates are measured and the corresponding data plotted on graph, then the preliminary values of Vmax and Km can be attained. With this method therefore, it is possible to estimate the substrate concentration that yields the most viable spread of initial velocity values.

In spite of the numerous disadvantages in the determination of an enzyme’s Km or its Vmax, and contrary to nonlinear plots, alterations in enzyme kinetics for instance as a result of activity of an enzyme inhibitor, are enthusiastically apparent on the linear plots. without a doubt, selection of the method to employ, whether a linear or nonlinear plot, should be founded on a thorough consideration of the sources of all possible errors in the experimentation and in line with the aim or purpose of that specific experiment (Bowden, 2003). Due to the simplicity of the graphical methods, they are the most widely used method of kinetic parameter estimation. In addition, they are quite useful where computerized estimations are impossible to use or where computers are not accessible making the method the most reliable in Km calculation and estimation (Estevez et al., 2002).

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