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Journal of Theoretical Chemistry
Volume 2013 (2013), Article ID 931091, 7 pages
http://dx.doi.org/10.1155/2013/931091
Research Article

Mathematical Modeling and Analysis of Nonlinear Enzyme Catalyzed Reaction Processes

Department of Mathematics, The Madura College, Madurai 625 011, Tamil Nadu, India

Received 20 March 2013; Accepted 28 October 2013

Academic Editors: A. M. Lamsabhi, A. Stavrakoudis, and B. M. Wong

Copyright © 2013 D. Mary Celin Sharmila et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A mathematical model for the nonlinear enzymatic reaction process is discussed. An approximate analytical expression of concentrations of substrate, enzyme, and free enzyme-product is obtained using homotopy perturbation method (HPM). The main objective is to propose an analytical solution, which does not require small parameters and avoid linearization and physically unrealistic assumptions. Theoretical results obtained can be used to analyze the effect of different parameters. Satisfactory agreement is obtained in the comparison of approximate analytical solution and numerical simulation.

1. Introduction

The importance of biocatalytic processes and reactions for organic synthesis and the pharmaceutical food and cosmetics industry has been constantly growing during the last few years [1, 2]. From a synthetic point of view, enzymes are highly efficient catalysts for an extremely broad palette of reactions [3]. Enzymes of one type, but from different origins, are specialized for substrates, positions in substrates, and products [4]. Enzyme reactions do not follow the law of mass action directly. The rate of the reaction only increases to a certain extent as the concentration of substrate increases. The maximum reaction rate is reached at high substrate concentration due to enzyme saturation. This is in contrast to the law of mass action that states that the reaction rate increases as the concentration of substrate increases [5]. Various simplified analytical models have been developed over the last 20 years. In brief, the analysis involves the construction and solution of reaction/diffusion differential equations, resulting in the development of approximate analytical expressions for [6, 7] nonlinear enzyme catalyzed reaction processes.

The simplest model that explains the kinetic behaviour of enzyme reactions is the classic 1913 model of Michaelis and Menten [8] which is widely used in biochemistry for many types of enzymes. The Michaelis-Menten model is based on the assumption that the enzyme binds the substrate to form an intermediate complex which then dissociates to form the final product and release the enzyme in its original form. The schematic representation of this two-step process is given by where, , and are constant parameters associated with the rates of the reaction. Note that it is generally assumed that the second step of the reaction equation (1) is irreversible. In reality, this is not always the case. Typically, reaction rates are measured under the condition that the product is continually removed, which prevents the reverse reaction of the second step from occurring effectively.

In this paper we have derived an expression for concentration of substrate, enzyme-substrate, and free enzyme-product with nonmechanism based enzyme inactivation, in terms of dimensionless reaction diffusion parameters,,, andusing homotopy perturbation method (HPM). Comparative study of the same with numerical simulation is presented.

2. Mathematical Formulation of the Problems

If a small amount of enzyme is used and all but one substrate is kept constant, then the rate of the enzymatically catalyzed reaction depends on the substrate concentration and initial rate as in the equation, given by [9], where is the Michaelis constant: . The typical notation of the enzyme catalyzed reaction with one substrate can be given as [10] where is substrate, is enzyme, is enzyme-substrate complex, and is free enzyme product. The kinetic equations consist ofwith a conservation relation given in [6]: It is obvious that the derivative of a substrate with respect to time gives the rate. Thus, the rate is a function of compounds(intracellular and extracellular), enzyme concentrations and kinetic parameter. However, the enzyme concentration is hidden in the kinetic constants in the parameter vector; herewith, we can writeas a function of,, and; that is,[11].

The more general form of (3a)–(3c) can be written in the form ofThe form of rate equations is as follows: The initial condition atare as follows: The concentration of the reactants in (5a)–(5d) is denoted by lower case letters The law of mass action leads to the system of following non-linear kinetic equations [12]:with the boundary conditions being Adding (9b) and (9c), we get Using the initial conditions (10) we obtain With this, the system of ordinary differential equations reduces to the following three differential equations:With initial conditions, , and. By introducing the following set of nondimensional variables and parameters, the system of (13a)–(13c) and the initial conditions (10) can be represented in dimensionless form as follows:with

3. Implementation of the HPM

We indicate how (33)–(35) in this paper are derived. To find the solution of (14)–(16), we first construct a homotopy as follows: And the initial approximations are as follows: Approximate solutions of (33)–(35) are Substituting (23)–(25) into (19)–(21), respectively, and comparing the coefficients of like powers of, we can obtain the following differential equations for the concentration of substrate: For enzyme substrate concentration, For product concentration Solving (26)–(28), and using the boundary conditions (22), we can find the following results: According to the HPM, we can conclude that Substitute (29) in (30)-(31) we obtain (33)–(35) in the text.

4. Analytical Solution of Substrate, Enzyme, Enzyme-Substrate Complex, and Free Enzyme Product Using HPM

Non-linear phenomena play a crucial role in applied mathematics and chemistry. Construction of particular exact solutions for these equations remains an important problem. Finding exact solutions that have a physical, chemical, or biological interpretation is of fundamental importance. The investigation of exact solution of non-linear equation is interesting and important. In the past, many authors mainly had paid attention to study solution of non-linear equations by using various methods, variational iteration method [13], and homotopy perturbation method [1417].

The homotopy perturbation method has been extensively worked out over a number of years by numerous authors. The idea has been used to solve nonlinear boundary value problems [15], integral equations [1820], Klein-Gordon and Sine-Gordon equations [21], Emden-Flower type equations [22], and many other problems. This wide variety of applications shows the power of the HPM to solve functional equations. The HPM is unique in its applicability, accuracy and efficiency. The HPM [23] uses the imbedding parameter as a small parameter, and only a few iterations are needed to search for an asymptotic solution. Using this method, we can obtain the following solution to (14)–(16) (see the appendix):

Equations (33)–(35) represent the analytical expression of the dimensionless substrate concentration, dimensionless enzyme-substrate concentration, and dimensionless free enzyme product concentrationfor all values of parameters,, and . For steady condition, the differential equations (17a)–(17c) become as follows: Solving the above equations, we can obtain the concentrations of substrate , enzyme substrate complex, and product free enzymeas follows:,,. Whentends to infinity, the analytical expression corresponding to the substrate concentration, enzyme substrate concentrationand free enzyme product concentrationfrom (33)–(35) confirm the validity of mathematical analysis.

5. Results and Discussion

The substrate concentration versus time is plotted in Figures 1 and 2 using (33). From Figures 1(a) and 1(b), it is observed that the dimensionless substrate concentrationdecreases. When parameters, the dimensionless substrate concentrationgradually decreases as the value of parameterincreases and reaches the steady state when. When, the concentration decreases rapidly asincreases.when. In Figure 2, it is inferred that the concentration decreases as dimensionless timeincreases and reaches its minimum when. The graph is shown for various values ofwhen, .

fig1
Figure 1: Profile of the normalized concentrations of substrateis calculated using (33) for various values of dimensionless parameters, and . Solid line: (33); dotted line: numerical simulation.
931091.fig.002
Figure 2: Profile of the normalized concentrations of substrate is calculated using (33) for various values of dimensionless parameter. Solid line: (33); dotted line: numerical simulation.

Figures 3 and 4 are plotted using (34), dimensionless timeas abscissa, and enzyme concentrationas ordinate. From Figures 3(a) and 3(b), it is observed that the enzyme concentration increases whenand reaches the steady state value when. The graph is shown for various values of the parameter, when and . From Figure 4, it is inferred that enzyme concentrationreaches its maximum between the time 0.03–0.09, and reaches the steady state when.

fig3
Figure 3: Profile of dimensionless enzyme concentration is calculated using (34) for various values of dimensionless parameters,,and. Solid line: (34); dotted line: numerical simulation.
931091.fig.004
Figure 4: Profile of dimensionless enzyme concentration is calculated using (34) for various values of dimensionless parameter. Solid line: (34); dotted line: numerical simulation.

Figures 5 and 6 shows the free enzyme product concentration versus time for various values of parameter using (35). From Figures 5(a)-5(b), it is inferred that product concentrationincreases very slowly as the time increases. The graphs are shown for various values of the parameter, when and . In Figure 6, it is noted that the product concentration increases slowly and reaches the steady state at. Profile of dimensionless concentrations , , and versus the dimensionless timeusing (33), (34), and (35) for the fixed values of the parameters is plotted in Figure 7. From this figure, it is inferred that the concentration of substrate decreases, whereas the concentration of enzyme increases. But for all time the concentration of free enzyme-product have at most constant value.

fig5
Figure 5: Profile of dimensionless free enzyme product concentrationis calculated using (35) for various values of dimensionless parameters,,and. Solid line: (35); dotted line: numerical simulation.
931091.fig.006
Figure 6: Profile of dimensionless free enzyme product concentration is calculated using (35) for various values of dimensionless parameter. Solid line: (35); dotted line: numerical simulation.
931091.fig.007
Figure 7: Profile of dimensionless concentrations , , and versus the dimensionless timeusing (33), (34), and (35) for the fixed values of the parameters,, and . Solid line: (33), (34), and (35); dotted line: numerical simulation.

6. Conclusion

Approximate analytical solutions to the system of non-linear reaction equations in enzyme reaction mechanism are presented using homotopy perturbation method. A simple, straight forward, and a new method of estimating the concentrations of substrate, enzyme-substrate, and product are derived. This solution procedure can be easily extended to all kinds of system of coupled non-linear equations with various complex boundary conditions in enzyme-substrate nonlinear reaction diffusion processes.

Appendix

Numerical Simulation Program for (14)–(16)

function graphmain3options = odeset (“RelTol”, , “Stats”, “on”);% initial conditions;;tic;tocfigurehold onplot, “−” )plot,“−”)plot,“”)legend (“”,“”,“”) label (“”) label (“”)returnfunction;;;;; + ;;,return

Nomenclature and Units

:Enzyme concentration (μM)
:Enzyme-substrate complex (μM)
:Substrate concentration (μM)
:Initial enzyme concentration (μM)
:Michaelis-Menten constant
:Initial substrate concentration (μM)
:Positive rate constants (none)
:Reaction diffusion parameter (none)
:Dimensionless substrate concentration (none)
:Dimensionless enzyme substrate concentration (none)
:Dimensionless product concentration (none)
:Time (sec)
:Dimensionless time (none).

Acknowledgment

This work was supported by CSIR Project (no. 01 (2442)/10/EMR-II). The authors are thankful to the Principal and the Secretary, the Madura College, Madurai, for their encouragement.

References

  1. A. Beloqui, P. D. de María, P. N. Golyshin, and M. Ferrer, “Recent trends in industrial microbiology,” Current Opinion in Microbiology, vol. 11, no. 3, pp. 240–248, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. B. M. Nestl, B. A. Nebel, and B. Hauer, “Recent progress in industrial biocatalysis,” Current Opinion in Chemical Biology, vol. 15, no. 2, pp. 187–193, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. C. M. Clouthier and J. N. Pelletier, “Expanding the organic toolbox: a guide to integrating biocatalysis in synthesis,” Chemical Society Reviews, vol. 41, no. 4, pp. 1585–1605, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. U. T. Bornscheuer, G. W. Huisman, R. J. Kazlauskas, S. Lutz, J. C. Moore, and K. Robins, “Engineering the third wave of biocatalysis,” Nature, vol. 485, pp. 185–194, 2012.
  5. J. Keener and J. Sneyd, Mathematical Physiology, Springer, New York, NY, USA, 1998.
  6. M. E. G. Lyons, J. C. Greer, C. A. Fitzgerald, T. Bannon, and P. N. Barlett, “Reaction/diffusion with Michaelis-Menten kinetics in electroactive polymer films. Part 1. The steady-state amperometric response,” Analyst, vol. 121, no. 6, pp. 715–731, 1996. View at Publisher · View at Google Scholar · View at Scopus
  7. M. E. G. Lyons, T. Bannon, G. Hinds, and S. Rebouillat, “The home of premier fundamental discoveries, inventions and applications in the analytical and bioanalytical sciences,” Analyst, vol. 123, p. 1947, 1998. View at Publisher · View at Google Scholar
  8. L. Michaelis and M. Menten, “Die Kinetik der Invertinwirkung,” Biochemische Zeitschrift, vol. 49, p. 333, 1913.
  9. A. Fersht, Enzyme Structure Mechanism, W.H. Freeman, New York, NY, USA, 2nd edition, 1985.
  10. K. M. Plowman, Enzyme Kinetics, McGraw-Hill, New York, NY, USA, 1972.
  11. J. Hurlebaus, A Pathway Modeling Tool for Metabolic Engineering, Institute für Biotechnologie Jülich-3912 Bundesrepublik Deutschland, 2001.
  12. J. D. Murray, Mathematical Biology, Springer, Berlin, Germany, 1989.
  13. J.-H. He, “Approximate analytical solution for seepage flow with fractional derivatives in porous media,” Computer Methods in Applied Mechanics and Engineering, vol. 167, no. 1-2, pp. 57–68, 1998. View at Scopus
  14. J. H. He, “Application of homotopy perturbation method to nonlinear wave equations,” Chaos, Solitons & Fractals, vol. 26, no. 3, pp. 695–700, 2005. View at Publisher · View at Google Scholar
  15. J.-H. He, “Homotopy perturbation method for solving boundary value problems,” Physics Letters A, vol. 350, no. 1-2, pp. 87–88, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. P. D. Ariel, “Homotopy perturbation method and the natural convection flow of a third grade fluid through a circular tube,” Nonlinear Science Letters A, vol. 1, pp. 43–52, 2010.
  17. D. D. Ganji and M. Rafei, “Solitary wave solutions for a generalized Hirota-Satsuma coupled KdV equation by homotopy perturbation method,” Physics Letters A, vol. 356, no. 2, pp. 131–137, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. A. Golbabai and B. Keramati, “Modified homotopy perturbation method for solving Fredholm integral equations,” Chaos, Solitons & Fractals, vol. 37, no. 5, pp. 1528–1537, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Ghasemi, M. T. Kajani, and E. Babolian, “Numerical solutions of the nonlinear Volterra-Fredholm integral equations by using homotopy perturbation method,” Applied Mathematics and Computation, vol. 188, no. 1, pp. 446–449, 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Biazar and H. Ghazvini, “He's homotopy perturbation method for solving systems of Volterra integral equations of the second kind,” Chaos, Solitons & Fractals, vol. 39, no. 2, pp. 770–777, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. Z. Odibat and S. Momani, “A reliable treatment of homotopy perturbation method for Klein-Gordon equations,” Physics Letters A, vol. 365, no. 5-6, pp. 351–357, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. M. S. H. Chowdhury and I. Hashim, “Solutions of time-dependent Emden-Fowler type equations by homotopy-perturbation method,” Physics Letters A, vol. 368, no. 3-4, pp. 305–313, 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. J.-H. He, “Some asymptotic methods for strongly nonlinear equations,” International Journal of Modern Physics B, vol. 20, no. 10, pp. 1141–1199, 2006. View at Publisher · View at Google Scholar · View at Scopus