Nonlinear Optimization of Enzyme Kinetic Parameters

Abstract

In the analysis of enzyme kinetics data, Km and Vmax play a very important role. Linearization of kinetic equation is still a common practice for determining these parameters. Although graphical methods help in understanding the kinetic behavior of enzymes, they have certain shortcomings associated with them due to which they sometimes lead to an anomalous estimation of the kinetic parameters. In order to yield a more accurate estimate of parameters, minimization of least square error can be quite useful. However, since the least square error determination is a non linear function, the usual methods may not be fruitful. This research recommends the use of two simple and fast evolutionary optimization techniques such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) which may be applied for the determination of Michaelis Menten (MM) enzyme analysis. We have shown the working of these methods on a set of six enzymes taken from literature along with a unique enzyme, geraniol acetyltransferase (GAAT), purified from the aromatic grass palmarosa. The entire study shows that GA and PSO can be used efficiently for determining accurate values for Km and Vmax.