title: Metabolic engineering with multi-objective optimization of kinetic models.
abstract: Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.
authors.affiliation: Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, 36208 Vigo, Spain; Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal; Department of Systems and Control Engineering, Universidade de Vigo, Rua Maxwell, 36310 Vigo, Spain.
authors.forename: Alejandro F
authors.initials: AF
authors.lastname: Villaverde
authors.affiliation: Insilico Biotechnology AG, Meitnerstraße 9, 70563 Stuttgart, Germany.
authors.forename: Sophia
authors.initials: S
authors.lastname: Bongard
authors.affiliation: Insilico Biotechnology AG, Meitnerstraße 9, 70563 Stuttgart, Germany.
authors.forename: Klaus
authors.initials: K
authors.lastname: Mauch
authors.affiliation: Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, 36208 Vigo, Spain.
authors.forename: Eva
authors.initials: E
authors.lastname: Balsa-Canto
authors.affiliation: Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, 36208 Vigo, Spain.
authors.forename: Julio R
authors.initials: JR
authors.lastname: Banga
mesh_terms: D000818:Animals; D000906:Antibodies / Q000378:metabolism; D001709:Biotechnology / Q000379:methods*; D016466:CHO Cells; D006224:Cricetinae; D003412:Cricetulus; D007700:Kinetics; D060847:Metabolic Engineering / Q000379:methods*; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D012107:Research Design
chemical_list: D000906:Antibodies
keywords: Dynamic modelling; Large-scale; Metabolic engineering; Multi-objective optimization; Target identification; Up/down-regulation