Optimization of Biochemical Systems Production Using Combination of Newton Method and Particle Swarm Optimization

Mohd Arfian Ismail, Vitaliy Mezhuyev, Irfan Darmawan, Shahreen Kasim, Mohd Saberi Mohamad, Ashraf Osman Ibrahim

Abstract


In the presented paper, an improved method that combines the Newton method with Particle Swarm Optimization (PSO) algorithm to optimize the production of biochemical systems was discussed and presented in detail. The optimization of the biochemical system's production became difficult and complicated when it involves a large size of biochemical systems that have many components and interaction between chemical. Also, two objectives and several constraints make the optimization process difficult. To overcome these situations, the proposed method was proposed by treating the biochemical systems as a nonlinear equations system and then optimizes using PSO. The proposed method was proposed to improve the biochemical system's production and at the same time reduce the total of chemical concentration involves. In the proposed method, the Newton method was used to deal with nonlinear equations system, while the PSO algorithm was utilized to fine-tune the variables in nonlinear equations system. The main reason for using the Newton method is its simplicity in solving the nonlinear equations system. The justification of choosing PSO algorithm is its direct implementation and effectiveness in the optimization process. In order to evaluate the proposed method, two biochemical systems were used, which were E.coli pathway and S. cerevisiae pathway. The experimental results showed that the proposed method was able to achieve the best result as compared to other works.


Keywords


newton method; particle swarm optimization; optimization; biochemical systems; computational intelligence.

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References


M. A. Ismail, S. Deris, M. S. Mohamad, and A. Abdullah, “A newton cooperative genetic algorithm method for in silico optimization of metabolic pathway production,” PloS one, vol. 10, no. 5, p. e0126199, 2015.

G. Xu, “Bi-objective optimization of biochemical systems by linear programming,” Applied Mathematics and Computation, vol. 218, no. 14, pp. 7562–7572, 2012.

G. Xu, “Steady-state optimization of biochemical systems through geometric programming,” European Journal of Operational Reseach, vol. 225, no. 1, pp. 12–20, 2013.

H. Link, J. Vera, D. Weuster-Botz, N. T. Darias, and E. Franco-Lara, “Multi-objective steady state optimization of biochemical reaction networks using a constrained genetic algorithm,” Computers and Chemical Engineering, vol. 32, no. 8, pp. 1707–1713, 2008.

M. A. Ismail, S. Deris, M. S. Mohamad, and A. Abdullah, “A hybrid of Newton method and genetic algorithm for constrained optimization method of the production of metabolic pathway,” Life Science Journal, vol. 11, no. 9 SPEC. ISSUE, 2014.

C. Grosan and A. Abraham, “A New Approach for Solving Nonlinear Equations Systems,” Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol. 38, no. 3, pp. 698–714, 2008.

M. H. Al-Towaiq and Y. S. A. Hour, “Two Improved Methods Based on Broyden’s Newton Methods for the Solution of Nonlinear System of Equations,” Journal of Engineering and Applied Sciences, vol. 11, pp. 2344–2348, 2016.

C.-S. Liu, “A modified Newton method for solving non-linear algebraic equations,” Journal of Marine Science and Technology, vol. 17, no. 3, pp. 238–247, Jun. 2009.

H. Ramos and M. T. T. Monteiro, “A new approach based on the Newton’s method to solve systems of nonlinear equations,” Journal of Computational and Applied Mathematics, vol. 318, pp. 3–13, Jul. 2017.

M. A. Ismail et al., “A hybrid of optimization method for multi-objective constraint optimization of biochemical system production,” Journal of Theoretical and Applied Information Technology, vol. 81, no. 3, pp. 502–513, 2015.

F. Rodriguez-Acosta, C. M. Regalado, and N. V Torres, “Non-linear optimization of biotechnological processes by stochastic algorithms: Application to the maximization of the production rate of ethanol, glycerol and carbohydrates by Saccharomyces cerevisiae,” Journal of Biotechnology, vol. 65, no. 1, pp. 15–28, 1999.

M. A. Ismail, V. Mezhuyev, K. Moorthy, and S. Kasim, “Optimisation of Biochemical Systems Production using Hybrid of Newton method , Differential Evolution Algorithm and Cooperative Coevolution Algorithm,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 8, no. 1, pp. 27–35, 2017.

M. A. Ismail, V. Mezhuyev, S. Deris, M. S. Mohamad, S. Kasim, and R. Saedudin, “Multi-objective Optimization of Biochemical System Production Using an Improve Newton Competitive Differential Evolution Method,” International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 4–2, pp. 1535–1542, 2017.

A. Marin-Sanguino and N. V Torres, “Optimization of biochemical systems by linear programming and general mass action model representations,” Mathematical Biosciences, vol. 184, no. 2, pp. 187–200, 2003.

J. Vera, C. Gonzalez-Alcon, A. Marin-Sanguino, and N. Torres, “Optimization of biochemical systems through mathematical programming: Methods and applications,” Computers {&} Operations Research, vol. 37, no. 8, pp. 1427–1438, 2010.

A. Marin-Sanguino, E. O. Voit, C. Gonzalez-Alcon, and N. V Torres, “Optimization of biotechnological systems through geometric programming,” Theoretical Biology and Medical Modelling, vol. 4, pp. 38–54, 2007.

M. Caspers, U. Brockmeier, C. Degering, T. Eggert, and R. Freudl, “Improvement of Sec-dependent secretion of a heterologous model protein in Bacillus subtilis by saturation mutagenesis of the N-domain of the AmyE signal peptide.,” Applied microbiology and biotechnology, vol. 86, no. 6, pp. 1877–85, May 2010.

Y.-S. Jang, J. Lee, A. Malaviya, D. Y. Seung, J. H. Cho, and S. Y. Lee, “Butanol production from renewable biomass: Rediscovery of metabolic pathways and metabolic engineering,” Biotechnology Journal, vol. 7, no. 2, pp. 186–198, 2011.

Y.-N. Zheng et al., “Problems with the microbial production of butanol,” Journal of industrial microbiology {&} biotechnology, vol. 36, no. 9, pp. 1127–1138, 2009.

M. A. Ismail, S. Deris, M. S. Mohamad, and A. Abdullah, “A Hybrid of Newton Method and Genetic Algorithm for Constrained Optimization method of the Production of Metabolic Pathway,” Life Science Journal, vol. 11, no. 9s, pp. 409–414, 2014.

M. Babaei, “A general approach to approximate solutions of nonlinear differential equations using particle swarm optimization,” Applied Soft Computing, vol. 13, no. 7, pp. 3354–3365, 2013.

C.-S. Liu and S. N. Atluri, “A novel time integration method for solving a large system of non-linear algebraic equations,” Computer Modeling in Engineering and Sciences, vol. 31, no. 2, pp. 71–83, Jan. 2008.

J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” in Proceedings of IEEE International Conference on Neural Networks, 1995, vol. 4, pp. 1942–1948.

J. J. Durillo and A. J. Nebro, “jMetal: A Java framework for multi-objective optimization,” Advances in Engineering Software, vol. 42, no. 10, pp. 760–771, 2011.

Z.-L. Xiu, A.-P. Zeng, and W.-D. Deckwer, “Model analysis concerning the effects of growth rate and intracellular tryptophan level on the stability and dynamics of tryptophan biosynthesis in bacteria,” Journal of Biotechnology, vol. 58, no. 2, pp. 125–140, 1997.

J. L. Galazzo and J. E. Bailey, “Fermentation pathway kinetics and metabolic flux control in suspended and immobilized Saccharomyces cerevisiae,” Enzyme and Microbial Technology, vol. 12, no. 3, pp. 162–172, 1990.

D. N. E. Phon, M. B. Ali, and N. D. A. Halim, “Collaborative Augmented Reality in Education: A Review,” in 2014 International Conference on Teaching and Learning in Computing and Engineering, 2014, pp. 78–83.

D. N. E. Phon, M. B. Ali, and N. D. A. Halim, “Learning with augmented reality: Effects toward student with different spatial abilities,” Advanced Science Letters, vol. 21, no. 7, pp. 2200–2204, 2015.

Z. Mustaffa, Y. Yusof, and S. S. Kamaruddin, “Gasoline Price Forecasting: An Application of LSSVM with Improved ABC,” Procedia - Social and Behavioral Sciences, vol. 129, pp. 601–609, May 2014.

Z. Mustaffa, M. H. Sulaiman, and M. N. M. Kahar, “LS-SVM hyper-parameters optimization based on GWO algorithm for time series forecasting,” in 2015 4th International Conference on Software Engineering and Computer Systems (ICSECS), 2015, pp. 183–188.




DOI: http://dx.doi.org/10.18517/ijaseit.8.5.4987

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