State and specific growth estimation in heterologous protein production by Pichia pastoris

Abstract

Estimation of biomass, substrate, and specific growth rate (μ) by two nonlinear observers (nonlinear observer‐based estimator—NLOBE, asymptotic observer with second‐order dynamics tuning—AO‐SODE) and a linear estimator (recursive least squares with variable forgetting factor—RLS‐VFF) is presented. Heterologous protein production in Pichia pastoris PAOX1 (Mut+) and PFLD1‐based systems is closely related to μ and has been addressed due to its high relevance in modern biotechnology and bioprocess engineering. μ was estimated by online gas analyses or substrate measurements, biomass, and substrate considering yield coefficients and mass balances. In simulation studies, NLOBE showed high sensitivity to tuning and initialization variables. Validation experiments demonstrated AO‐SODE performs better than the RLS‐VFF for moderate to rapid changes of μ and model parameters being known. If low changes on μ are presented, for instance, in substrate regulation, RLS‐VFF comes up as the best option, because of its reduced requirements. © 2011 American Institute of Chemical Engineers AIChE J, 58: 2966–2979, 2012

Physiological state as transferable operating criterion to improve recombinant protein production in Pichia pastoris through oxygen limitation

“…Each available technique has its own advantages and disadvantages . Alternatively, real‐time determination of biomass can be conducted by means of different estimation algorithms and techniques, but always incorporating some complexity and even instability in the system. In contrast, control of RQ is a priori not so complex because its on‐line determination is commonly carried out from off‐gas analysis and mass balancing of CO 2 and O 2 .…”

A macrokinetic model‐based comparative meta‐analysis of recombinant protein production by Pichia pastoris under AOX1 promoter

“…The most widely used kinetic model for m in Pichia is the wellknown Monod equation, in culture broths typically falling in the substrate range from 0 to 4-5 g Á L À1 (Barrigón et al, 2012;Curvers et al, 2002). However, the system is also accurately described by a non-monotonically increasing pattern (Table IV), usually selected for the high-methanol operating region, which is cytotoxic and inhibitory of microbial growth (Potvin et al, 2012).…”

“…Combinations of metabolic and mechanistic models can lead to a more comprehensive knowledge of cellular organization (Carneiro et al, 2013). However, unstructured models provide quite acceptable results in many situations such as when the balanced growth condition is fulfilled (Albaek et al, 2011;Chae et al, 2000;Heijnen et al, 1979) or with estimation, control and optimization applications of little mathematical complexity for biotechnological processes (Barrigón et al, 2012;Maurer et al, 2006;Ren and Yuan, 2005;Zhang et al, 2005). Thus, using a bioreactor and a macrokinetic model accounting for macro reactions in combination can be an effective compromise between simplicity and the comprehensive process description typically provided by highly complex alternatives such as cybernetic (Ramakrishna et al, 1996), genetically structured (Lee and Bailey, 1984) and metabolic models (Heyland et al, 2011;Jordà et al, 2012;Soons et al, 2011).…”

Production and characterization of a human lysosomal recombinant iduronate‐2‐sulfatase produced inPichia pastoris

“…In this sense, it has been reported that high levels of recombinant protein can be obtained at limited oxygen conditions . On the other hand, production at substrate (i.e., methanol) limited conditions allows to maintain a specific growth rate, the reduction of metabolic problems associated with the accumulation of methanol or its by‐products, and the improvement in the reproducibility of the process .…”