Metabolic engineering with multi-objective optimization of kinetic models

• Published in: Journal of biotechnology Vol. 222; no. C; pp. 1 - 8
• Main Authors: Villaverde, Alejandro F., Bongard, Sophia, Mauch, Klaus, Balsa-Canto, Eva, Banga, Julio R.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 20.03.2016; Elsevier
• Subjects: ammonia; Animals; Antibodies - metabolism; antibody formation; Biomass; biomass production; Biotechnology - methods; Chinese hamsters; CHO Cells; Cricetinae; Cricetulus; Dynamic modelling; dynamic models; Genetics; Government regulations; Kinetics; lactic acid; Large-scale; Metabolic engineering; Metabolic Engineering - methods; Metabolic Networks and Pathways; Metabolites; Methodology; Models, Biological; monitoring; Multi-objective optimization; Optimization; Performance measurement; Productivity; Research Design; Science & Technology; Target identification; Up/down-regulation; Metabolic engineering; Up/down-regulation; Multi-objective optimization; Dynamic modelling; Large-scale; Target identification
• ISSN: 0168-1656; 1873-4863; 1873-4863; 0168-1656
• DOI: 10.1016/j.jbiotec.2016.01.005

•Method for increasing productivity in biotechnological processes using dynamic models.•Finds best combination of targets and optimal level of up/down-regulation.•Multi-objective optimization takes into account several requirements.•Balanced improvement: best trade-off between productivity vs number of interventions.•Tested on a large-scale metabolic model of CHO cells used for antibody production. 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.