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Metabolic engineering with multi-objective optimization of kinetic models.

Alejandro F Villaverde1, Sophia Bongard2, Klaus Mauch2

  • 1Bioprocess 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.

Journal of Biotechnology
|January 31, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic modeling method to boost biotechnological production. It optimizes genetic modifications in cells like Chinese Hamster Ovary (CHO) to enhance product yield and cell growth.

Keywords:
Dynamic modellingLarge-scaleMetabolic engineeringMulti-objective optimizationTarget identificationUp/down-regulation

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Area of Science:

  • Biotechnology
  • Metabolic Engineering
  • Systems Biology

Background:

  • Kinetic models are valuable tools for metabolic engineering, enabling the prediction of genetic and regulatory modifications to enhance metabolite production.
  • Monitoring host organism functions alongside production is crucial for successful metabolic engineering.

Purpose of the Study:

  • To present a methodology for increasing productivity in biotechnological processes using dynamic models.
  • To identify optimal genetic and regulatory modifications for enhancing metabolite production while maintaining cellular functions.

Main Methods:

  • Utilizing multi-objective dynamic optimization to pinpoint enzymatic targets and regulation levels.
  • Applying the methodology to a large-scale metabolic model of Chinese Hamster Ovary (CHO) cells for antibody production in a fed-batch process.

Main Results:

  • The proposed methodology successfully increased productivity, biomass production, and product titer in CHO cells.
  • Sustained and robust cell growth was achieved, with lactate and ammonia concentrations maintained at low levels.

Conclusions:

  • The developed approach effectively optimizes metabolic models by determining optimal targets and regulation levels.
  • This flexible methodology can accommodate various trade-offs and constraints for metabolic engineering applications.