Towards optimal substrate feeding for heterologous protein production in Pichia pastoris (Komagataella spp) fed-batch processes under P<inf>AOX1</inf> control: a modeling aided approach

Xavier Ponte, José Manuel Barrigón, Michael Maurer, Diethard Mattanovich, Francisco Valero, José Luis Montesinos-Seguí

Research output: Contribution to journalArticleResearch

7 Citations (Scopus)

Abstract

© 2018 Society of Chemical Industry BACKGROUND: To improve the efficiency of a bioprocess, key parameters, such as yield, titer and productivity, must be considered. They are mainly dependent on the specific rates for product (qP) and cell growth (μ), and their correlation determines the most suitable feeding polices that should be applied. RESULTS: The mathematical description of the Pichia pastoris (Komagataella spp) PAOX1-based system (Mut+) expressing recombinant Rhizopus oryzae lipase (ROL), which includes cell growth, substrate consumption and product formation kinetics, was used to define the optimal profiles regarding methanol feeding-rate (F), residual methanol concentration (S) and specific growth rate (μ). Optimal trajectories obtained using numerical optimization and algorithms based on S-profile determination were first applied to manage the multiplicity of S-states with both μ and qP. Once the S-profile was determined, the F and μ profiles were obtained in a straightforward manner from mass balances and growth kinetics. From experimental results, the bioprocess efficiency improvement was confirmed in terms of a 2.2-fold higher final titer and 3.4-fold higher productivity compared with the best standard pre-programmed exponential substrate feeding strategy. Additionally, 1.2-fold higher productivity and 1.1-fold higher final titer relative to the most promising feedback control of the methanol concentration strategy were obtained. CONCLUSION: The whole model-based approach was established as a versatile and simple platform, which would then be transferable to alternative modes of operation and even to produce other proteins of interest. © 2018 Society of Chemical Industry.
Original languageEnglish
Pages (from-to)3208-3218
JournalJournal of Chemical Technology and Biotechnology
Volume93
DOIs
Publication statusPublished - 1 Nov 2018

Keywords

  • biochemical engineering
  • industrial biotechnology
  • mathematical modeling
  • process optimization
  • proteins
  • yeast

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