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Chen YC, Destouches L, Cook A, Fedorec AJH. Synthetic microbial ecology: engineering habitats for modular consortia. J Appl Microbiol 2024; 135:lxae158. [PMID: 38936824 DOI: 10.1093/jambio/lxae158] [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: 04/27/2024] [Revised: 06/13/2024] [Accepted: 06/26/2024] [Indexed: 06/29/2024]
Abstract
Microbiomes, the complex networks of micro-organisms and the molecules through which they interact, play a crucial role in health and ecology. Over at least the past two decades, engineering biology has made significant progress, impacting the bio-based industry, health, and environmental sectors; but has only recently begun to explore the engineering of microbial ecosystems. The creation of synthetic microbial communities presents opportunities to help us understand the dynamics of wild ecosystems, learn how to manipulate and interact with existing microbiomes for therapeutic and other purposes, and to create entirely new microbial communities capable of undertaking tasks for industrial biology. Here, we describe how synthetic ecosystems can be constructed and controlled, focusing on how the available methods and interaction mechanisms facilitate the regulation of community composition and output. While experimental decisions are dictated by intended applications, the vast number of tools available suggests great opportunity for researchers to develop a diverse array of novel microbial ecosystems.
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Affiliation(s)
- Yue Casey Chen
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
| | - Louie Destouches
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
| | - Alice Cook
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
| | - Alex J H Fedorec
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
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2
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Murugan C, Subbian S, Kaliyaperumal S, Sadasivuni KK, Siddiqui MIH, Muthusamy S, Rosen MA, Prakash C, Chan CK. An event triggered control scheme for enhanced production of Escherichia coli and biomass concentration during fed-batch cultivation. Heliyon 2024; 10:e32210. [PMID: 38975212 PMCID: PMC11226784 DOI: 10.1016/j.heliyon.2024.e32210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 05/08/2024] [Accepted: 05/29/2024] [Indexed: 07/09/2024] Open
Abstract
Control of a bioprocess is a challenging task mainly due to the nonlinearity of the process, the complex nature of microorganisms, and variations in critical parameters such as temperature, pH, and agitator speed. Generally, the optimum values chosen for critical parameters during Escherichia coli (E.coli) K-12fed-batch fermentation are37 ᵒC for temperature, 7 for pH, and 35 % for Dissolved Oxygen (DO). The objective of this research is to enhance biomass concentration while minimizing energy consumption. To achieve this, an Event-Triggered Control (ETC) scheme based on feedback-feed forward control is proposed. The ETC system dynamically adjusts the substrate feed rate in response to variations in critical parameters. We compare the performance of classical Proportional Integral (PI) controllers and advanced Model Predictive Control (MPC) controllers in terms of bioprocess yield. Initially, the data are collected from a laboratory-scaled 3L bioreactor setup under fed-batch operating conditions, and data-driven models are developed using system identification techniques. Then, classical Proportional Integral (PI) and advanced Model Predictive Control (MPC) based feedback controllers are developed for controlling the yield of bioprocess by manipulating substrate flow rate, and their performances are compared. PI and MPC-based Event Triggered Feed Forward Controllers are designed to increase the yield and to suppress the effect of known disturbances due to critical parameters. Whenever there is a variation in the value of a critical parameter, it is considered an event, and ETC initiates a control action by manipulating the substrate feed rate. PI and MPC-based ETC controllers are developed in simulation, and their closed-loop performances are compared. It is observed that the Integral Square Error (ISE) is notably minimized to 4.668 for MPC with disturbance and 4.742 for MPC with Feed Forward Control. Similarly, the Integral Absolute Error (IAE) reduces to 2.453 for MPC with disturbance and 0.8124 for MPC with Feed Forward Control. The simulation results reveal that the MPC-based ETC control scheme enhances the biomass yield by 7 %, and this result is verified experimentally. This system dynamically adjusts the substrate feed rate in response to variations in critical parameters, which is a novel approach in the field of bioprocess control. Also, the proposed control schemes help reduce the frequency of communication between controller and actuator, which reduces power consumption.
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Affiliation(s)
- Chitra Murugan
- Department of Electronics and Communication Engineering, Bharath Institute of Higher Education and Research (Deemed to be University), Chennai, Tamil Nadu, India
| | - Sutha Subbian
- Department of Instrumentation Engineering, Madras Institute of Technology Campus, Anna University Chennai, Tamil Nadu, India
| | - Saravanan Kaliyaperumal
- Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology (Deemed to be University), Kattankulathur, Chennai, Tamil Nadu, India
| | - Kishor Kumar Sadasivuni
- Centre for Advanced Materials, Qatar University, Qatar
- Department of Mechanical and Industrial Engineering, Qatar University, Qatar
| | | | - Suresh Muthusamy
- Department of Electrical and Electronics Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India
| | - Marc A. Rosen
- Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Applied Sciences, University of Ontario Institute of Technology, Oshawa, Canada
- Centre for Research Impact and Outcomes, Chitkara University, Rajpura, Punjab, India
| | - Chander Prakash
- Faculty of Engineering and Quantity Surveying, INTI International University, Putra Nilai, 71800, Nilai, Negeri Sembilan, Malaysia
| | - Choon Kit Chan
- Faculty of Engineering and Quantity Surveying, INTI International University, Putra Nilai, 71800, Nilai, Negeri Sembilan, Malaysia
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3
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Allampalli SSP, Sivaprakasam S. Unveiling the potential of specific growth rate control in fed-batch fermentation: bridging the gap between product quantity and quality. World J Microbiol Biotechnol 2024; 40:196. [PMID: 38722368 DOI: 10.1007/s11274-024-03993-1] [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: 02/21/2024] [Accepted: 04/18/2024] [Indexed: 05/18/2024]
Abstract
During the epoch of sustainable development, leveraging cellular systems for production of diverse chemicals via fermentation has garnered attention. Industrial fermentation, extending beyond strain efficiency and optimal conditions, necessitates a profound understanding of microorganism growth characteristics. Specific growth rate (SGR) is designated as a key variable due to its influence on cellular physiology, product synthesis rates and end-product quality. Despite its significance, the lack of real-time measurements and robust control systems hampers SGR control strategy implementation. The narrative in this contribution delves into the challenges associated with the SGR control and presents perspectives on various control strategies, integration of soft-sensors for real-time measurement and control of SGR. The discussion highlights practical and simple SGR control schemes, suggesting their seamless integration into industrial fermenters. Recommendations provided aim to propose new algorithms accommodating mechanistic and data-driven modelling for enhanced progress in industrial fermentation in the context of sustainable bioprocessing.
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Affiliation(s)
- Satya Sai Pavan Allampalli
- BioPAT Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Assam, 781039, India
| | - Senthilkumar Sivaprakasam
- BioPAT Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Assam, 781039, India.
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4
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Gysel E, Larijani L, Kallos MS, Krawetz RJ. Suicide gene-enabled cell therapy: A novel approach to scalable human pluripotent stem cell quality control. Bioessays 2023; 45:e2300037. [PMID: 37582645 DOI: 10.1002/bies.202300037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 07/28/2023] [Accepted: 08/02/2023] [Indexed: 08/17/2023]
Abstract
There are an increasing number of cell therapy approaches being studied and employed world-wide. An emerging area in this field is the use of human pluripotent stem cell (hPSC) products for the treatment of injuries/diseases that cannot be effectively managed through current approaches. However, as with any cell therapy, vast numbers of functional and safe cells are required. Bioreactors provide an attractive avenue to generate clinically relevant cell numbers with decreased labour and decreased batch to batch variation. Yet, current methods of performing quality control are not readily scalable to the cell densities produced during bioreactor scale-up. One potential solution is the application of inducible/controllable suicide genes that can trigger cell death in unwanted cell types. These types of approaches have been demonstrated to increase the quality and safety of the resultant cell products. In this review, we will provide background on these approaches and how they could be used together with bioreactor technology to create effective bioprocesses for the generation of high quality and safe hPSCs for use in regenerative medicine approaches.
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Affiliation(s)
- Emilie Gysel
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB, Canada
- Department of Biomedical Engineering, University of Calgary, Calgary, AB, Canada
| | - Leila Larijani
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB, Canada
- Department of Biomedical Engineering, University of Calgary, Calgary, AB, Canada
| | - Michael S Kallos
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB, Canada
- Department of Biomedical Engineering, University of Calgary, Calgary, AB, Canada
- Department of Cell Biology and Anatomy, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Roman J Krawetz
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB, Canada
- Department of Biomedical Engineering, University of Calgary, Calgary, AB, Canada
- Department of Cell Biology and Anatomy, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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5
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Wainaina S, Taherzadeh MJ. Automation and artificial intelligence in filamentous fungi-based bioprocesses: A review. BIORESOURCE TECHNOLOGY 2023; 369:128421. [PMID: 36462761 DOI: 10.1016/j.biortech.2022.128421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/25/2022] [Accepted: 11/27/2022] [Indexed: 06/17/2023]
Abstract
By utilizing their powerful metabolic versatility, filamentous fungi can be utilized in bioprocesses aimed at achieving circular economy. With the current digital transformation within the biomanufacturing sector, the interest of automating fungi-based systems has intensified. The purpose of this paper was therefore to review the potentials connected to the use of automation and artificial intelligence in fungi-based systems. Automation is characterized by the substitution of manual tasks with mechanized tools. Artificial intelligence is, on the other hand, a domain within computer science that aims at designing tools and machines with the capacity to execute functions that would usually require human aptitude. Process flexibility, enhanced data reliability and increased productivity are some of the benefits of integrating automation and artificial intelligence in fungi-based bioprocesses. One of the existing gaps that requires further investigation is the use of such data-based technologies in the production of food from fungi.
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Affiliation(s)
- Steven Wainaina
- Swedish Centre for Resource Recovery, University of Borås, 50190 Borås, Sweden
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6
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Jones J, Kindembe D, Branton H, Lawal N, Montero EL, Mack J, Shi S, Patton R, Montague G. Improved Control Strategies for the Environment Within Cell Culture Bioreactors. FOOD AND BIOPRODUCTS PROCESSING 2023. [DOI: 10.1016/j.fbp.2023.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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7
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Rodriguez-Jara M, Ramírez-Castelan CE, Samano-Perfecto Q, Ricardez-Sandoval LA, Puebla H. Robust control designs for microalgae cultivation in continuous photobioreactors. INTERNATIONAL JOURNAL OF CHEMICAL REACTOR ENGINEERING 2023. [DOI: 10.1515/ijcre-2022-0115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Abstract
Microalgae are used to produce renewable biofuels and high-value components and in bioremediation and CO2 sequestration tasks. These increasing applications, in conjunction with a desirable constant large-scale productivity, motivate the development and application of practical controllers. This paper addresses the application of robust control schemes for microalgae cultivation in continuous photobioreactors. Due to the model uncertainties and external perturbations, robust control designs are required to guarantee the desired microalgae productivity. Furthermore, simple controller designs are desirable for practical implementation purposes. Therefore, two robust control designs are applied and evaluated in this paper for two relevant case studies of microalgae cultivation in photobioreactors. The first control design is based on an enhanced simple-input output model with uncertain estimation. The second control design is the robust nonlinear model predictive control considering different uncertain scenarios. Numerical simulations of two case studies aimed at lipid production and CO2 capture under different conditions are presented to evaluate the robust closed-loop performance.
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Affiliation(s)
- Mariana Rodriguez-Jara
- Departameto de Energía , Universidad Autónoma Metropolitana-Azcapotzalco , Cd. de México , México
| | | | | | | | - Hector Puebla
- Departameto de Energía , Universidad Autónoma Metropolitana-Azcapotzalco , Cd. de México , México
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8
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Robust Control Based on Modeling Error Compensation of Microalgae Anaerobic Digestion. FERMENTATION-BASEL 2022. [DOI: 10.3390/fermentation9010034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Microalgae are used to produce renewable biofuels (biodiesel, bioethanol, biogas, and biohydrogen) and high-value-added products, as well as in bioremediation and CO2 sequestration tasks. In the case of anaerobic digestion of microalgae, biogas can be produced from mainly proteins and carbohydrates. Anaerobic digestion is a complex process that involves several stages and is susceptible to operational instability due to various factors. Robust controllers with simple structure and design are necessary for practical implementation purposes and to achieve a proper process operation despite process variabilities, uncertainties, and complex interactions. This paper presents the application of a control design based on the modeling error compensation technique for the anaerobic digestion of microalgae. The control design departs from a low-order input–output model by enhancement with uncertainty estimation. The results show that achieving desired organic pollution levels and methanogenic biomass concentrations as well as minimizing the effect of external perturbations on a benchmark case study of the anaerobic digestion of microalgae is possible with the proposed control design.
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9
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Pappenreiter M, Döbele S, Striedner G, Jungbauer A, Sissolak B. Model predictive control for steady-state performance in integrated continuous bioprocesses. Bioprocess Biosyst Eng 2022; 45:1499-1513. [PMID: 35915164 PMCID: PMC9399210 DOI: 10.1007/s00449-022-02759-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 07/16/2022] [Indexed: 11/06/2022]
Abstract
Perfusion bioreactors are commonly used for the continuous production of monoclonal antibodies (mAb). One potential benefit of continuous bioprocessing is the ability to operate under steady-state conditions for an extended process time. However, the process performance is often limited by the feedback control of feed, harvest, and bleed flow rates. If the future behavior of a bioprocess can be adequately described, predictive control can reduce set point deviations and thereby maximize process stability. In this study, we investigated the predictive control of biomass in a perfusion bioreactor integrated to a non-chromatographic capture step, in a series of Monte-Carlo simulations. A simple algorithm was developed to estimate the current and predict the future viable cell concentrations (VCC) of the bioprocess. This feature enabled the single prediction controller (SPC) to compensate for process variations that would normally be transported to adjacent units in integrated continuous bioprocesses (ICB). Use of this SPC strategy significantly reduced biomass, product concentration, and harvest flow variability and stabilized the operation over long periods of time compared to simulations using feedback control strategies. Additionally, we demonstrated the possibility of maximizing product yields simply by adjusting perfusion control strategies. This method could be used to prevent savings in total product losses of 4.5–10% over 30 days of protein production.
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Affiliation(s)
- Magdalena Pappenreiter
- Innovation Management, Bilfinger Life Science GmbH, Salzburg, Austria.,Department of Biotechnology, Institute of Bioprocess Science and Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Sebastian Döbele
- Innovation Management, Bilfinger Life Science GmbH, Salzburg, Austria
| | - Gerald Striedner
- Department of Biotechnology, Institute of Bioprocess Science and Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Alois Jungbauer
- Department of Biotechnology, Institute of Bioprocess Science and Engineering, University of Natural Resources and Life Sciences, Vienna, Austria.
| | - Bernhard Sissolak
- Innovation Management, Bilfinger Life Science GmbH, Salzburg, Austria
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10
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Sharma N, Liu YA. A Hybrid
Science‐Guided
Machine Learning Approach for Modeling Chemical Processes: A Review. AIChE J 2022. [DOI: 10.1002/aic.17609] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Niket Sharma
- AspenTech Center of Excellence in Process System Engineering, Department of Chemical Engineering Virginia Polytechnic Institute and State University Blacksburg Virginia USA
| | - Y. A. Liu
- AspenTech Center of Excellence in Process System Engineering, Department of Chemical Engineering Virginia Polytechnic Institute and State University Blacksburg Virginia USA
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11
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Käßer L, Rotter M, Coletta L, Salzig D, Czermak P. Process intensification for the continuous production of an antimicrobial peptide in stably-transformed Sf-9 insect cells. Sci Rep 2022; 12:1086. [PMID: 35058492 PMCID: PMC8776851 DOI: 10.1038/s41598-022-04931-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 01/04/2022] [Indexed: 01/22/2023] Open
Abstract
The antibiotic resistance crisis has prompted research into alternative candidates such as antimicrobial peptides (AMPs). However, the demand for such molecules can only be met by continuous production processes, which achieve high product yields and offer compatibility with the Quality-by-Design initiative by implementing process analytical technologies such as turbidimetry and dielectric spectroscopy. We developed batch and perfusion processes at the 2-L scale for the production of BR033, a cecropin-like AMP from Lucilia sericata, in stably-transformed polyclonal Sf-9 cells. This is the first time that BR033 has been expressed as a recombinant peptide. Process analytical technology facilitated the online monitoring and control of cell growth, viability and concentration. The perfusion process increased productivity by ~ 180% compared to the batch process and achieved a viable cell concentration of 1.1 × 107 cells/mL. Acoustic separation enabled the consistent retention of 98.5–100% of the cells, viability was > 90.5%. The recombinant AMP was recovered from the culture broth by immobilized metal affinity chromatography and gel filtration and was able to inhibit the growth of Escherichia coli K12. These results demonstrate a successful, integrated approach for the development and intensification of a process from cloning to activity testing for the production of new biopharmaceutical candidates.
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12
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DeBenedictis EA, Chory EJ, Gretton DW, Wang B, Golas S, Esvelt KM. Systematic molecular evolution enables robust biomolecule discovery. Nat Methods 2022; 19:55-64. [PMID: 34969982 DOI: 10.1038/s41592-021-01348-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 11/09/2021] [Indexed: 11/09/2022]
Abstract
Evolution occurs when selective pressures from the environment shape inherited variation over time. Within the laboratory, evolution is commonly used to engineer proteins and RNA, but experimental constraints have limited the ability to reproducibly and reliably explore factors such as population diversity, the timing of environmental changes and chance on outcomes. We developed a robotic system termed phage- and robotics-assisted near-continuous evolution (PRANCE) to comprehensively explore biomolecular evolution by performing phage-assisted continuous evolution in high-throughput. PRANCE implements an automated feedback control system that adjusts the stringency of selection in response to real-time measurements of each molecular activity. In evolving three distinct types of biomolecule, we find that evolution is reproducibly altered by both random chance and the historical pattern of environmental changes. This work improves the reliability of protein engineering and enables the systematic analysis of the historical, environmental and random factors governing biomolecular evolution.
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Affiliation(s)
- Erika A DeBenedictis
- Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Emma J Chory
- Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dana W Gretton
- Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brian Wang
- Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stefan Golas
- Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kevin M Esvelt
- Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
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13
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Survyla A, Levisauskas D, Urniezius R, Simutis R. An oxygen-uptake-rate-based estimator of the specific growth rate in Escherichia coli BL21 strains cultivation processes. Comput Struct Biotechnol J 2021; 19:5856-5863. [PMID: 34765100 PMCID: PMC8564730 DOI: 10.1016/j.csbj.2021.10.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 10/05/2021] [Accepted: 10/09/2021] [Indexed: 11/24/2022] Open
Abstract
The cell cultivation process in a bioreactor is a high-value manufacturing process that requires excessive monitoring and control compatibility. The specific cell growth rate is a crucial parameter that describes the online quality of the cultivation process. Most methods and algorithms developed for online estimations of the specific growth rate controls in batch and fed-batch microbial cultivation processes rely on biomass growth models. In this paper, we present a soft sensor – a specific growth rate estimator that does not require a particular bioprocess model. The approach for online estimation of the specific growth rate is based on an online measurement of the oxygen uptake rate. The feasibility of the estimator developed in this study was determined in two ways. First, we used numerical simulations on a virtual platform, where the cell culture processes were theoretically modeled. Next, we performed experimental validation based on laboratory-scale (7, 12, 15 L) bioreactor experiments with three different Escherichia coli BL21 cell strains.
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Affiliation(s)
- Arnas Survyla
- Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367 Kaunas, Lithuania
| | - Donatas Levisauskas
- Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367 Kaunas, Lithuania
| | - Renaldas Urniezius
- Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367 Kaunas, Lithuania
| | - Rimvydas Simutis
- Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367 Kaunas, Lithuania
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14
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Simple Gain-Scheduled Control System for Dissolved Oxygen Control in Bioreactors. Processes (Basel) 2021. [DOI: 10.3390/pr9091493] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
An adaptive control system for the set-point control and disturbance rejection of biotechnological-process parameters is presented. The gain scheduling of PID (PI) controller parameters is based on only controller input/output signals and does not require additional measurement of process variables for controller-parameter adaptation. Realization of the proposed system does not depend on the instrumentation-level of the bioreactor and is, therefore, attractive for practical application. A simple gain-scheduling algorithm is developed, using tendency models of the controlled process. Dissolved oxygen concentration was controlled using the developed control system. The biotechnological process was simulated in fed-batch operating mode, under extreme operating conditions (the oxygen uptake-rate’s rapidly and widely varying, feeding and aeration rate disturbances). In the simulation experiments, the gain-scheduled controller demonstrated robust behavior and outperformed the compared conventional PI controller with fixed parameters.
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15
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Rathore AS, Mishra S, Nikita S, Priyanka P. Bioprocess Control: Current Progress and Future Perspectives. Life (Basel) 2021; 11:life11060557. [PMID: 34199245 PMCID: PMC8231968 DOI: 10.3390/life11060557] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 06/09/2021] [Accepted: 06/10/2021] [Indexed: 02/07/2023] Open
Abstract
Typical bioprocess comprises of different unit operations wherein a near optimal environment is required for cells to grow, divide, and synthesize the desired product. However, bioprocess control caters to unique challenges that arise due to non-linearity, variability, and complexity of biotech processes. This article presents a review of modern control strategies employed in bioprocessing. Conventional control strategies (open loop, closed loop) along with modern control schemes such as fuzzy logic, model predictive control, adaptive control and neural network-based control are illustrated, and their effectiveness is highlighted. Furthermore, it is elucidated that bioprocess control is more than just automation, and includes aspects such as system architecture, software applications, hardware, and interfaces, all of which are optimized and compiled as per demand. This needs to be accomplished while keeping process requirement, production cost, market value of product, regulatory constraints, and data acquisition requirements in our purview. This article aims to offer an overview of the current best practices in bioprocess control, monitoring, and automation.
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16
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Schlembach I, Grünberger A, Rosenbaum MA, Regestein L. Measurement Techniques to Resolve and Control Population Dynamics of Mixed-Culture Processes. Trends Biotechnol 2021; 39:1093-1109. [PMID: 33573846 PMCID: PMC7612867 DOI: 10.1016/j.tibtech.2021.01.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 01/15/2021] [Accepted: 01/15/2021] [Indexed: 12/22/2022]
Abstract
Microbial mixed cultures are gaining increasing attention as biotechnological production systems, since they offer a large but untapped potential for future bioprocesses. Effects of secondary metabolite induction and advantages of labor division for the degradation of complex substrates offer new possibilities for process intensification. However, mixed cultures are highly complex, and, consequently, many biotic and abiotic parameters are required to be identified, characterized, and ideally controlled to establish a stable bioprocess. In this review, we discuss the advantages and disadvantages of existing measurement techniques for identifying, characterizing, monitoring, and controlling mixed cultures and highlight promising examples. Moreover, existing challenges and emerging technologies are discussed, which lay the foundation for novel analytical workflows to monitor mixed-culture bioprocesses.
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Affiliation(s)
- Ivan Schlembach
- Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute, Adolf-Reichwein-Str. 23, 07745 Jena, Germany; Faculty for Biological Sciences, Friedrich-Schiller-University Jena, Bachstrasse 18K, 07743 Jena, Germany
| | - Alexander Grünberger
- Multiscale Bioengineering, Faculty of Technology, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany
| | - Miriam A Rosenbaum
- Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute, Adolf-Reichwein-Str. 23, 07745 Jena, Germany; Faculty for Biological Sciences, Friedrich-Schiller-University Jena, Bachstrasse 18K, 07743 Jena, Germany
| | - Lars Regestein
- Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute, Adolf-Reichwein-Str. 23, 07745 Jena, Germany.
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Sinner P, Stiegler M, Herwig C, Kager J. Noninvasive online monitoring of Corynebacterium glutamicum fed-batch bioprocesses subject to spent sulfite liquor raw material uncertainty. BIORESOURCE TECHNOLOGY 2021; 321:124395. [PMID: 33285509 DOI: 10.1016/j.biortech.2020.124395] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 11/04/2020] [Accepted: 11/05/2020] [Indexed: 06/12/2023]
Abstract
In this study the use of a particle filter algorithm to monitor Corynebacterium glutamicum fed-batch bioprocesses with uncertain raw material input composition is shown. The designed monitoring system consists of a dynamic model describing biomass growth on spent sulfite liquor. Based on particle filtering, model simulations are aligned with continuously and noninvasively measured carbon evolution and oxygen uptake rates, giving an estimate of the most probable culture state. Applied on two validation experiments, culture states were accurately estimated during batch and fed-batch operations with root mean square errors below 1.1 g L-1 for biomass, 0.6 g L-1 for multiple substrate concentrations and 0.01 g g-1 h-1 for biomass specific substrate uptake rates. Additionally, upon fed-batch start uncertain feedstock concentrations were corrected by the estimator without the need of any additional measurements. This provides a solid basis towards a more robust operation of bioprocesses utilizing lignocellulosic side streams.
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Affiliation(s)
- Peter Sinner
- Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorfer Straße 1a, 1060 Vienna, Austria
| | - Marlene Stiegler
- Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorfer Straße 1a, 1060 Vienna, Austria
| | - Christoph Herwig
- Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorfer Straße 1a, 1060 Vienna, Austria
| | - Julian Kager
- Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorfer Straße 1a, 1060 Vienna, Austria.
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18
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Process maps with metabolic constraints for bioethanol production by continuous fermentation. Chem Eng Sci 2021. [DOI: 10.1016/j.ces.2020.116134] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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19
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Usage of Digital Twins Along a Typical Process Development Cycle. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2020. [PMID: 33346864 DOI: 10.1007/10_2020_149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register]
Abstract
Digital methods for process design, monitoring, and control can convert classical trial-and-error bioprocess development to a quantitative engineering approach. By interconnecting hardware, software, data, and humans currently untapped process optimization potential can be accessed. The key component within such a framework is a digital twin interacting with its physical process counterpart. In this chapter, we show how digital twin guided process development can be applied on an exemplary microbial cultivation process. The usage of digital twins is described along a typical process development cycle, ranging from early strain characterization to real-time control applications. Along an illustrative case study on microbial upstream bioprocessing, we emphasize that digital twins can integrate entire process development cycles if the digital twin itself and the underlying models are continuously adapted to newly available data. Therefore, the digital twin can be regarded as a powerful knowledge management tool and a decision support system for efficient process development. Its full potential can be deployed in a real-time environment where targeted control actions can further improve process performance.
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20
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Challenges and Opportunities on Nonlinear State Estimation of Chemical and Biochemical Processes. Processes (Basel) 2020. [DOI: 10.3390/pr8111462] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
This paper provides an overview of nonlinear state estimation techniques along with a discussion on the challenges and opportunities for future work in the field. Emphasis is given on Bayesian methods such as moving horizon estimation (MHE) and extended Kalman filter (EKF). A discussion on Bayesian, deterministic, and hybrid methods is provided and examples of each of these methods are listed. An approach for nonlinear state estimation design is included to guide the selection of the nonlinear estimator by the user/practitioner. Some of the current challenges in the field are discussed involving covariance estimation, uncertainty quantification, time-scale multiplicity, bioprocess monitoring, and online implementation. A case study in which MHE and EKF are applied to a batch reactor system is addressed to highlight the challenges of these technologies in terms of performance and computational time. This case study is followed by some possible opportunities for state estimation in the future including the incorporation of more efficient optimization techniques and development of heuristics to streamline the further adoption of MHE.
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21
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Noll P, Henkel M. History and Evolution of Modeling in Biotechnology: Modeling & Simulation, Application and Hardware Performance. Comput Struct Biotechnol J 2020; 18:3309-3323. [PMID: 33240472 PMCID: PMC7670204 DOI: 10.1016/j.csbj.2020.10.018] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 10/15/2020] [Accepted: 10/17/2020] [Indexed: 12/17/2022] Open
Abstract
Biological systems are typically composed of highly interconnected subunits and possess an inherent complexity that make monitoring, control and optimization of a bioprocess a challenging task. Today a toolset of modeling techniques can provide guidance in understanding complexity and in meeting those challenges. Over the last four decades, computational performance increased exponentially. This increase in hardware capacity allowed ever more detailed and computationally intensive models approaching a "one-to-one" representation of the biological reality. Fueled by governmental guidelines like the PAT initiative of the FDA, novel soft sensors and techniques were developed in the past to ensure product quality and provide data in real time. The estimation of current process state and prediction of future process course eventually enabled dynamic process control. In this review, past, present and envisioned future of models in biotechnology are compared and discussed with regard to application in process monitoring, control and optimization. In addition, hardware requirements and availability to fit the needs of increasingly more complex models are summarized. The major techniques and diverse approaches of modeling in industrial biotechnology are compared, and current as well as future trends and perspectives are outlined.
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Affiliation(s)
- Philipp Noll
- Institute of Food Science and Biotechnology, Department of Bioprocess Engineering (150k), University of Hohenheim, Fruwirthstr. 12, 70599 Stuttgart, Germany
| | - Marius Henkel
- Institute of Food Science and Biotechnology, Department of Bioprocess Engineering (150k), University of Hohenheim, Fruwirthstr. 12, 70599 Stuttgart, Germany
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22
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Sanicola HW, Stewart CE, Mueller M, Ahmadi F, Wang D, Powell SK, Sarkar K, Cutbush K, Woodruff MA, Brafman DA. Guidelines for establishing a 3-D printing biofabrication laboratory. Biotechnol Adv 2020; 45:107652. [PMID: 33122013 DOI: 10.1016/j.biotechadv.2020.107652] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/21/2020] [Accepted: 10/23/2020] [Indexed: 12/23/2022]
Abstract
Advanced manufacturing and 3D printing are transformative technologies currently undergoing rapid adoption in healthcare, a traditionally non-manufacturing sector. Recent development in this field, largely enabled by merging different disciplines, has led to important clinical applications from anatomical models to regenerative bioscaffolding and devices. Although much research to-date has focussed on materials, designs, processes, and products, little attention has been given to the design and requirements of facilities for enabling clinically relevant biofabrication solutions. These facilities are critical to overcoming the major hurdles to clinical translation, including solving important issues such as reproducibility, quality control, regulations, and commercialization. To improve process uniformity and ensure consistent development and production, large-scale manufacturing of engineered tissues and organs will require standardized facilities, equipment, qualification processes, automation, and information systems. This review presents current and forward-thinking guidelines to help design biofabrication laboratories engaged in engineering model and tissue constructs for therapeutic and non-therapeutic applications.
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Affiliation(s)
- Henry W Sanicola
- Faculty of Medicine, The University of Queensland, Brisbane 4006, Australia
| | - Caleb E Stewart
- Department of Neurosurgery, Louisiana State Health Sciences Center, Shreveport, LA 71103, USA.
| | | | - Farzad Ahmadi
- Department of Electrical and Computer Engineering, Youngstown State University, Youngstown, OH 44555, USA
| | - Dadong Wang
- Quantitative Imaging Research Team, Data61, Commonwealth Scientific and Industrial Research Organization, Marsfield, NSW 2122, Australia
| | - Sean K Powell
- Science and Engineering Faculty, Queensland University of Technology, Brisbane 4029, Australia
| | - Korak Sarkar
- M3D Laboratory, Ochsner Health System, New Orleans, LA 70121, USA
| | - Kenneth Cutbush
- Faculty of Medicine, The University of Queensland, Brisbane 4006, Australia
| | - Maria A Woodruff
- Science and Engineering Faculty, Queensland University of Technology, Brisbane 4029, Australia.
| | - David A Brafman
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA.
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23
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Fuzzy Logic-Based Adaptive Control of Specific Growth Rate in Fed-Batch Biotechnological Processes. A Simulation Study. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10196818] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This article presents the development and application of a distinct adaptive control algorithm that is based on fuzzy logic and was used to control the specific growth rate (SGR) in a fed-batch biotechnological process. The developed control algorithm was compared with two adaptive control systems that were based on a model-free adaptive technique and gain scheduling technique. A typical mathematical model of recombinant Escherichia coli fed-batch cultivation process was selected to evaluate the performance of the fuzzy-based control algorithm. The investigated control techniques performed similarly when considering the whole process duration. The adaptive PI controller with fuzzy-based parameter adaptation demonstrated advantages over the previously mentioned algorithms—especially when compensating the deviations of the SGR. These deviations usually occur when the equipment malfunctions or process disturbances take place. The fuzzy-based control system was stable within the investigated ranges. It was determined that, regarding control quality, the investigated control algorithms are suited to control the SGR in a fed-batch biotechnological process. However, substrate feeding rate manipulation and limitation needs to be used. Taking into account the time needed to design and tune the controller, the developed controller is suitable for practical applications when expert knowledge is available. The proposed algorithm can be further adapted and developed to control the SGR in other cell cultivations while running the process under substrate limitation conditions.
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24
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Horvat M, Winkler M. In Vivo
Reduction of Medium‐ to Long‐Chain Fatty Acids by Carboxylic Acid Reductase (CAR) Enzymes: Limitations and Solutions. ChemCatChem 2020. [DOI: 10.1002/cctc.202000895] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Melissa Horvat
- acib –Austrian Center of Industrial Biotechnology Krenngasse 37 8010 Graz Austria
| | - Margit Winkler
- acib –Austrian Center of Industrial Biotechnology Krenngasse 37 8010 Graz Austria
- Institute of Molecular Biotechnology Graz University of Technology Petersgasse 14 8010 Graz Austria
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25
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Insights from Mathematical Modelling into Process Control of Oxygen Transfer in Batch Stirred Tank Bioreactors for Reducing Energy Requirement. CHEMENGINEERING 2020. [DOI: 10.3390/chemengineering4020034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Significant energy savings can be made in aerobic stirred tank batch bioreactors by the manipulation of agitator power (Pag) and air flowrate per unit working volume (vvm). Control is often implemented to maintain the oxygen concentration in the bioreaction liquid (COL) at a constant value. This work used model simulations to show that controlling the Pag and vvm continuously over time, such that it is operated at or near the impeller flooding constraint results in the minimum energy requirement for oxygen transfer (strategy Cmin); however, this might prove impractical to control and operate in practice. As an alternative, the work shows that dividing the bioreaction time into a small number of constant Pag time segments (5–10), where a PID controller is used to control vvm to maintain COL constant in each segment, can achieve much of the energy saving that is associated with Cmin. During each time segment, vvm is increased and a sudden decrease in COL is used to detect the onset of flooding, after which there is a step increase in Pag. This sequence of Pag step increases continues until the bioreaction is completed. This practical control approach was shown to save most of the energy that is associated with Cmin.
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26
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Treloar NJ, Fedorec AJH, Ingalls B, Barnes CP. Deep reinforcement learning for the control of microbial co-cultures in bioreactors. PLoS Comput Biol 2020; 16:e1007783. [PMID: 32275710 PMCID: PMC7176278 DOI: 10.1371/journal.pcbi.1007783] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 04/22/2020] [Accepted: 03/10/2020] [Indexed: 01/01/2023] Open
Abstract
Multi-species microbial communities are widespread in natural ecosystems. When employed for biomanufacturing, engineered synthetic communities have shown increased productivity in comparison with monocultures and allow for the reduction of metabolic load by compartmentalising bioprocesses between multiple sub-populations. Despite these benefits, co-cultures are rarely used in practice because control over the constituent species of an assembled community has proven challenging. Here we demonstrate, in silico, the efficacy of an approach from artificial intelligence-reinforcement learning-for the control of co-cultures within continuous bioreactors. We confirm that feedback via a trained reinforcement learning agent can be used to maintain populations at target levels, and that model-free performance with bang-bang control can outperform a traditional proportional integral controller with continuous control, when faced with infrequent sampling. Further, we demonstrate that a satisfactory control policy can be learned in one twenty-four hour experiment by running five bioreactors in parallel. Finally, we show that reinforcement learning can directly optimise the output of a co-culture bioprocess. Overall, reinforcement learning is a promising technique for the control of microbial communities.
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Affiliation(s)
- Neythen J. Treloar
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
| | - Alex J. H. Fedorec
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
| | - Brian Ingalls
- Department of Applied Mathematics, University of Waterloo, Waterloo, Canada
| | - Chris P. Barnes
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
- UCL Genetics Institute, University College London, London, United Kingdom
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27
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The high-efficient production of phelligridin LA by Inonotus baumii with an integrated fermentation-separation process. Bioprocess Biosyst Eng 2020; 43:1141-1151. [PMID: 32078046 DOI: 10.1007/s00449-020-02310-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 02/09/2020] [Indexed: 10/25/2022]
Abstract
The phelligridin LA was one of the valuable metabolites synthesized by the medicinal fungus Sanghuang in liquid fermentation. In the improvement of PLA productivity by fermentation, we investigated the optimal conditions for the efficient separation of PLA from the fermentation broth with a chromatographic column packed with the macroporous resin ADS-17. Based on the findings, we further developed an integrated bioreactor system that coupled the fermentation and separation of PLA. Fermentation experiments with the bioreactor system testified the performance of our design in fortification of the PLA production: an improvement of PLA production by 2.14 folds was successfully achieved due to the prompt removal of the PLA, while the formation of hyphae biomass was not affected. Also, the integrated system could afford a simultaneous purification of PLA to a purity of 92.95% with a recovery of 84.3%, which was comparable to that of the PLA purified with an additional process (97.53%), at a reasonable recovery. This study provided a feasible approach for the improved production of PLA by fermentation. Besides, the design of the integrated bioreactor system offered a useful reference for the fermentation process development of fungi for the production of diverse valuable metabolites.
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28
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Wang B, Wang Z, Chen T, Zhao X. Development of Novel Bioreactor Control Systems Based on Smart Sensors and Actuators. Front Bioeng Biotechnol 2020; 8:7. [PMID: 32117906 PMCID: PMC7011095 DOI: 10.3389/fbioe.2020.00007] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Accepted: 01/07/2020] [Indexed: 01/15/2023] Open
Abstract
Bioreactors of various forms have been widely used in environmental protection, healthcare, industrial biotechnology, and space exploration. Robust demand in the field stimulated the development of novel designs of bioreactor geometries and process control strategies and the evolution of the physical structure of the control system. After the introduction of digital computers to bioreactor process control, a hierarchical structure control system (HSCS) for bioreactors has become the dominant physical structure, having high efficiency and robustness. However, inherent drawbacks of the HSCS for bioreactors have produced a need for a more consolidated solution of the control system. With the fast progress in sensors, machinery, and information technology, the development of a flat organizational control system (FOCS) for bioreactors based on parallel distributed smart sensors and actuators may provide a more concise solution for process control in bioreactors. Here, we review the evolution of the physical structure of bioreactor control systems and discuss the properties of the novel FOCS for bioreactors and related smart sensors and actuators and their application circumstances, with the hope of further improving the efficiency, robustness, and economics of bioprocess control.
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Affiliation(s)
- Baowei Wang
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, China
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (MOE), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
| | - Zhiwen Wang
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, China
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (MOE), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
| | - Tao Chen
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, China
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (MOE), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
| | - Xueming Zhao
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, China
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (MOE), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
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29
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Urniezius R, Survyla A. Identification of Functional Bioprocess Model for Recombinant E. Coli Cultivation Process. ENTROPY 2019. [PMCID: PMC7514566 DOI: 10.3390/e21121221] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The purpose of this study is to introduce an improved Luedeking–Piret model that represents a structurally simple biomass concentration approach. The developed routine provides acceptable accuracy when fitting experimental data that incorporate the target protein concentration of Escherichia coli culture BL21 (DE3) pET28a in fed-batch processes. This paper presents system identification, biomass, and product parameter fitting routines, starting from their roots of origin to the entropy-related development, characterized by robustness and simplicity. A single tuning coefficient allows for the selection of an optimization criterion that serves equally well for higher and lower biomass concentrations. The idea of the paper is to demonstrate that the use of fundamental knowledge can make the general model more common for technological use compared to a sophisticated artificial neural network. Experimental validation of the proposed model involved data analysis of six cultivation experiments compared to 19 experiments used for model fitting and parameter estimation.
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30
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Urniezius R, Survyla A, Paulauskas D, Bumelis VA, Galvanauskas V. Generic estimator of biomass concentration for Escherichia coli and Saccharomyces cerevisiae fed-batch cultures based on cumulative oxygen consumption rate. Microb Cell Fact 2019; 18:190. [PMID: 31690339 PMCID: PMC6833213 DOI: 10.1186/s12934-019-1241-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 10/23/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The focus of this study is online estimation of biomass concentration in fed-batch cultures. It describes a bioengineering software solution, which is explored for Escherichia coli and Saccharomyces cerevisiae fed-batch cultures. The experimental investigation of both cultures presents experimental validation results since the start of the bioprocess, i.e. since the injection of inoculant solution into bioreactor. In total, four strains were analyzed, and 21 experiments were performed under varying bioprocess conditions, out of which 7 experiments were carried out with dosed substrate feeding. Development of the microorganisms' culture invariant generic estimator of biomass concentration was the main goal of this research. RESULTS The results show that stoichiometric parameters provide acceptable knowledge on the state of biomass concentrations during the whole cultivation process, including the exponential growth phase of both E. coli and S. cerevisiae cultures. The cell culture stoichiometric parameters are estimated by a procedure based on the Luedeking/Piret-model and maximization of entropy. The main input signal of the approach is cumulative oxygen uptake rate at fed-batch cultivation processes. The developed noninvasive biomass estimation procedure was intentionally made to not depend on the selection of corresponding bioprocess/bioreactor parameters. CONCLUSIONS The precision errors, since the bioprocess start, when inoculant was injected to a bioreactor, confirmed that the approach is relevant for online biomass state estimation. This included the lag and exponential growth phases for both E. coli and S. cerevisiae. The suggested estimation procedure is identical for both cultures. This approach improves the precision achieved by other authors without compromising the simplicity of the implementation. Moreover, the suggested approach is a candidate method to be the microorganisms' culture invariant approach. It does not depend on any numeric initial optimization conditions, it does not require any of bioreactor parameters. No numeric stability issues of convergence occurred during multiple performance tests. All this makes this approach a potential candidate for industrial tasks with adaptive feeding control or automatic inoculations when substrate feeding profile and bioreactor parameters are not provided.
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Affiliation(s)
- Renaldas Urniezius
- Department of Automation, Kaunas University of Technology, 51367, Kaunas, Lithuania.
| | - Arnas Survyla
- Department of Automation, Kaunas University of Technology, 51367, Kaunas, Lithuania
| | - Dziugas Paulauskas
- Biopharmaceutical Division of Centre for Innovative Medicine, 08406, Vilnius, Lithuania
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31
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Adaptive Control of Biomass Specific Growth Rate in Fed-Batch Biotechnological Processes. A Comparative Study. Processes (Basel) 2019. [DOI: 10.3390/pr7110810] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This article presents a comparative study on the development and application of two distinct adaptive control algorithms for biomass specific growth rate control in fed-batch biotechnological processes. A typical fed-batch process using Escherichia coli for recombinant protein production was selected for this research. Numerical simulation results show that both developed controllers, an adaptive PI controller based on the gain scheduling technique and a model-free adaptive controller based on the artificial neural network, delivered a comparable control performance and are suitable for application when using the substrate limitation approach and substrate feeding rate manipulation. The controller performance was tested within the realistic ranges of the feedback signal sampling intervals and measurement noise intensities. Considering the efforts for controller design and tuning, including development of the adaptation/learning algorithms, the model-free adaptive control algorithm proves to be more attractive for industrial applications, especially when only limited knowledge of the process and its mathematical model is available. The investigated model-free adaptive controller also tended to deliver better control quality under low specific growth rate conditions that prevail during the recombinant protein production phase. In the investigated simulation runs, the average tracking error did not exceed 0.01 (1/h). The temporary overshoots caused by the maximal disturbances stayed within the range of 0.025–0.11 (1/h). Application of the algorithm can be further extended to specific growth rate control in other bacterial and mammalian cell cultivations that run under substrate limitation conditions.
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32
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Practical Solutions for Specific Growth Rate Control Systems in Industrial Bioreactors. Processes (Basel) 2019. [DOI: 10.3390/pr7100693] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This contribution discusses the main challenges related to successful application of automatic control systems used to control specific growth rate in industrial biotechnological processes. It is emphasized that, after the implementation of basic automatic control systems, primary attention shall be paid to the specific growth rate control systems because this process variable critically affects the physiological state of microbial cultures and the formation of the desired product. Therefore, control of the specific growth rate enables improvement of the quality and reproducibility of the biotechnological processes. The main requirements have been formulated that shall be met to successfully implement the specific growth rate control systems in industrial bioreactors. The relatively easy-to-implement schemes of specific growth rate control systems have been reviewed and discussed. The recommendations for selection of particular control systems for specific biotechnological processes have been provided.
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Narayanan H, Luna MF, Stosch M, Cruz Bournazou MN, Polotti G, Morbidelli M, Butté A, Sokolov M. Bioprocessing in the Digital Age: The Role of Process Models. Biotechnol J 2019; 15:e1900172. [DOI: 10.1002/biot.201900172] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 07/15/2019] [Indexed: 12/20/2022]
Affiliation(s)
- Harini Narayanan
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
| | - Martin F. Luna
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
| | | | - Mariano Nicolas Cruz Bournazou
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| | - Gianmarco Polotti
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| | - Massimo Morbidelli
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| | - Alessandro Butté
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| | - Michael Sokolov
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
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34
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A hierarchical state estimation and control framework for monitoring and dissolved oxygen regulation in bioprocesses. Bioprocess Biosyst Eng 2019; 42:1467-1481. [DOI: 10.1007/s00449-019-02143-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 05/02/2019] [Indexed: 12/20/2022]
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35
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Dutta D, Saini S. Phenomenological models as effective tools to discover cellular design principles. Arch Microbiol 2019; 201:283-293. [PMID: 30826848 DOI: 10.1007/s00203-019-01641-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 02/20/2019] [Accepted: 02/21/2019] [Indexed: 11/28/2022]
Abstract
Microbes have proved useful to us in many different ways. To utilize microbes, we have mostly focused on maximizing growth, to improve yield of chemicals derived from the microbes. However, to truly tap into their potential, we should also aim to understand microbial physiology. We present a historical perspective of the developments in the field of Microbial Biotechnology, focusing on how the growth-modelling approaches have changed. Starting from simple empirical growth models, we have evolved towards mechanistic and phenomenological models which use molecular and physiological details to drastically improve prediction power of these models. Lastly, we explore the as of yet unsolved questions in microbial physiology, and discuss how the ability to monitor microbial growth at single cell resolution using the lab-on-a-chip technologies is uncovering previously unobservable causal principles underlying microbial growth.
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Affiliation(s)
- Dibyendu Dutta
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Supreet Saini
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India.
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Urniezius R, Galvanauskas V, Survyla A, Simutis R, Levisauskas D. From Physics to Bioengineering: Microbial Cultivation Process Design and Feeding Rate Control Based on Relative Entropy Using Nuisance Time. ENTROPY 2018; 20:e20100779. [PMID: 33265867 PMCID: PMC7512341 DOI: 10.3390/e20100779] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 10/08/2018] [Accepted: 10/10/2018] [Indexed: 11/16/2022]
Abstract
For historic reasons, industrial knowledge of reproducibility and restrictions imposed by regulations, open-loop feeding control approaches dominate in industrial fed-batch cultivation processes. In this study, a generic gray box biomass modeling procedure uses relative entropy as a key to approach the posterior similarly to how prior distribution approaches the posterior distribution by the multivariate path of Lagrange multipliers, for which a description of a nuisance time is introduced. The ultimate purpose of this study was to develop a numerical semi-global convex optimization procedure that is dedicated to the calculation of feeding rate time profiles during the fed-batch cultivation processes. The proposed numerical semi-global convex optimization of relative entropy is neither restricted to the gray box model nor to the bioengineering application. From the bioengineering application perspective, the proposed bioprocess design technique has benefits for both the regular feed-forward control and the advanced adaptive control systems, in which the model for biomass growth prediction is compulsory. After identification of the gray box model parameters, the options and alternatives in controllable industrial biotechnological processes are described. The main aim of this work is to achieve high reproducibility, controllability, and desired process performance. Glucose concentration measurements, which were used for the development of the model, become unnecessary for the development of the desired microbial cultivation process.
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Fulazzaky MA, Nuid M, Aris A, Muda K. Mass transfer kinetics of biosorption of nitrogenous matter from palm oil mill effluent by aerobic granules in sequencing batch reactor. ENVIRONMENTAL TECHNOLOGY 2018; 39:2151-2161. [PMID: 28675960 DOI: 10.1080/09593330.2017.1351494] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 07/01/2017] [Indexed: 06/07/2023]
Abstract
Understanding of mass transfer kinetics is important for biosorption of nitrogen compounds from palm oil mill effluent (POME) to gain a mechanistic insight into future biological processes for the treatment of high organic loading wastewater. In this study, the rates of global and sequential mass transfer were determined using the modified mass transfer factor equations for the experiments to remove nitrogen by aerobic granular sludge accumulation in a sequencing batch reactor (SBR). The maximum efficiencies as high as 97% for the experiment run at [kLa]g value of 1421.8 h-1 and 96% for the experiment run at [kLa]g value of 9.6 × 1037 h-1 were verified before and after the addition of Serratia marcescens SA30, respectively. The resistance of mass transfer could be dependent on external mass transfer that controls the transport of nitrogen molecule along the experimental period of 256 days. The increase in [kLa]g value leading to increased performance of the SBR was verified to contribute to the future applications of the SBR because this phenomenon provides new insight into the dynamic response of biological processes to treat POME.
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Affiliation(s)
- Mohamad Ali Fulazzaky
- a Directorate General of Water Resources, Ministry of Public Works and Housing , Jakarta , Indonesia
- b Islamic Science Research Network , Muhammadiyah University of Hamka , Jakarta , Indonesia
- c Centre for Environmental Sustainability and Water Security , Research Institute for Sustainable Environment, Universiti Teknologi Malaysia , Johor Bahru , Malaysia
| | - Maria Nuid
- c Centre for Environmental Sustainability and Water Security , Research Institute for Sustainable Environment, Universiti Teknologi Malaysia , Johor Bahru , Malaysia
- d Department of Environmental Engineering, Faculty of Civil Engineering , Universiti Teknologi Malaysia , Johor Bahru , Malaysia
| | - Azmi Aris
- c Centre for Environmental Sustainability and Water Security , Research Institute for Sustainable Environment, Universiti Teknologi Malaysia , Johor Bahru , Malaysia
| | - Khalida Muda
- d Department of Environmental Engineering, Faculty of Civil Engineering , Universiti Teknologi Malaysia , Johor Bahru , Malaysia
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Pantano MN, Fernández MC, Serrano ME, Ortiz OA, Scaglia GJE. Tracking Control of Optimal Profiles in a Nonlinear Fed-Batch Bioprocess under Parametric Uncertainty and Process Disturbances. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b01791] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- María N. Pantano
- Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Avenida Libertador San Martín (O) 1109, San Juan J5400ARL, Argentina
| | - María C. Fernández
- Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Avenida Libertador San Martín (O) 1109, San Juan J5400ARL, Argentina
| | - Mario E. Serrano
- Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Avenida Libertador San Martín (O) 1109, San Juan J5400ARL, Argentina
| | - Oscar A. Ortiz
- Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Avenida Libertador San Martín (O) 1109, San Juan J5400ARL, Argentina
| | - Gustavo J. E. Scaglia
- Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Avenida Libertador San Martín (O) 1109, San Juan J5400ARL, Argentina
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Nair R, Santosh W, Seetharaman B. Enhanced Biosynthesis of Laccase and Concomitant Degradation of 2, 3-Dichlorodibenzo-p-Dioxin by Pleurotus florid. ACTA ACUST UNITED AC 2018. [DOI: 10.17485/ijst/2018/v11i25/126630] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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Puvendran K, Anupama K, Jayaraman G. Real-time monitoring of hyaluronic acid fermentation by in situ transflectance spectroscopy. Appl Microbiol Biotechnol 2018; 102:2659-2669. [DOI: 10.1007/s00253-018-8816-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 01/23/2018] [Accepted: 01/27/2018] [Indexed: 01/22/2023]
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Simutis R, Lübbert A. Hybrid Approach to State Estimation for Bioprocess Control. Bioengineering (Basel) 2017; 4:bioengineering4010021. [PMID: 28952500 PMCID: PMC5590450 DOI: 10.3390/bioengineering4010021] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Revised: 02/27/2017] [Accepted: 03/02/2017] [Indexed: 11/21/2022] Open
Abstract
An improved state estimation technique for bioprocess control applications is proposed where a hybrid version of the Unscented Kalman Filter (UKF) is employed. The underlying dynamic system model is formulated as a conventional system of ordinary differential equations based on the mass balances of the state variables biomass, substrate, and product, while the observation model, describing the less established relationship between the state variables and the measurement quantities, is formulated in a data driven way. The latter is formulated by means of a support vector regression (SVR) model. The UKF is applied to a recombinant therapeutic protein production process using Escherichia coli bacteria. Additionally, the state vector was extended by the specific biomass growth rate µ in order to allow for the estimation of this key variable which is crucial for the implementation of innovative control algorithms in recombinant therapeutic protein production processes. The state estimates depict a sufficiently low noise level which goes perfectly with different advanced bioprocess control applications.
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Affiliation(s)
- Rimvydas Simutis
- Department of Automation, Kaunas University of Technology, Kaunas 44249, Lithuania.
| | - Andreas Lübbert
- Department of Biochemie/Biotechnologie, Martin-Luther-Universität Halle-Wittenberg, 06108 Halle, Germany.
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Cerminati S, Eberhardt F, Elena CE, Peirú S, Castelli ME, Menzella HG. Development of a highly efficient oil degumming process using a novel phosphatidylinositol-specific phospholipase C enzyme. Appl Microbiol Biotechnol 2017; 101:4471-4479. [PMID: 28238084 DOI: 10.1007/s00253-017-8201-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 01/31/2017] [Accepted: 02/14/2017] [Indexed: 11/27/2022]
Abstract
Enzymatic degumming using phospholipase C (PLC) enzymes may be used in environmentally friendly processes with improved oil recovery yields. In this work, phosphatidylinositol-specific phospholipase C (PIPLC) candidates obtained from an in silico analysis were evaluated for oil degumming. A PIPLC from Lysinibacillus sphaericus was shown to efficiently remove phosphatidylinositol from crude oil, and when combined with a second phosphatidylcholine and phosphatidylethanolamine-specific phospholipase C, the three major phospholipids were completely hydrolyzed, providing an extra yield of oil greater than 2.1%, compared to standard methods. A remarkably efficient fed-batch Escherichia coli fermentation process producing ∼14 g/L of the recombinant PIPLC enzyme was developed, which may facilitate the adoption of this cost-effective oil-refining process.
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Affiliation(s)
- Sebastián Cerminati
- CONICET y Departamento de Tecnología, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario (UNR), Instituto de Procesos Biotecnológicos y Químicos (IPROBYQ), Suipacha 531, 2000, Rosario, Argentina
| | - Florencia Eberhardt
- CONICET y Departamento de Tecnología, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario (UNR), Instituto de Procesos Biotecnológicos y Químicos (IPROBYQ), Suipacha 531, 2000, Rosario, Argentina
| | | | - Salvador Peirú
- CONICET y Departamento de Tecnología, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario (UNR), Instituto de Procesos Biotecnológicos y Químicos (IPROBYQ), Suipacha 531, 2000, Rosario, Argentina.,Keclon S.A., Tucumán 7180, 2000, Rosario, Argentina
| | - María E Castelli
- CONICET y Departamento de Tecnología, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario (UNR), Instituto de Procesos Biotecnológicos y Químicos (IPROBYQ), Suipacha 531, 2000, Rosario, Argentina.,Keclon S.A., Tucumán 7180, 2000, Rosario, Argentina
| | - Hugo G Menzella
- CONICET y Departamento de Tecnología, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario (UNR), Instituto de Procesos Biotecnológicos y Químicos (IPROBYQ), Suipacha 531, 2000, Rosario, Argentina. .,Keclon S.A., Tucumán 7180, 2000, Rosario, Argentina.
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Galvanauskas V, Grincas V, Simutis R, Kagawa Y, Kino-oka M. Current state and perspectives in modeling and control of human pluripotent stem cell expansion processes in stirred-tank bioreactors. Biotechnol Prog 2017; 33:355-364. [DOI: 10.1002/btpr.2431] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Revised: 12/10/2016] [Indexed: 01/02/2023]
Affiliation(s)
| | - Vykantas Grincas
- Department of Automation; Kaunas University of Technology; Kaunas Lithuania
| | - Rimvydas Simutis
- Department of Automation; Kaunas University of Technology; Kaunas Lithuania
| | - Yuki Kagawa
- Department of Biotechnology; Osaka University; Osaka Japan
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Ferreira G, Jacques P. Editorial: Bioengineering for a better quality of life. Biotechnol J 2015; 10:1093-4. [DOI: 10.1002/biot.201500419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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