Multi-objective optimization in biotechnological processes: application to plant cell suspension cultures of Thevetia peruviana

Keywords: Model-based optimization, Modelling and optimization, Multi-objective problem, Mechanistic models, Biotechnological processes

Abstract

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.

|Abstract
= 467 veces | PDF
= 460 veces|

Downloads

Download data is not yet available.

Author Biographies

Adriana Patricia Villegas-Quiceño, Universidad Cooperativa de Colombia
Grupo Termomec, Facultad de Ingeniería
Juan Pablo Arias-Echeverri, Universidad Nacional de Colombia
Grupo de Biotecnología Industrial, Facultad de Ciencias
Daira Aragón-Mena, Louisiana State University Agricultural Center
Audubon Sugar Institute
Silvia Mercedes Ochoa-Cáceres, Universidad de Antioquia
Grupo de Investigación en Simulación, Diseño, Control y Optimización de Procesos (SIDCOP), Facultad de Ingeniería
Mario Evelio Arias-Zabala, Universidad Nacional de Colombia
Grupo de Biotecnología Industrial, Facultad de Ciencias

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.

Published
2018-06-19