Model-based optimization of biopharmaceutical manufacturing in Pichia pastoris based on dynamic flux balance analysis

Developing a metabolic model-based fed-batch feeding strategy for Pichia pastoris fermentation through fine-tuning of the methanol utilization pathway

Pichia pastoris is a commonly used microbial host for recombinant protein production. It is mostly cultivated in fed-batch mode, in which the environment of the cell is continuously changing. Hence, it is vital to understand the influence of feeding strategy parameters on the intracellular reaction network to fine-tune bioreactor performance. This study used dynamic flux balance analysis (DFBA) integrated with transcriptomics data to simulate the recombinant P. pastoris (Mut^s ) growth during the induction phase for three fed-batch strategies, conducted at constant specific growth rates (μ-stat). The induction phase was split into equal time intervals, and the correlated reactions with protein yield were identified in the three fed-batch strategies using the Pearson correlation coefficient. Subsequently, principal component analysis (PCA) was applied to cluster induction phase time intervals and identify the role of correlated reactions on metabolic differentiation of time intervals. It was found that increasing fluxes through the methanol dissimilation pathway increased protein yield. By adding a methanol assimilation pathway inhibitor (HgCl₂ ) to the shake flask medium growing on glycerol: methanol mixture (10%: 90%, v/v), the protein titre increased by 60%. As per DFBA, the higher the methanol to biomass flux ratio (R_meoh/Δx ), the higher the protein yield. Finally, a novel feeding strategy was developed to increase the amount of R_meoh/Δx compared to the three feeding strategies. The concentration of recombinant human growth hormone (rhGH), used as the model protein, increased by 16% compared to the optimal culture result obtained previously (800 mg L^-1 to 928 mg L^-1 ), while production yield improved by 85% (24.8 mg g_DCW ^-1 to 46 mg g_DCW ^-1 ).

Current advances of Pichia pastoris as cell factories for production of recombinant proteins

Pichia pastoris (syn. Komagataella spp.) has attracted extensive attention as an efficient platform for recombinant protein (RP) production. For obtaining a higher protein titer, many researchers have put lots of effort into different areas and made some progress. Here, we summarized the most recent advances of the last 5 years to get a better understanding of its future direction of development. The appearance of innovative genetic tools and methodologies like the CRISPR/Cas9 gene-editing system eases the manipulation of gene expression systems and greatly improves the efficiency of exploring gene functions. The integration of novel pathways in microorganisms has raised more ideas of metabolic engineering for enhancing RP production. In addition, some new opportunities for the manufacture of proteins have been created by the application of novel mathematical models coupled with high-throughput screening to have a better overview of bottlenecks in the biosynthetic process.

Advances in Komagataella phaffii Engineering for the Production of Renewable Chemicals and Proteins

The need for a more sustainable society has prompted the development of bio-based processes to produce fuels, chemicals, and materials in substitution for fossil-based ones. In this context, microorganisms have been employed to convert renewable carbon sources into various products. The methylotrophic yeast Komagataella phaffii has been extensively used in the production of heterologous proteins. More recently, it has been explored as a host organism to produce various chemicals through new metabolic engineering and synthetic biology tools. This review first summarizes Komagataella taxonomy and diversity and then highlights the recent approaches in cell engineering to produce renewable chemicals and proteins. Finally, strategies to optimize and develop new fermentative processes using K. phaffii as a cell factory are presented and discussed. The yeast K. phaffii shows an outstanding performance for renewable chemicals and protein production due to its ability to metabolize different carbon sources and the availability of engineering tools. Indeed, it has been employed in producing alcohols, carboxylic acids, proteins, and other compounds using different carbon sources, including glycerol, glucose, xylose, methanol, and even CO2.

Recombinant protein production in Pichia pastoris: From transcriptionally redesigned strains to bioprocess optimization and metabolic modelling

Pichia pastoris is one of the most widely used host for the production of recombinant proteins. Expression systems that rely mostly on promoters from genes encoding alcohol oxidase 1 or glyceraldehyde-3-phosphate dehydrogenase have been developed together with related bioreactor operation strategies based on carbon sources such as methanol, glycerol, or glucose. Although, these processes are relatively efficient and easy to use, there have been notable improvements over the last twenty years to better control gene expression from these promoters and their engineered variants. Methanol-free and more efficient protein production platforms have been developed by engineering promoters and transcription factors. The production window of P. pastoris has been also extended by using alternative feedstocks including ethanol, lactic acid, mannitol, sorbitol, sucrose, xylose, gluconate, formate, or rhamnose. Herein, the specific aspects that are emerging as key parameters for recombinant protein synthesis are discussed. For this purpose, a holistic approach has been considered to scrutinize protein production processes from strain design to bioprocess optimization, particularly focusing on promoter engineering, transcriptional circuitry redesign. This review also considers the optimization of bioprocess based on alternative carbon sources and derived co-feeding strategies. Optimization strategies for recombinant protein synthesis through metabolic modelling are also discussed.

How to Tackle Underdeterminacy in Metabolic Flux Analysis? A Tutorial and Critical Review

Metabolic flux analysis is often (not to say almost always) faced with system underdeterminacy. Indeed, the linear algebraic system formed by the steady-state mass balance equations around the intracellular metabolites and the equality constraints related to the measurements of extracellular fluxes do not define a unique solution for the distribution of intracellular fluxes, but instead a set of solutions belonging to a convex polytope. Various methods have been proposed to tackle this underdeterminacy, including flux pathway analysis, flux balance analysis, flux variability analysis and sampling. These approaches are reviewed in this article and a toy example supports the discussion with illustrative numerical results.

Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters

Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters. This article is protected by copyright. All rights reserved.

Towards a Novel Computer-Aided Optimization of Microreactors: Techno-Economic Evaluation of an Immobilized Enzyme System

Immobilized multi-enzyme cascades are increasingly used in microfluidic devices. In particular, their application in continuous flow reactors shows great potential, utilizing the benefits of reusability and control of the reaction conditions. However, capitalizing on this potential is challenging and requires detailed knowledge of the investigated system. Here, we show the application of computational methods for optimization with multi-level reactor design (MLRD) methodology based on the underlying physical and chemical processes. We optimize a stereoselective reduction of a diketone catalyzed by ketoreductase (Gre2) and Nicotinamidadenindinukleotidphosphat (NADPH) cofactor regeneration with glucose dehydrogenase (GDH). Both enzymes are separately immobilized on magnetic beads forming a packed bed within the microreactor. We derive optimal reactor feed concentrations and enzyme ratios for enhanced performance and a basic economic model in order to maximize the techno-economic performance (TEP) for the first reduction of 5-nitrononane-2,8-dione.

Rigorous Model-Based Design and Experimental Verification of Enzyme-Catalyzed Carboligation under Enzyme Inactivation

Enzyme catalyzed reactions are complex reactions due to the interplay of the enzyme, the reactants, and the operating conditions. To handle this complexity systematically and make use of a design space without technical restrictions, we apply the model based approach of elementary process functions (EPF) for selecting the best process design for enzyme catalysis problems. As a representative case study, we consider the carboligation of propanal and benzaldehyde catalyzed by benzaldehyde lyase from Pseudomonas fluorescens (PfBAL) to produce (R)-2-hydroxy-1-phenylbutan-1-one, because of the substrate dependent reaction rates and the challenging substrate dependent PfBAL inactivation. The apparatus independent EPF concept optimizes the material fluxes influencing the enzyme catalyzed reaction for the given process intensification scenarios. The final product concentration is improved by 13% with the optimized feeding rates, and the optimization results are verified experimentally. In general, the rigorous model driven approach could lead to selecting the best existing reactor, designing novel reactors for enzyme catalysis, and combining protein engineering and process systems engineering concepts.

Systems biology approach in the formulation of chemically defined media for recombinant protein overproduction

The cell culture medium is an intricate mixture of components which has a tremendous effect on cell growth and recombinant protein production. Regular cell culture medium includes various components, and the decision about which component should be included in the formulation and its optimum amount is an underlying issue in biotechnology industries. Applying conventional techniques to design an optimal medium for the production of a recombinant protein requires meticulous and immense research. Moreover, since the medium formulation for the production of one protein could not be the best choice for another protein, hence, the most suitable media should be determined for each recombinant cell line. Accordingly, medium formulation becomes a laborious, time-consuming, and costly process in biomanufacturing of recombinant protein, and finding alternative strategies for medium development seems to be crucial. In silico modeling is an attractive concept to be adapted for medium formulation due to its high potential to supersede laboratory examinations. By emerging the high-throughput datasets, scientists can disclose the knowledge about the effect of medium components on cell growth and metabolism, and via applying this information through systems biology approach, medium formulation optimization could be accomplished in silico with no need of significant amount of experimentation. This review demonstrates some of the applications of systems biology as a powerful tool for medium development and illustrates the effect of medium optimization with system-level analysis on the production of recombinant proteins in different host cells.