Dynamic flux balance analysis for pharmaceutical protein production by Pichia pastoris: Human growth hormone

Çalı́k, Pınar; Şahin, Merve; Taşpınar, Hatice; Soyaslan, Elif Ş.; İnankur, Bahar

doi:10.1016/j.enzmictec.2010.09.016

Cited by 12 publications

(5 citation statements)

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“…The substrates and co‐substrate(s) used, bioreactor operation parameters and the feeding strategy applied strongly affect product formation by influencing metabolic pathways and changing metabolic fluxes . Thus, to fine‐tune bioreactor performance in relation with the physiology of the microorganism, in addition to the main carbon source, the feeding strategy of the co‐substrate needs to be clarified.…”

Section: Discussionmentioning

confidence: 99%

“…The substrates and co-substrate(s) used, bioreactor operation parameters and the feeding strategy applied strongly affect product formation by influencing metabolic pathways and changing metabolic fluxes. 13 Thus, to fine-tune bioreactor performance in relation with the physiology of the microorganism, in addition to the main carbon source, the feeding strategy of the co-substrate needs to be clarified. Therefore, in the present work different feeding strategies of sorbitol and mannitol, nonrepressing carbon sources for AOX1 promoter, were investigated by determining the cultivation time profiles of sorbitol, mannitol, cell, rHuEPO, protease enzymes, AOX activity, and organic acids to understand the details of the metabolism and product selectivity.…”

Section: Discussionmentioning

confidence: 99%

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Co‐substrate mannitol feeding strategy design in semi‐batch production of recombinant human erythropoietin production by Pichia pastoris

Eskitoros

Çalı́k

2013

J of Chemical Tech & Biotech

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BACKGROUND The effects of alternative co‐substrate feeding strategies on recombinant human erythropoietin (rHuEPO) production by Pichia pastoris‐Mut+ strain were investigated. RESULTS Five different production strategies were designed and performed in the production phase of the bioprocesses with the use of either mannitol or sorbitol as the co‐substrate. The highest rHuEPO production was achieved as CrHuEPO= 0.65 g L−1 at t=9 h, with the cell concentration Cx=55 g L−1 in the production phase; wherein methanol was fed to the bioreactor with a predetermined dynamic feeding rate calculated for constant μM0=0.03 h−1; and three consecutive pulses of the co‐substrate mannitol were introduced at t=0, 6, and 12 h, so that CMan=50 g L−1. The overall cell and product yields on the substrates methanol and mannitol were found, respectively, as YX/St=0.22 g g−1 and YrHuEPO/St=3.74 mg g−1. CONCLUSIONS The use of mannitol as the co‐substrate enhanced rHuEPO production and furthermore shortened the bioprocess time. The design of semi‐batch feeding strategy, based on the selection of the substrates and the co‐substrates, and their dynamic feeding into the bioreactor, is important to increase the production and productivity in r‐protein production by Mut+ strains of P. pastoris, and the developed strategy would be especially important for therapeutic glycoprotein productions by P. pastoris. © 2013 Society of Chemical Industry

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Co‐substrate mannitol feeding strategy design in semi‐batch production of recombinant human erythropoietin production by Pichia pastoris

Eskitoros

Çalı́k

2013

J of Chemical Tech & Biotech

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“…FBA can provide the necessary knowledge on metabolic pathway fluxes. Using this methodology, researchers can calculate intracellular fluxes by using stoichiometric models for the major intracellular reactions and apply mass balances for intracellular metabolites (Stephanopoulos et al, 1998;Kauffman et al, 2003;Ç alık et al, 2011). Genomic comparative tools have been used for the analysis of a variety of metagenomic samples (Dinsdale et al, 2008), however the FBA offers a new insight for extreme environments ecosystems.…”

Section: Introductionmentioning

confidence: 99%

A comparison between functional frequency and metabolic flows framed by biogeochemical cycles in metagenomes: The case of “El Coquito” hot spring located at Colombia's national Nevados park

Zamora

Pinzón

Zambrano³

et al. 2015

Ecological Modelling

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“…Consequently, interactions that depend on changes in substrate concentration or species abundance can be modeled with this approach. Dynamic-SMNs have been applied to model single species (Mahadevan et al, 2002 ; Hjersted et al, 2005 ; Çalik et al, 2011 ) as well as multiple species (Scheibe et al, 2009 ; Zhuang et al, 2011 ). This hybrid approach has been previously referred to as dynamic FBA (dFBA) (Mahadevan et al, 2002 ).…”

Section: Approaches To Modeling Microbial Interactions Using Smn Modementioning

confidence: 99%

Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems

2016

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We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can be used to analyze complex “omics” data and to infer and optimize metabolic processes. Thereby, SMN models are suitable to capitalize on advances in high-throughput molecular and metabolic data generation. SMN models are starting to be applied to describe microbial interactions during wastewater treatment, in-situ bioremediation, microalgae blooms methanogenic fermentation, and bioplastic production. Despite their current challenges, we envisage that SMN models have future potential for the design and development of novel growth media, biochemical pathways and synthetic microbial associations.

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Dynamic flux balance analysis for pharmaceutical protein production by Pichia pastoris: Human growth hormone

Cited by 12 publications

References 15 publications

Co‐substrate mannitol feeding strategy design in semi‐batch production of recombinant human erythropoietin production by Pichia pastoris

Co‐substrate mannitol feeding strategy design in semi‐batch production of recombinant human erythropoietin production by Pichia pastoris

A comparison between functional frequency and metabolic flows framed by biogeochemical cycles in metagenomes: The case of “El Coquito” hot spring located at Colombia's national Nevados park

Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems

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