Multi-objective optimization in biotechnological processes: application to plant cell suspension cultures of Thevetia peruviana
DOI:
https://doi.org/10.17533/udea.redin.n87a05Keywords:
model-based optimization, modelling and optimization, multi-objective problem, mechanistic models, biotechnological processesAbstract
Bioprocesses productivity is a compromise between two conflicting objectives, maximization of biomass growth rate and minimization of substrate consumption. In this work, a model based multi-objective optimization problem is solved for improving the process productivity in plant cell suspension cultures of Thevetia peruviana. A solution of the multi-objective problem allowed determining the optimal initial concentrations of substrate and biomass for assuring maximal productivity. Model-based optimization is carried out using a mechanistic model, which includes a representation of the intracellular processes taking place on the plant cells. The best solutions were chosen from the Pareto front in agreement with expert criterion. Results indicate that an initial inoculum concentration of 3.91g/L and an initial sucrose concentration of 23.63g/L, are recommended as initial conditions for obtaining a biomass productivity of 1.57g/L*day with an acceptable sucrose uptake. Experimental validation of the optimal found was carried out and the productivity obtained was 1.52g/L using an initial inoculum concentration of 4.27g/L and an initial sucrose concentration of 25.44g/L. Results suggest that the proposed methodology can be extended to increase the productivity in terms of metabolite production from this plant cell cultures and other plant species.
Downloads
References
O. H. Sendín, J. Vera, N. V. Torres, and J. R. Banga, “Model based optimization of biochemical systems using multiple objectives: a comparison of several solution strategies,” Mathematical and Computer Modelling of Dynamical Systems, vol. 12, no. 5, pp. 469–487, Feb. 2007.
J. O. Sendín, O. Exler, and J. R. Banga, “Multi-objective mixed integer strategy for the optimisation of biological networks,” IET Syst. Biol., vol. 4, no. 3, pp. 236–248, May. 2010.
A. F. Villaverde, S. Bongard, K. Mauch, E. Balsa, and J. R. Banga, “Metabolic engineering with multi-objective optimization of kinetic models,” J Biotechnol., vol. 222, pp. 1–8, Mar. 2016.
J. Vera, P. de Atauri, M. Cascante, and N. V. Torres, “Multicriteria optimization of biochemical systems by linear programming: application to production of ethanol by Saccharomyces cerevisiae,” Biotechnol. Bioeng., vol. 83, no. 3, pp. 335–343, Aug. 2003.
J. Branke, K. Deb, K. Miettinen, and R. Slowinski, Multiobjective Optimization: Interactive and Evolutionary Approaches, 1st ed. Berlín, Germany: Springer Berlin Heidelberg, 2008.
F. Wang and J. Sheu, “Multiobjective parameter estimation problems of fermentation processes using a high ethanol tolerance yeast,” Chemical Engineering Science, vol. 55, no. 18, pp. 3685–3695, Sep. 2000.
R. Brunet, G. Guillén, and L. Jiménez, “Cleaner design of single-product biotechnological facilities through the integration of process simulation, multiobjective optimization, life cycle assessment, and principal component analysis,” Industrial & Engineering Chemistry Research, vol. 51, no. 1, pp. 410–424, Nov. 2012.
A. Villegas, J. P. Arias, D. Aragón, S. Ochoa, and M. Arias, “Structured model and parameter estimation in plant cell cultures of Thevetia peruviana,” Bioprocess and Biosystems Engineering, vol. 40, no. 4, pp. 573–587, Apr. 2017.
A. Villegas, J. P. Arias, D. Aragón, S. Ochoa, and M. Arias, “First principle-based models in plant suspension cell cultures: a review,” Critical Reviews in Biotechnology, vol. 37, no. 8, pp. 1077–1089, Apr. 2017.
C. A. Coello, G. B. Lamont, and D. A. van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd ed. New York, USA: Springer US, 2007.
J. Handl, D. B. Kell, and J. Knoeles, “Multiobjective optimization in bioinformatics and computational biology,” IEEE/ACM Trans. Comput. Biol. Bioinformatics, vol. 4, no. 2, pp. 279–292, Apr. 2017.
J. Branke, K. Deb, H. Dierolf, and M. Osswald, “Finding knees in multi-objective optimization,” in Parallel Problem Solving from Nature - PPSN VIII, 8th International Conference, Birmingham, Proceedings, UK, 2004, pp. 18–22.
L. Rachmawati and D. Srinivasan, “Multiobjective evolutionary algorithm with controllable focus on the knees of the pareto front,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 4, pp. 810–824, Aug. 2009.
J. Almquist, M. Cvijovic, V. Hatzimanikatis, J. Nielsen, and M. Jirstrand, “Kinetic models in industrial biotechnology - improving cell factory performance,” Metabolic Engineering, vol. 24, pp. 38–60, Jul. 2014.
I. C. Chou and E. O. Voit, “Recent developments in parameter estimation and structure identification of biochemical and genomic systems,” Mathematical Biosciences, vol. 219, no. 2, pp. 57–83, Jun. 2009.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2018 Revista Facultad de Ingeniería Universidad de Antioquia
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Revista Facultad de Ingeniería, Universidad de Antioquia is licensed under the Creative Commons Attribution BY-NC-SA 4.0 license. https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
The material published in the journal can be distributed, copied and exhibited by third parties if the respective credits are given to the journal. No commercial benefit can be obtained and derivative works must be under the same license terms as the original work.
Twitter