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Sharma S, Mahadevan J, Giri L, Mitra K. Identification of optimal flow rate for culture media, cell density, and oxygen toward maximization of virus production in a fed-batch baculovirus-insect cell system. Biotechnol Bioeng 2023; 120:3529-3542. [PMID: 37749905 DOI: 10.1002/bit.28558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 08/03/2023] [Accepted: 09/05/2023] [Indexed: 09/27/2023]
Abstract
In recent times, it has been realized that novel vaccines are required to combat emerging disease outbreaks, and faster optimization is required to respond to global vaccine demands. Although, fed-batch operations offer better productivity, experiment-based optimization of a new fed-batch process remains expensive and time-consuming. In this context, we propose a novel computational framework that can be used for process optimization and control of a fed-batch baculovirus-insect cell system. Since the baculovirus expression vector system (BEVS) is known to be widely used platforms for recombinant protein/vaccine production, we chose this system to demonstrate the identification of optimal profile. Toward this, first, we constructed a mathematical model that captures the time course of cell and virus growth in a baculovirus-insect cell system. Second, the proposed model was used for numerical analysis to determine the optimal operating profiles of control variables such as culture media, cell density, and oxygen based on a multiobjective optimal control formulation. Third, a detailed comparison between batch and fed-batch culture was perfromed along with a comparison between various alternatives of fed-batch operation. Finally, we demonstrate that a model-based quantification of controlled feed addition in fed-batch culture is capable of providing better productivity as compared to a batch culture. The proposed framework can be utilized for the estimation of optimal operating regions of different control variables to achieve maximum infected cell density and virus yield while minimizing the substrate/media, uninfected cell, and oxygen consumption.
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Affiliation(s)
- Surbhi Sharma
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, Telangana, India
| | - Jagadeesh Mahadevan
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, Telangana, India
| | - Lopamudra Giri
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, Telangana, India
| | - Kishalay Mitra
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, Telangana, India
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Ji C, Ma F, Wang J, Sun W. Profitability Related Industrial-Scale Batch Processes Monitoring via Deep Learning based Soft Sensor Development. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.108125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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3
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Gao X, Shardt YAW. EVOLVE·INFOMAX: An Unsupervised Learning Principle of Invariances for Nonlinear Dynamic Systems. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c03330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Affiliation(s)
- Xinrui Gao
- Department of Automation Engineering, Technical University of Ilmenau, Ilmenau, Thuringia98684, Germany
| | - Yuri A. W. Shardt
- Department of Automation Engineering, Technical University of Ilmenau, Ilmenau, Thuringia98684, Germany
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4
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Open benchmarks for assessment of process monitoring and fault diagnosis techniques: A review and critical analysis. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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5
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Destro F, Nagy ZK, Barolo M. A benchmark simulator for quality-by-design and quality-by-control studies in continuous pharmaceutical manufacturing - Intensified filtration-drying of crystallization slurries. Comput Chem Eng 2022; 163:107809. [PMID: 38178942 PMCID: PMC10765423 DOI: 10.1016/j.compchemeng.2022.107809] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
This article introduces ContCarSim, a benchmark simulator for the development and testing of quality-by-design and quality-by-control strategies in the continuous intensified filtration-drying of paracetamol/ethanol slurries on a novel carousel technology, developed by Alconbury Weston Ltd (United Kingdom). The simulator is based on a detailed mechanistic mathematical modeling framework, and has been validated with filtration and drying experiments on a prototype equipment. A set of design- and control-relevant challenges to be addressed through ContCarSim are proposed. A case study is developed, to demonstrate the features of the simulator and its suitability to design, test and optimize the unit operation. ContCarSim is expected to promote the transition to end-to-end continuous pharmaceutical manufacturing and the adoption of closed-loop quality control by the pharmaceutical industry. The simulator can also be employed as a benchmark for data analytics and process monitoring studies.
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Affiliation(s)
- Francesco Destro
- CAPE-Lab – Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova PD (Italy)
| | - Zoltan K. Nagy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Massimiliano Barolo
- CAPE-Lab – Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova PD (Italy)
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Lo-Thong-Viramoutou O, Charton P, Cadet XF, Grondin-Perez B, Saavedra E, Damour C, Cadet F. Non-linearity of Metabolic Pathways Critically Influences the Choice of Machine Learning Model. Front Artif Intell 2022; 5:744755. [PMID: 35757298 PMCID: PMC9226554 DOI: 10.3389/frai.2022.744755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 04/29/2022] [Indexed: 11/13/2022] Open
Abstract
The use of machine learning (ML) in life sciences has gained wide interest over the past years, as it speeds up the development of high performing models. Important modeling tools in biology have proven their worth for pathway design, such as mechanistic models and metabolic networks, as they allow better understanding of mechanisms involved in the functioning of organisms. However, little has been done on the use of ML to model metabolic pathways, and the degree of non-linearity associated with them is not clear. Here, we report the construction of different metabolic pathways with several linear and non-linear ML models. Different types of data are used; they lead to the prediction of important biological data, such as pathway flux and final product concentration. A comparison reveals that the data features impact model performance and highlight the effectiveness of non-linear models (e.g., QRF: RMSE = 0.021 nmol·min-1 and R2 = 1 vs. Bayesian GLM: RMSE = 1.379 nmol·min-1 R2 = 0.823). It turns out that the greater the degree of non-linearity of the pathway, the better suited a non-linear model will be. Therefore, a decision-making support for pathway modeling is established. These findings generally support the hypothesis that non-linear aspects predominate within the metabolic pathways. This must be taken into account when devising possible applications of these pathways for the identification of biomarkers of diseases (e.g., infections, cancer, neurodegenerative diseases) or the optimization of industrial production processes.
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Affiliation(s)
- Ophélie Lo-Thong-Viramoutou
- University of Paris, BIGR—Biologie Intégrée du Globule Rouge, Inserm, UMR_S1134, Paris, France
- Laboratory of Excellence GR-Ex, Paris, France
- Laboratory DSIMB, UMR_S1134, BIGR, Inserm, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| | - Philippe Charton
- University of Paris, BIGR—Biologie Intégrée du Globule Rouge, Inserm, UMR_S1134, Paris, France
- Laboratory of Excellence GR-Ex, Paris, France
- Laboratory DSIMB, UMR_S1134, BIGR, Inserm, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| | | | - Brigitte Grondin-Perez
- EnergyLab, EA 4079, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| | - Emma Saavedra
- Departamento de Bioquímica, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City, Mexico
| | - Cédric Damour
- EnergyLab, EA 4079, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| | - Frédéric Cadet
- University of Paris, BIGR—Biologie Intégrée du Globule Rouge, Inserm, UMR_S1134, Paris, France
- Laboratory of Excellence GR-Ex, Paris, France
- Laboratory DSIMB, UMR_S1134, BIGR, Inserm, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
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Ask M, Stocks SM. Aerobic bioreactors: condensers, evaporation rates, scale-up and scale-down. Biotechnol Lett 2022; 44:813-822. [PMID: 35650455 DOI: 10.1007/s10529-022-03258-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 05/02/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Hydrodynamics, mixing and shear are terms often used when explaining or modelling scale differences, but other scale differences, such as evaporation, can arise from non-hydrodynamic factors that can be managed with some awareness and effort. RESULTS We present an engineering approach to the prediction of evaporation rates in bioreactors based on gH2O/Nm3 of air entering and leaving the bioreactor and confirm its usefulness in a 28-run design of experiments investigating the effects of aeration rate (0.02 to 2.0 VVM), condenser temperature (10 to 20 °C), fill (2.5 to 5 kg), broth temperature (25 to 40 °C) and agitator speed (25 to 800 rpm). Aeration rate and condenser temperature used in the engineering prediction provided a practically useful estimate of evaporation; the other factors, while statistically identified as having some influence, were of negligible practical usefulness. Evaporation rates were never found to be zero, and could be at least 10% different to those expected at scale. CONCLUSIONS An assessment of evaporation rates for any project is encouraged, and it is recommended that the effects are accounted for by measurements, modelling or by tuning the exhaust cooling device temperature to minimize scale differences.
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Oh TH, Park HM, Kim JW, Lee JM. Integration of Reinforcement Learning and Model Predictive Control to Optimize Semi‐batch Bioreactor. AIChE J 2022. [DOI: 10.1002/aic.17658] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Tae Hoon Oh
- School of Chemical and Biological Engineering Institute of Chemical Processes, Seoul National University Seoul Republic of Korea
| | - Hyun Min Park
- School of Chemical and Biological Engineering Institute of Chemical Processes, Seoul National University Seoul Republic of Korea
| | - Jong Woo Kim
- Bioprocess Engineering Technische Universität Berlin Berlin Germany
| | - Jong Min Lee
- School of Chemical and Biological Engineering Institute of Chemical Processes, Seoul National University Seoul Republic of Korea
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Lee HJ, Lee S, Lee JM. Online Synchronization in Latent Variable Model Predictive Control for Trajectory Tracking of an Uneven Batch Process. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c03898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Hye Ji Lee
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Shinje Lee
- Engineering Development Research Center (EDRC), Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Jong Min Lee
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
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11
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Real-time synchronization with expected distribution of synchronized index for on-line monitoring of uneven multiphase batch process. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107490] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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12
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Kim JW, Park BJ, Oh TH, Lee JM. Model-based reinforcement learning and predictive control for two-stage optimal control of fed-batch bioreactor. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107465] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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13
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Recent advances in biocatalysis of nitrogen-containing heterocycles. Biotechnol Adv 2021; 54:107813. [PMID: 34450199 DOI: 10.1016/j.biotechadv.2021.107813] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/27/2021] [Accepted: 08/08/2021] [Indexed: 12/20/2022]
Abstract
Nitrogen-containing heterocycles (N-heterocycles) are ubiquitous in both organisms and pharmaceutical products. Biocatalysts are providing green approaches for synthesizing various N-heterocycles under mild reaction conditions. This review summarizes the recent advances in the biocatalysis of N-heterocycles through the discovery and engineering of natural N-heterocycle synthetic pathway, and the design of artificial synthetic routes, with an emphasis on biocatalysts applied in retrosynthetic design for preparing complex N-heterocycles. Furthermore, this review discusses the future prospects and challenges of biocatalysts involved in the synthesis of N-heterocycles.
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Liu K, Zhang J. Nonlinear process modelling using echo state networks optimised by covariance matrix adaption evolutionary strategy. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106730] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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15
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Kager J, Tuveri A, Ulonska S, Kroll P, Herwig C. Experimental verification and comparison of model predictive, PID and model inversion control in a Penicillium chrysogenum fed-batch process. Process Biochem 2020. [DOI: 10.1016/j.procbio.2019.11.023] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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16
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Duran-Villalobos CA, Goldrick S, Lennox B. Multivariate statistical process control of an industrial-scale fed-batch simulator. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2019.106620] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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17
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Wagner SG, Mähler C, Polte I, von Poschinger J, Löwe H, Kremling A, Pflüger-Grau K. An automated and parallelised DIY-dosing unit for individual and complex feeding profiles: Construction, validation and applications. PLoS One 2019; 14:e0217268. [PMID: 31216302 PMCID: PMC6583958 DOI: 10.1371/journal.pone.0217268] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 05/07/2019] [Indexed: 11/18/2022] Open
Abstract
Since biotechnological research becomes more and more important for industrial applications, there is an increasing need for scalable and controllable laboratory procedures. A widely used approach in biotechnological research to improve the performance of a process is to vary the growth rates in order to find the right balance between growth and the production. This can be achieved by the application of a suitable feeding strategy. During this initial bioprocess development, it is beneficial to have at hand cheap and easy setups that work in parallel (e.g. in shaking flasks). Unfortunately, there is a gap between these easy setups and defined and controllable processes, which are necessary for up-scaling to an industrial relevant volume. One prerequisite to test and evaluate different process strategies apart from batch-mode is the availability of pump systems that allow for defined feeding profiles in shaking flasks. To our knowledge, there is no suitable dosing device on the market which fulfils the requirements of being cheap, precise, programmable, and parallelizable. Commercially available dosing units are either already integrated in bioreactors and therefore inflexible, or not programmable, or expensive, or a combination of those. Here, we present a LEGO-MINDSTORMS-based syringe pump, which has the potential of being widely used in daily laboratory routine due to its low price, programmability, and parallelisability. The acquisition costs do not exceed 350 € for up to four dosing units, that are independently controllable with one EV3 block. The system covers flow rates ranging from 0.7 μL min-1 up to 210 mL min-1 with a reliable flux. One dosing unit can convey at maximum a volume of 20 mL (using all 4 units even up to 80 mL in total) over the whole process time. The design of the dosing unit enables the user to perform experiments with up to four different growth rates in parallel (each measured in triplicates) per EV3-block used. We estimate, that the LEGO-MINDSTORMS-based dosing unit with 12 syringes in parallel is reducing the costs up to 50-fold compared to a trivial version of a commercial pump system (~1500 €) which fits the same requirements. Using the pump, we set the growth rates of a E. coli HMS174/DE3 culture to values between 0.1 and 0.4 h-1 with a standard deviation of at best 0.35% and an average discrepancy of 13.2%. Additionally, we determined the energy demand of a culture for the maintenance of the pTRA-51hd plasmid by quantifying the changes in biomass yield with different growth rates set. Around 25% of total substrate taken up is used for plasmid maintenance. To present possible applications and show the flexibility of the system, we applied a constant feed to perform microencapsulation of Pseudomonas putida and an individual dosing profile for the purification of a his-tagged eGFP via IMAC. This smart and versatile dosing unit, which is ready-to-use without any prior knowledge in electronics and control, is affordable for everyone and due to its flexibility and broad application range a valuable addition to the laboratory routine.
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Affiliation(s)
- Sabine G. Wagner
- TU Munich, Systems Biotechnology, Faculty of Mechanical Engineering, Garching, Germany
| | - Christoph Mähler
- TU Munich, Biochemical Engineering, Faculty of Mechanical Engineering, Garching, Germany
| | - Ingmar Polte
- TU Munich, Biochemical Engineering, Faculty of Mechanical Engineering, Garching, Germany
| | - Jeremy von Poschinger
- TU Munich, Systems Biotechnology, Faculty of Mechanical Engineering, Garching, Germany
| | - Hannes Löwe
- TU Munich, Systems Biotechnology, Faculty of Mechanical Engineering, Garching, Germany
| | - Andreas Kremling
- TU Munich, Systems Biotechnology, Faculty of Mechanical Engineering, Garching, Germany
- * E-mail:
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Luo L, Bao S, Mao J, Tang D. Monitoring Batch Processes Using Sparse Parallel Factor Decomposition. Ind Eng Chem Res 2017. [DOI: 10.1021/acs.iecr.7b02618] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Lijia Luo
- Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Shiyi Bao
- Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Jianfeng Mao
- Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Di Tang
- Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, China
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20
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Mechanistic Fermentation Models for Process Design, Monitoring, and Control. Trends Biotechnol 2017; 35:914-924. [DOI: 10.1016/j.tibtech.2017.07.002] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Revised: 07/04/2017] [Accepted: 07/05/2017] [Indexed: 11/24/2022]
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Pachauri N, Singh V, Rani A. Two degree of freedom PID based inferential control of continuous bioreactor for ethanol production. ISA TRANSACTIONS 2017; 68:235-250. [PMID: 28351531 DOI: 10.1016/j.isatra.2017.03.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2016] [Revised: 12/13/2016] [Accepted: 03/21/2017] [Indexed: 06/06/2023]
Abstract
This article presents the development of inferential control scheme based on Adaptive linear neural network (ADALINE) soft sensor for the control of fermentation process. The ethanol concentration of bioreactor is estimated from temperature profile of the process using soft sensor. The prediction accuracy of ADALINE is enhanced by retraining it with immediate past measurements. The ADALINE and retrained ADALINE are used along with PID and 2-DOF-PID leading to APID, A2PID, RAPID and RA2PID inferential controllers. Further the parameters of 2-DOF-PID are optimized using Non-dominated sorted genetic algorithm-II and used with retrained ADALINE soft sensor which leads to RAN2PID inferential controller. Simulation results demonstrate that performance of proposed RAN2PID controller is better in comparison to other designed controllers in terms of qualitative and quantitative performance indices.
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Affiliation(s)
- Nikhil Pachauri
- Instrumentation and Control Engineering Division, Netaji Subhas Institute of Technology, University of Delhi, Sec-3 Dwarka, New Delhi 110078, India.
| | - Vijander Singh
- Instrumentation and Control Engineering Division, Netaji Subhas Institute of Technology, University of Delhi, Sec-3 Dwarka, New Delhi 110078, India.
| | - Asha Rani
- Instrumentation and Control Engineering Division, Netaji Subhas Institute of Technology, University of Delhi, Sec-3 Dwarka, New Delhi 110078, India.
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Tanatavikorn H, Yamashita Y. Batch Process Monitoring Based on Fuzzy Segmentation of Multivariate Time-Series. JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2017. [DOI: 10.1252/jcej.16we193] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | - Yoshiyuki Yamashita
- Department of Chemical Engineering, Tokyo University of Agriculture and Technology
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Kreyenschulte D, Emde F, Regestein L, Büchs J. Computational minimization of the specific energy demand of large-scale aerobic fermentation processes based on small-scale data. Chem Eng Sci 2016. [DOI: 10.1016/j.ces.2016.07.016] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Haringa C, Tang W, Deshmukh AT, Xia J, Reuss M, Heijnen JJ, Mudde RF, Noorman HJ. Euler-Lagrange computational fluid dynamics for (bio)reactor scale down: An analysis of organism lifelines. Eng Life Sci 2016; 16:652-663. [PMID: 27917102 PMCID: PMC5129516 DOI: 10.1002/elsc.201600061] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2016] [Revised: 07/11/2016] [Accepted: 07/18/2016] [Indexed: 12/28/2022] Open
Abstract
The trajectories, referred to as lifelines, of individual microorganisms in an industrial scale fermentor under substrate limiting conditions were studied using an Euler‐Lagrange computational fluid dynamics approach. The metabolic response to substrate concentration variations along these lifelines provides deep insight in the dynamic environment inside a large‐scale fermentor, from the point of view of the microorganisms themselves. We present a novel methodology to evaluate this metabolic response, based on transitions between metabolic “regimes” that can provide a comprehensive statistical insight in the environmental fluctuations experienced by microorganisms inside an industrial bioreactor. These statistics provide the groundwork for the design of representative scale‐down simulators, mimicking substrate variations experimentally. To focus on the methodology we use an industrial fermentation of Penicillium chrysogenum in a simplified representation, dealing with only glucose gradients, single‐phase hydrodynamics, and assuming no limitation in oxygen supply, but reasonably capturing the relevant timescales. Nevertheless, the methodology provides useful insight in the relation between flow and component fluctuation timescales that are expected to hold in physically more thorough simulations. Microorganisms experience substrate fluctuations at timescales of seconds, in the order of magnitude of the global circulation time. Such rapid fluctuations should be replicated in truly industrially representative scale‐down simulators.
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Affiliation(s)
- Cees Haringa
- Transport Phenomena Section Department of Chemical Engineering Delft University of Technology Delft The Netherlands
| | - Wenjun Tang
- State key laboratory of Bioreactor Engineering East China University of Science and Technology (ECUST) Shanghai People's Republic of China
| | | | - Jianye Xia
- State key laboratory of Bioreactor Engineering East China University of Science and Technology (ECUST) Shanghai People's Republic of China
| | - Matthias Reuss
- Stuttgart Research Center Systems Biology (SRCSB) University of Stuttgart Stuttgart Germany
| | - Joseph J Heijnen
- Cell Systems Engineering Department of Biotechnology Delft University of Technology Delft The Netherlands
| | - Robert F Mudde
- Transport Phenomena Section Department of Chemical Engineering Delft University of Technology Delft The Netherlands
| | - Henk J Noorman
- DSM Biotechnology Center Delft The Netherlands; Bio Separation Technology Department of Biotechnology Delft University of Technology Delft The Netherlands
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Martínez-Núñez MA, López VELY. Nonribosomal peptides synthetases and their applications in industry. ACTA ACUST UNITED AC 2016. [DOI: 10.1186/s40508-016-0057-6] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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26
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Perez-Pinera P, Han N, Cleto S, Cao J, Purcell O, Shah KA, Lee K, Ram R, Lu TK. Synthetic biology and microbioreactor platforms for programmable production of biologics at the point-of-care. Nat Commun 2016; 7:12211. [PMID: 27470089 PMCID: PMC4974573 DOI: 10.1038/ncomms12211] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2015] [Accepted: 06/07/2016] [Indexed: 11/10/2022] Open
Abstract
Current biopharmaceutical manufacturing systems are not compatible with portable or distributed production of biologics, as they typically require the development of single biologic-producing cell lines followed by their cultivation at very large scales. Therefore, it remains challenging to treat patients in short time frames, especially in remote locations with limited infrastructure. To overcome these barriers, we developed a platform using genetically engineered Pichia pastoris strains designed to secrete multiple proteins on programmable cues in an integrated, benchtop, millilitre-scale microfluidic device. We use this platform for rapid and switchable production of two biologics from a single yeast strain as specified by the operator. Our results demonstrate selectable and near-single-dose production of these biologics in <24 h with limited infrastructure requirements. We envision that combining this system with analytical, purification and polishing technologies could lead to a small-scale, portable and fully integrated personal biomanufacturing platform that could advance disease treatment at point-of-care.
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Affiliation(s)
- Pablo Perez-Pinera
- Synthetic Biology Group, Department of Biological Engineering and Electrical Engineering &Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
| | - Ningren Han
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
| | - Sara Cleto
- Synthetic Biology Group, Department of Biological Engineering and Electrical Engineering &Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
| | - Jicong Cao
- Synthetic Biology Group, Department of Biological Engineering and Electrical Engineering &Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
| | - Oliver Purcell
- Synthetic Biology Group, Department of Biological Engineering and Electrical Engineering &Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
| | - Kartik A Shah
- The David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Kevin Lee
- Pharyx Inc., Woburn, Massachusetts 01801, USA
| | - Rajeev Ram
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
| | - Timothy K Lu
- Synthetic Biology Group, Department of Biological Engineering and Electrical Engineering &Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.,The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02412, USA
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27
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Ochoa S. A new approach for finding smooth optimal feeding profiles in fed-batch fermentations. Biochem Eng J 2016. [DOI: 10.1016/j.bej.2015.09.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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28
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29
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Golabgir A, Hoch T, Zhariy M, Herwig C. Observability analysis of biochemical process models as a valuable tool for the development of mechanistic soft sensors. Biotechnol Prog 2015; 31:1703-15. [PMID: 26404038 DOI: 10.1002/btpr.2176] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Revised: 08/27/2015] [Indexed: 11/10/2022]
Abstract
By enabling the estimation of difficult-to-measure target variables using available indirect measurements, mechanistic soft sensors have become important tools for various bioprocess monitoring and control scenarios. Despite promising higher process efficiencies and increased process understanding, widespread application of soft sensors has been stalled by uncertainty about the feasibility and reliability of their estimations given present process analytical constraints. Observability analysis can provide an indication of the possibility and reliability of soft sensor estimations by analyzing the structural properties of first-principle (mechanistic) models. In addition, it can provide a criteria for selection of suitable measurement methods with respect to their information content; thereby leading to successful implementation of soft sensors in bioprocess development and manufacturing environments. We demonstrate the utility of observability analysis for two classes of upstream bioprocesses: the processes involving growth and ethanol formation by Saccharomyces cerevisiae and the process of penicillin production by Penicillium chrysogenum. Results obtained from laboratory-scale cultivations in addition to in-silico experiments enable a comparison of theoretical aspects of observability analysis and the real-life performance of soft sensors. By taking the expected error of measurements provided to the soft sensor into account, an innovative scaling approach facilitates a higher degree of comparability of observability results among various measurement configurations and process conditions.
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Affiliation(s)
- Aydin Golabgir
- Research Div. Biochemical Engineering, Inst. of Chemical Engineering, Vienna University of Technology, Vienna, Austria
| | - Thomas Hoch
- Software Competence Center Hagenberg GmbH, Hagenberg im Mühlkreis, Austria
| | - Mariya Zhariy
- Software Competence Center Hagenberg GmbH, Hagenberg im Mühlkreis, Austria
| | - Christoph Herwig
- Research Div. Biochemical Engineering, Inst. of Chemical Engineering, Vienna University of Technology, Vienna, Austria.,CD Laboratory on Mechanistic and Physiological Methods for Improved Bioprocesses, Research Div. Biochemical Engineering, Vienna University of Technology, Vienna, Austria
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30
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Prauße MTE, Schäuble S, Guthke R, Schuster S. Computing the various pathways of penicillin synthesis and their molar yields. Biotechnol Bioeng 2015; 113:173-81. [PMID: 26134880 DOI: 10.1002/bit.25694] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Revised: 06/05/2015] [Accepted: 06/22/2015] [Indexed: 11/09/2022]
Abstract
More than 80 years after its discovery, penicillin is still a widely used and commercially highly important antibiotic. Here, we analyse the metabolic network of penicillin synthesis in Penicillium chrysogenum based on the concept of elementary flux modes. In particular, we consider the synthesis of the invariant molecular core of the various subtypes of penicillin and the two major ways of incorporating sulfur: transsulfuration and direct sulfhydrylation. 66 elementary modes producing this invariant core are obtained. These show four different yields with respect to glucose, notably ½, 2/5, 1/3, and 2/7, with the highest yield of ½ occurring only when direct sulfhydrylation is used and α-aminoadipate is completely recycled. In the case of no recycling of this intermediate, we find the maximum yield to be 2/7. We compare these values with earlier literature values. Our analysis provides a systematic overview of the redundancy in penicillin synthesis and a detailed insight into the corresponding routes. Moreover, we derive suggestions for potential knockouts that could increase the average yield.
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Affiliation(s)
- Maria T E Prauße
- Department of Bioinformatics, University of Jena, Ernst-Abbe-Pl. 2, 07743 Jena, Germany.,Leibniz-Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Jena, Germany
| | - Sascha Schäuble
- Jena University Language & Information Engineering Lab, Jena, Germany
| | - Reinhard Guthke
- Leibniz-Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Jena, Germany
| | - Stefan Schuster
- Department of Bioinformatics, University of Jena, Ernst-Abbe-Pl. 2, 07743 Jena, Germany.
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