Robust control of fed-batch high-cell density cultures: a simulation-based assessment

[1]  Jonas Andersson,et al.  Probing control in B. licheniformis fermentations , 2010 .

[2]  F. García-Ochoa,et al.  Bioreactor scale-up and oxygen transfer rate in microbial processes: an overview. , 2009, Biotechnology advances.

[3]  Georges Bastin,et al.  A case study of adaptive nonlinear regulation of fed-batch biological reactors , 1995, Autom..

[4]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[5]  Alain Vande Wouwer,et al.  Adaptive and Robust Linearizing Control Strategies for Fed-Batch Cultures of Microorganisms Exhibiting Overflow Metabolism , 2010, ICINCO.

[6]  C. Herwig,et al.  Experimental verification and comparison of model predictive, PID and model inversion control in a Penicillium chrysogenum fed-batch process , 2020 .

[7]  J. Nielsen,et al.  Production of farnesene and santalene by Saccharomyces cerevisiae using fed-batch cultivations with RQ-controlled feed. , 2016, Biotechnology and bioengineering.

[8]  Mohamed Azlan Hussain,et al.  Review of the applications of neural networks in chemical process control - simulation and online implementation , 1999, Artif. Intell. Eng..

[9]  Fernando Morilla,et al.  Benchmark for PID control of Refrigeration Systems based on Vapour Compression , 2018 .

[10]  Zhongping Shi,et al.  An on-line adaptive control based on DO/pH measurements and ANN pattern recognition model for fed-batch cultivation , 2006 .

[11]  Krist V. Gernaey,et al.  A review of control strategies for manipulating the feed rate in fed-batch fermentation processes. , 2017, Journal of biotechnology.

[12]  Manuel A. Duarte-Mermoud,et al.  A robust hybrid observer for monitoring high-cell density cultures exhibiting overflow metabolism , 2021, Journal of Process Control.

[13]  Preventing Overflow Metabolism in Crabtree-Positive Microorganisms through On-Line Monitoring and Control of Fed-Batch Fermentations , 2018, Fermentation.

[14]  A. Rathore,et al.  Comparison and implementation of different control strategies for improving production of rHSA using Pichia pastoris. , 2019, Journal of biotechnology.

[15]  Krist V. Gernaey,et al.  Monitoring yeast fermentations by nonlinear infrared technology and chemometrics—understanding process correlations and indirect predictions , 2020, Applied Microbiology and Biotechnology.

[16]  Ralf Cord-Ruwisch,et al.  Online detection of feed demand in high cell density cultures of Escherichia coli by measurement of changes in dissolved oxygen transients in complex media , 2004, Biotechnology and bioengineering.

[17]  S. O. Ranaei Siadat,et al.  A robust feeding control strategy adjusted and optimized by a neural network for enhancing of alpha 1-antitrypsin production in Pichia pastoris , 2019, Biochemical Engineering Journal.

[18]  Chao Yu,et al.  An Online Respiratory Quotient-Feedback Strategy of Feeding Yeast Extract for Efficient Arachidonic Acid Production by Mortierella alpina , 2018, Front. Bioeng. Biotechnol..

[19]  N. W. F. Kossen,et al.  Regime analysis and scale-down: Tools to investigate the performance of bioreactors , 1987 .

[20]  Zhongping Shi,et al.  Effective and stable porcine interferon-α production by Pichia pastoris fed-batch cultivation with multi-variables clustering and analysis , 2010, Bioprocess and biosystems engineering.

[21]  Zhengxiang Wang,et al.  Effective induction of phytase in Pichia pastoris fed-batch culture using an ANN pattern recognition model-based on-line adaptive control strategy , 2007 .

[22]  Denis Dochain,et al.  Oxygen Control for an Industrial Pilot-Scale Fed-Batch Filamentous Fungal Fermentation , 2007 .

[23]  R. Biener,et al.  Calorimetric control of the specific growth rate during fed-batch cultures of Saccharomyces cerevisiae. , 2012, Journal of biotechnology.

[24]  Eduardo Agosin,et al.  Effective Dissolved Oxygen Control Strategy for High-Cell-Density Cultures , 2014, IEEE Latin America Transactions.

[25]  Michel Perrier,et al.  Extremum-seeking algorithm design for fed-batch cultures of microorganisms with overflow metabolism , 2011 .

[26]  Denis Dochain,et al.  Review and classification of recent observers applied in chemical process systems , 2015, Comput. Chem. Eng..

[27]  G. E. Noether.  Wilcoxon Confidence Intervals for Location Parameters in the Discrete Case , 1967 .

[28]  H. Noorman,et al.  Substrate gradients in bioreactors: origin and consequences , 1996 .

[29]  E. Agosin,et al.  Build Your Bioprocess on a Solid Strain—β-Carotene Production in Recombinant Saccharomyces cerevisiae , 2019, Front. Bioeng. Biotechnol..

[30]  Michal Dabros,et al.  Simple control of specific growth rate in biotechnological fed-batch processes based on enhanced online measurements of biomass , 2010, Bioprocess and biosystems engineering.

[31]  J. Ricardo Pérez-Correa,et al.  Modeling oxygen dissolution and biological uptake during pulse oxygen additions in oenological fermentations , 2012, Bioprocess and Biosystems Engineering.

[32]  Per Hagander,et al.  Bioreactor control using a probing feeding strategy and mid-ranging control , 2007 .

[33]  Alain Vande Wouwer,et al.  Nonlinear model predictive control of fed-batch cultures of micro-organisms exhibiting overflow metabolism: Assessment and robustness , 2012, Comput. Chem. Eng..

[34]  S. Enfors,et al.  Cell Segregation and Lysis Have Profound Effects on the Growth of Escherichia coli in High Cell Density Fed Batch Cultures , 1996, Biotechnology progress.

[35]  S. Mortazavi,et al.  Bioprocess and downstream optimization of recombinant human growth hormone in Pichia pastoris , 2018, Research in pharmaceutical sciences.

[36]  Joaquim M. S. Cabral,et al.  Real-time bioprocess monitoring: Part I: In situ sensors , 2006 .

[37]  Y. Chisti,et al.  Shear rate in stirred tank and bubble column bioreactors , 2006 .

[38]  P Neubauer,et al.  Determination of the maximum specific uptake capacities for glucose and oxygen in glucose-limited fed-batch cultivations of Escherichia coli. , 2001, Biotechnology and bioengineering.

[39]  R. Guthke,et al.  High-cell-density cultivation of microorganisms , 1999, Applied Microbiology and Biotechnology.

[40]  U. Stahl,et al.  Enhanced incorporation yield of cysteine for glutathione overproduction by fed-batch fermentation of Saccharomyces cerevisiae. , 2015, Journal of biotechnology.

[41]  G. Greller,et al.  Monitoring online biomass with a capacitance sensor during scale-up of industrially relevant CHO cell culture fed-batch processes in single-use bioreactors , 2019, Bioprocess and Biosystems Engineering.

[42]  Per Hagander,et al.  A probing feeding strategy for Escherichia coli cultures , 1999 .

[43]  I. Marison,et al.  Real-time monitoring and control of microbial bioprocesses with focus on the specific growth rate: current state and perspectives , 2012, Applied Microbiology and Biotechnology.

[44]  Bernhard Sonnleitner,et al.  Automated measurement and monitoring of bioprocesses: key elements of the M(3)C strategy. , 2013, Advances in biochemical engineering/biotechnology.

[45]  F. García-Ochoa,et al.  Oxygen uptake rate measurements both by the dynamic method and during the process growth of Rhodococcus erythropolis IGTS8: Modelling and difference in results , 2006 .

[46]  B Sonnleitner,et al.  Growth of Saccharomyces cerevisiae is controlled by its limited respiratory capacity: Formulation and verification of a hypothesis , 1986, Biotechnology and bioengineering.

[47]  J. Doucha,et al.  Production of high-density Chlorella culture grown in fermenters , 2010, Journal of Applied Phycology.

[48]  Ting Gong,et al.  Fed-batch high-cell-density fermentation strategies for Pichia pastoris growth and production , 2019, Critical reviews in biotechnology.

[49]  W. Deckwer,et al.  High cell density cultivation of Escherichia coli at controlled specific growth rate. , 1991, Journal of biotechnology.

[50]  S. Agatonovic-Kustrin,et al.  Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. , 2000, Journal of pharmaceutical and biomedical analysis.

[51]  Nina F. Thornhill,et al.  Estimation of dissolved carbon dioxide concentrations in aerobic fermentations , 1991 .

[52]  H Honda,et al.  Fuzzy control of bioprocess. , 2000, Journal of bioscience and bioengineering.

[53]  Thomas Hartmann,et al.  Controlling the specific growth rate via biomass trend regulation in filamentous fungi bioprocesses , 2017 .

[54]  C. Goudar,et al.  Influence of microbial concentration on the rheology of non-Newtonian fermentation broths , 1999, Applied Microbiology and Biotechnology.

[55]  Q. Hu,et al.  Ultrahigh‐cell‐density heterotrophic cultivation of the unicellular green microalga Scenedesmus acuminatus and application of the cells to photoautotrophic culture enhance biomass and lipid production , 2019, Biotechnology and bioengineering.

[56]  Z. Deng,et al.  Systematic metabolic engineering of Saccharomyces cerevisiae for lycopene overproduction. , 2019, Journal of agricultural and food chemistry.

[58]  N Hvala,et al.  Experimental design of an optimal phase duration control strategy used in batch biological wastewater treatment. , 2001, ISA transactions.

[59]  J A Roels,et al.  A quantitative description of the growth of Saccharomyces cerevisiae CBS 426 on a mixed substrate of glucose and ethanol , 1980, Biotechnology and bioengineering.

[60]  Ju Chu,et al.  Improved vitamin B(12) production by step-wise reduction of oxygen uptake rate under dissolved oxygen limiting level during fermentation process. , 2010, Bioresource technology.

[61]  C. Verduyn.  Physiology of yeasts in relation to biomass yields , 1991, Antonie van Leeuwenhoek.

[62]  E. Agosin,et al.  Engineering Saccharomyces cerevisiae for the Overproduction of β-Ionone and Its Precursor β-Carotene , 2020, Frontiers in Bioengineering and Biotechnology.

[63]  K. Zou,et al.  High cell density culture of baker's yeast FX‐2 based on pH‐stat coupling with respiratory quotient , 2019, Biotechnology and applied biochemistry.

[64]  Jens Nielsen,et al.  Lipid engineering combined with systematic metabolic engineering of Saccharomyces cerevisiae for high-yield production of lycopene. , 2019, Metabolic engineering.

[65]  Gen Larsson,et al.  Comparison of the Baker's yeast process performance in laboratory and production scale , 1998 .

[66]  E. Agosin,et al.  Heterologous production of the epoxycarotenoid violaxanthin in Saccharomyces cerevisiae. , 2020, Metabolic engineering.

[67]  Jay D. Keasling,et al.  Production of amorphadiene in yeast, and its conversion to dihydroartemisinic acid, precursor to the antimalarial agent artemisinin , 2012, Proceedings of the National Academy of Sciences.

[68]  Josep López-Santín,et al.  A simple feedback control of Escherichia coli growth for recombinant aldolase production in fed-batch mode , 2006 .

[69]  Radim Mareš,et al.  New International Formulation for the Viscosity of H2O , 2009 .

[70]  Krist V Gernaey,et al.  Towards smart biomanufacturing: a perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes , 2020, Journal of industrial microbiology & biotechnology.

[71]  E. Karlsson,et al.  On-line detection of acetate formation in Escherichia coli cultures using dissolved oxygen responses to feed transients. , 1999, Biotechnology and bioengineering.

[72]  Kamran Behdinan,et al.  Particle swarm approach for structural design optimization , 2007 .

[73]  Bernhard Sonnleitner,et al.  Controlled fed-batch by tracking the maximal culture capacity. , 2007, Journal of biotechnology.

[74]  M. Aghasadeghi,et al.  Comparative study of μ ‐stat methanol feeding control in fed‐batch fermentation of Pichia pastoris producing HBsAg: an open‐loop control versus recurrent artificial neural network‐based feedback control , 2019, Journal of Chemical Technology & Biotechnology.

[75]  S. Gehlen,et al.  Application of model-predictive control based on artificial neural networks to optimize the fed-batch process for riboflavin production. , 2000, Journal of biotechnology.

[76]  Cees Haringa,et al.  Euler-Lagrange analysis towards representative down-scaling of a 22 m3 aerobic S. cerevisiae fermentation , 2017 .

[77]  Suna Timur,et al.  A new set up for multi-analyte sensing: at-line bio-process monitoring. , 2011, Biosensors & bioelectronics.

[78]  Joseph S. Alford,et al.  Bioprocess control: Advances and challenges , 2006, Comput. Chem. Eng..

[79]  Denis Dochain,et al.  State and parameter estimation in chemical and biochemical processes: a tutorial , 2003 .

[80]  C. Fonteix,et al.  Fuzzy control of baker's yeast fed-batch bioprocess: A robustness study , 1994 .

[81]  O. Velev,et al.  Real-time monitoring and control of CHO cell apoptosis by in situ multifrequency scanning dielectric spectroscopy , 2019, Process Biochemistry.

[82]  T. Scheper,et al.  Alcohol biosensing by polyamidoamine (PAMAM)/cysteamine/alcohol oxidase‐modified gold electrode , 2010, Biotechnology progress.

[83]  T. Tan,et al.  High-cell-density fermentation for ergosterol production by Saccharomyces cerevisiae. , 2006, Journal of bioscience and bioengineering.

[84]  K. Gernaey,et al.  Understanding gradients in industrial bioreactors. , 2020, Biotechnology advances.

[85]  T. Tan,et al.  Effect of amino acids addition and feedback control strategies on the high-cell-density cultivation of Saccharomyces cerevisiae for glutathione production , 2007 .

[86]  Mustafa Türker,et al.  Model based control of minimal overflow metabolite in technical scale fed-batch yeast fermentation , 2010 .

[87]  Dörte Solle,et al.  Sensor systems for bioprocess monitoring , 2015 .

[88]  Gabriel Potvin,et al.  Bioprocess engineering aspects of heterologous protein production in Pichia pastoris: A review , 2012 .

[89]  He Huang,et al.  Development of a real-time bioprocess monitoring method for docosahexaenoic acid production by Schizochytrium sp. , 2016, Bioresource technology.

[90]  N. Aguila-Camacho,et al.  Improving the control energy use for first order plus time delay plants with fractional proportional integral controllers: a preliminary analysis , 2018, 2018 IEEE International Conference on Automation/XXIII Congress of the Chilean Association of Automatic Control (ICA-ACCA).

[91]  P. Zhu,et al.  Scaling-up Fermentation of Pichia pastoris to demonstration-scale using new methanol-feeding strategy and increased air pressure instead of pure oxygen supplement , 2016, Scientific Reports.

[92]  Oskars Grigs,et al.  Model Predictive Feeding Rate Control in Conventional and Single-use Lab-scale Bioreactors: A Study on Practical Application , 2016 .

[93]  S. Enfors,et al.  Growth and energy metabolism in aerobic fed-batch cultures of Saccharomyces cerevisiae: simulation and model verification. , 1998, Biotechnology and bioengineering.

[94]  Xingguo Wang,et al.  The relationship of oxygen uptake rate and k(L)a with rheological properties in high cell density cultivation of docosahexaenoic acid by Schizochytrium sp. S31. , 2014, Bioresource technology.