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Journal articles on the topic 'Bioprocesses'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Aranda-Barradas, Juan Silvestre, Claudia Guerrero-Barajas, and Alberto Ordaz. "Addressing Challenges in Large-Scale Bioprocess Simulations: A Circular Economy Approach Using SuperPro Designer." Processes 13, no. 7 (2025): 2259. https://doi.org/10.3390/pr13072259.

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Bioprocess simulation is a powerful tool for leveraging circular economy principles in the analysis of large-scale bioprocesses, enhancing decision-making for efficient and sustainable production. By simulating different process scenarios, researchers and engineers can evaluate the techno-economic feasibility of different approaches. This approach enables the identification of cost-effective and sustainable solutions, optimizing resource use and minimizing waste, thereby enhancing the overall efficiency and viability of bioprocesses within a circular economy framework. In this review, we provi

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2

Nimmegeers, Philippe, Dominique Vercammen, Satyajeet Bhonsale, Filip Logist, and Jan Van Impe. "Metabolic Reaction Network-Based Model Predictive Control of Bioprocesses." Applied Sciences 11, no. 20 (2021): 9532. http://dx.doi.org/10.3390/app11209532.

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Bioprocesses are increasingly used for the production of high added value products. Microorganisms are used in bioprocesses to mediate or catalyze the necessary reactions. This makes bioprocesses highly nonlinear and the governing mechanisms are complex. These complex governing mechanisms can be modeled by a metabolic network that comprises all interactions within the cells of the microbial population present in the bioprocess. The current state of the art in bioprocess control is model predictive control based on the use of macroscopic models, solely accounting for substrate, biomass, and pro

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3

Nimmegeers, Philippe, Dominique Vercammen, Satyajeet Bhonsale, Filip Logist, and Jan Van Impe. "Metabolic Reaction Network-Based Model Predictive Control of Bioprocesses." Applied Sciences 11, no. 20 (2021): 9532. http://dx.doi.org/10.3390/app11209532.

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Bioprocesses are increasingly used for the production of high added value products. Microorganisms are used in bioprocesses to mediate or catalyze the necessary reactions. This makes bioprocesses highly nonlinear and the governing mechanisms are complex. These complex governing mechanisms can be modeled by a metabolic network that comprises all interactions within the cells of the microbial population present in the bioprocess. The current state of the art in bioprocess control is model predictive control based on the use of macroscopic models, solely accounting for substrate, biomass, and pro

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4

Salvador, William O. S., Inês A. B. Ribeiro, Diogo E. S. Nogueira, Frederico C. Ferreira, Joaquim M. S. Cabral, and Carlos A. V. Rodrigues. "Bioprocess Economic Modeling: Decision Support Tools for the Development of Stem Cell Therapy Products." Bioengineering 9, no. 12 (2022): 791. http://dx.doi.org/10.3390/bioengineering9120791.

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Over recent years, the field of cell and gene therapy has witnessed rapid growth due to the demonstrated benefits of using living cells as therapeutic agents in a broad range of clinical studies and trials. Bioprocess economic models (BEMs) are fundamental tools for guiding decision-making in bioprocess design, being capable of supporting process optimization and helping to reduce production costs. These tools are particularly important when it comes to guiding manufacturing decisions and increasing the likelihood of market acceptance of cell-based therapies, which are often cost-prohibitive b

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5

Theuer, Lorenz, Judit Randek, Stefan Junne, Peter Neubauer, Carl-Fredrik Mandenius, and Valerio Beni. "Single-Use Printed Biosensor for L-Lactate and Its Application in Bioprocess Monitoring." Processes 8, no. 3 (2020): 321. http://dx.doi.org/10.3390/pr8030321.

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There is a profound need in bioprocess manufacturing for low-cost single-use sensors that allow timely monitoring of critical product and production attributes. One such opportunity is screen-printed enzyme-based electrochemical sensors, which have the potential to enable low-cost online and/or off-line monitoring of specific parameters in bioprocesses. In this study, such a single-use electrochemical biosensor for lactate monitoring is designed and evaluated. Several aspects of its fabrication and use are addressed, including enzyme immobilization, stability, shelf-life and reproducibility. A

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6

Barragán-Ocaña, Alejandro, Paz Silva-Borjas, Samuel Olmos-Peña, and Mirtza Polanco-Olguín. "Biotechnology and Bioprocesses: Their Contribution to Sustainability." Processes 8, no. 4 (2020): 436. http://dx.doi.org/10.3390/pr8040436.

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Significant advancements in biotechnology have resulted in the development of numerous fundamental bioprocesses, which have consolidated research and development and industrial progress in the field. These bioprocesses are used in medical therapies, diagnostic and immunization procedures, agriculture, food production, biofuel production, and environmental solutions (to address water-, soil-, and air-related problems), among other areas. The present study is a first approach toward the identification of scientific and technological bioprocess trajectories within the framework of sustainability.

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7

Lyubenova, Velislava, Maya Ignatova, Denitsa Kristeva, and Olympia Roeva. "Multistep Modelling and Monitoring of Bioprocesses." International Journal Bioautomation 28, no. 4 (2024): 185–96. https://doi.org/10.7546/ijba.2024.28.4.001033.

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A new approach is proposed for modelling and monitoring bioprocesses dynamics characterized by different metabolic states. Bioprocesses cannot be described by a single model. For this reason, three phases characterised by the bioprocess are defined – periodic, exponential, and stationary. During each phase, the process passes through one or more physiological states. Each physiological state is described by a sub-model with a different structure and parameter values. The transition of the process from one physiological state to another is carried out by switching the sub-models based on a pred

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8

ERMURAT, Yakup. "Bioprocesses modeling of acidolysis and redoxolysis activities of ferric and ferrous iron by Saccharomyces cerevisiae and Acetobacter aceti." Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 6, no. 3 (2023): 2046–62. http://dx.doi.org/10.47495/okufbed.1126692.

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The acidolysis and redoxolysis reactions regulate the oxidation and reduction of ferric [Fe^(+3) ] and ferrous iron [Fe^(+2) ] which are vital for living organisms. Bioprocesses modeling of the acidolysis along with redoxolysis activities of ferric to ferrous iron [(Fe^(+3))/(Fe^(+2) )] by Saccharomyces cerevisiae and Acetobacter aceti was studied. The bioprocess experiments were carried out at different temperatures of 25oC, 30oC and 35oC for eight weeks. Glucose, ascorbic acid, acetic acid, ethyl alcohol and vinegar were used in incubation media as substrate and acidic purposes. The [(Fe^(+3

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9

Schügerl, Karl, and Jürgen Hubbuch. "Integrated bioprocesses." Current Opinion in Microbiology 8, no. 3 (2005): 294–300. http://dx.doi.org/10.1016/j.mib.2005.01.002.

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10

Roman, Monica, and Dan Selişteanu. "Enzymatic Synthesis of Ampicillin: Nonlinear Modeling, Kinetics Estimation, and Adaptive Control." Journal of Biomedicine and Biotechnology 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/512691.

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Nowadays, the use of advanced control strategies in biotechnology is quite low. A main reason is the lack of quality of the data, and the fact that more sophisticated control strategies must be based on a model of the dynamics of bioprocesses. The nonlinearity of the bioprocesses and the absence of cheap and reliable instrumentation require an enhanced modeling effort and identification strategies for the kinetics. The present work approaches modeling and control strategies for the enzymatic synthesis of ampicillin that is carried out inside a fed-batch bioreactor. First, a nonlinear dynamical

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11

English, Niall. "Nanobubbles and microbubbles: A fresh and economic appraisal to redesigning liquids’ life-line to bioprocesses." Open Access Government 45, no. 1 (2025): 390–91. https://doi.org/10.56367/oag-045-11345.

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Fine-bubble engineering: A fresh and economic appraisal to redesigning liquids’ life-line to bioprocesses Niall J. English, from Chemical & Bioprocess Engineering at University College Dublin, discusses how new paradigms in manipulating microbubble and nanobubble populations by novel and inventive engineering approaches have an impact on tailoring liquids for various bioprocess applications. A fundamental challenge in many unit operations in (broadly defined) biochemical engineering (such as activated-sludge processes in wastewater treatment), as well as in environmental settings (e.g., su

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12

Rendón-Castrillón, Leidy, Margarita Ramírez-Carmona, Carlos Ocampo-López, and Luis Gómez-Arroyave. "Mathematical Model for Scaling up Bioprocesses Using Experiment Design Combined with Buckingham Pi Theorem." Applied Sciences 11, no. 23 (2021): 11338. http://dx.doi.org/10.3390/app112311338.

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Scaling up bioprocesses from the experimental to the pilot or industrial scale involves heuristics and scale relationships that are far from the specific phenomena and are usually not connected to the experimental data. In complex systems, the scaling-up methodology must connect the experimental data with the tools of engineering design. In this work, a two-stage gold bioleaching process was used as a case study to develop a mathematical model of bioprocess scaling that combines the design of experiments with dimensional analysis using the Buckingham Pi theorem to formulate a predictive model

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13

Van Brunt, Jennifer. "Biosensors for Bioprocesses." Nature Biotechnology 5, no. 5 (1987): 437–40. http://dx.doi.org/10.1038/nbt0587-437.

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14

Caramihai, Mihai, and Irina Severin. "Intelligent Techniques for Fed-Batch Bioprocess Control." Key Engineering Materials 467-469 (February 2011): 1478–81. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.1478.

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Bioprocesses are appreciated as difficult to control because their dynamic behavior is highly nonlinear and time varying, in particular, when they are operating in fed batch mode. For this kind of bioprocess where the mathematical model contains many structured and unstructured uncertainties, we try to combine different intelligent techniques based on natural syllogisms of these techniques. In order to obtain a high bioprocess productivity it is essential to accord the benefits of the classical control strategy (i.e. the analytical determination of the optimum) with the subjective bioprocess c

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15

Lange, Julian, Ralf Takors, and Bastian Blombach. "Zero-growth bioprocesses: A challenge for microbial production strains and bioprocess engineering." Engineering in Life Sciences 17, no. 1 (2016): 27–35. http://dx.doi.org/10.1002/elsc.201600108.

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16

Dughan, Louise. "Speeding Bioscience to Bioprocesses." Nature Biotechnology 12, no. 7 (1994): 668–69. http://dx.doi.org/10.1038/nbt0795-668.

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17

Shimizu, Yasutoshi. "Ceramic membranes for bioprocesses." membrane 15, no. 4 (1990): 179–87. http://dx.doi.org/10.5360/membrane.15.179.

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18

HORIUCHI, JUN'ICHI. "Fuzzy control in bioprocesses." Kagaku To Seibutsu 37, no. 8 (1999): 521–25. http://dx.doi.org/10.1271/kagakutoseibutsu1962.37.521.

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19

Vaidyanathan, Seetharaman, Graeme Macaloney, Jacqueline Vaughan, Brian McNeil, and Linda M. Harvey. "Monitoring of Submerged Bioprocesses." Critical Reviews in Biotechnology 19, no. 4 (1999): 277–316. http://dx.doi.org/10.1080/0738-859991229161.

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20

Bogaerts, Ph, and A. Vande Wouwer. "Software sensors for bioprocesses." ISA Transactions 42, no. 4 (2003): 547–58. http://dx.doi.org/10.1016/s0019-0578(07)60005-6.

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21

Das, Keshav. "Dynamics of environmental bioprocesses." Agricultural Systems 54, no. 3 (1997): 427–28. http://dx.doi.org/10.1016/s0308-521x(97)84853-6.

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22

BONTE, PIERRE M. "The Automation of Bioprocesses." Annals of the New York Academy of Sciences 469, no. 1 Biochemical E (1986): 104–10. http://dx.doi.org/10.1111/j.1749-6632.1986.tb26489.x.

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23

Koumpouras, George, and Cleo Kontoravdi. "Dynamic Optimization of Bioprocesses." Applied Mathematics 03, no. 10 (2012): 1487–95. http://dx.doi.org/10.4236/am.2012.330208.

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24

Romero, G., J. M. Flaus, and A. Cheruy. "Semiqualitative modelling of bioprocesses." Mathematical Modelling of Systems 3, no. 3 (1997): 246–64. http://dx.doi.org/10.1080/13873959708837059.

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25

Salgó, András, and Szilveszter Gergely. "BME = Bioprocesses, Measurement, Evaluation." NIR news 23, no. 2 (2012): 6–8. http://dx.doi.org/10.1255/nirn.1291.

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26

Silva, Javier, Laura Arias-Torres, Carlos Carlesi, and Germán Aroca. "Use of Nanobubbles to Improve Mass Transfer in Bioprocesses." Processes 12, no. 6 (2024): 1227. http://dx.doi.org/10.3390/pr12061227.

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Nanobubble technology has emerged as a transformative approach in bioprocessing, significantly enhancing mass-transfer efficiency for effective microbial activity. Characterized by their nanometric size and high internal pressure, nanobubbles possess distinct properties such as prolonged stability and minimal rise velocities, allowing them to remain suspended in liquid media for extended periods. These features are particularly beneficial in bioprocesses involving aerobic strains, where they help overcome common obstacles, such as increased culture viscosity and diffusion limitations, that tra

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27

Lyubenova, V. "Monitoring the Kinetics of Bioprocess Variables – Theory and Applications." Information Technologies and Control 14, no. 1 (2016): 2–12. http://dx.doi.org/10.1515/itc-2016-0016.

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Abstract A new method for monitoring of bioprocess kinetics is developed where there exists a set of measured variables but the use of constant values of kinetic parameters is not acceptable. The method is based on new formalization of kinetics of biotechnological processes. It is generalized in one vector including unknown time-varying parameters only. For kinetics estimation, a general structure of software sensor is presented. A optimization procedure for tuning of the sensor’s parameters is proposed on the basis of stability and convergence analysis. The proposed method is applied for moni

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28

Srivastava, Rajesh K. "Yeast Species Mediated Bioprocesses and Bio-Products for Biotechnological Application." Journal of Biotechnology and Biomedical Science 2, no. 1 (2019): 1–11. http://dx.doi.org/10.14302/issn.2576-6694.jbbs-19-2684.

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Yeast as unicellular organism, has shown multiple application due to exhibition of noble ability in its cells. And engineered yeast has found more suitability in bioprocesses application as well as adverse conditions adaptation. Different types of yeast strains showed their best capability to adapt the salt and sugar rich environment with their optimal growth capability. These strains, used as suitable and novel cell factories for production of value added bio-products (via utilization of fermentation processes) and also for different types of bioprocesses. Application of yeast species in biot

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29

Knowle, R., A. Werner, and R. K. DeLong. "R4 Peptide-pDNA Nanoparticle Coated HepB Vaccine Microparticles: Sedimentation, Partitioning, and Spray Freeze Dry Bioprocesses." Journal of Nanoscience and Nanotechnology 6, no. 9 (2006): 2783–89. http://dx.doi.org/10.1166/jnn.2006.427.

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Broad therapeutic application of nucleic acid micro- and nanoparticles will require bioprocesses capable of achieving high loads of structurally intact and functionality active DNA. Here we report condensation of pDNA into nanoparticles by sedimentation through R4 peptide and partitioning at a hydrophobic interface. ≥90% coating efficiency onto microparticles is achieved via this combined bioprocess with the pDNA retaining 85–90% intact supercoil after bioprocessing. SEM analyses of the microparticles produced therefrom reveals bound pDNA and R4 peptide nanoparticles. HPLC and chemical analyse

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30

Arulrajah, Prasika, and Anna-Lena Heins. "Versteckte Vielfalt: Diversität von Zellen im industriellen Bioprozess." BIOspektrum 29, no. 3 (2023): 241–44. http://dx.doi.org/10.1007/s12268-023-1922-y.

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AbstractPopulation heterogeneity poses a major challenge in industrial scale bioprocesses, affecting process performance but also leading to more robust phenotypes. To investigate this phenomenon, multiple fluorescent reporter strains are cultivated under simulated industrial scale conditions in multi-compartment bioreactors. This approach significantly raises the quantitative understanding of population heterogenity which is needed before its benefits can help to develop high-yielding, more robust bioprocesses.

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31

Vahidinasab, Maliheh, Lisa Thewes, Bahar Abrishamchi, et al. "In Vivo Quantification of Surfactin Nonribosomal Peptide Synthetase Complexes in Bacillus subtilis." Microorganisms 12, no. 11 (2024): 2381. http://dx.doi.org/10.3390/microorganisms12112381.

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Surfactin, a potent biosurfactant produced by Bacillus subtilis, is synthesized using a non-ribosomal peptide synthetase (NRPS) encoded by the srfAA-AD operon. Despite its association with quorum sensing via the ComX pheromone, the dynamic behavior and in vivo quantification of the NRPS complex remain underexplored. This study established an in vivo quantification system using fluorescence labeling to monitor the availability of surfactin-forming NRPS subunits (SrfAA, SrfAB, SrfAC, and SrfAD) during bioprocesses. Four Bacillus subtilis sensor strains were constructed by fusing these subunits w

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32

Singh, Vishal Kumar, Ioscani Jiménez del Val, Jarka Glassey, and Fatemeh Kavousi. "Integration Approaches to Model Bioreactor Hydrodynamics and Cellular Kinetics for Advancing Bioprocess Optimisation." Bioengineering 11, no. 6 (2024): 546. http://dx.doi.org/10.3390/bioengineering11060546.

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Large-scale bioprocesses are increasing globally to cater to the larger market demands for biological products. As fermenter volumes increase, the efficiency of mixing decreases, and environmental gradients become more pronounced compared to smaller scales. Consequently, the cells experience gradients in process parameters, which in turn affects the efficiency and profitability of the process. Computational fluid dynamics (CFD) simulations are being widely embraced for their ability to simulate bioprocess performance, facilitate bioprocess upscaling, downsizing, and process optimisation. Recen

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33

Siegl, Manuel, Manuel Kämpf, Dominik Geier, et al. "Generalizability of Soft Sensors for Bioprocesses through Similarity Analysis and Phase-Dependent Recalibration." Sensors 23, no. 4 (2023): 2178. http://dx.doi.org/10.3390/s23042178.

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A soft sensor concept is typically developed and calibrated for individual bioprocesses in a time-consuming manual procedure. Following that, the prediction performance of these soft sensors degrades over time, due to changes in raw materials, biological variability, and modified process strategies. Through automatic adaptation and recalibration, adaptive soft sensor concepts have the potential to generalize soft sensor principles and make them applicable across bioprocesses. In this study, a new generalized adaptation algorithm for soft sensors is developed to provide phase-dependent recalibr

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34

Riascos, C. A. M., and J. M. Pinto. "Simultaneous optimization of dynamic bioprocesses." Brazilian Journal of Chemical Engineering 19, no. 4 (2002): 449–56. http://dx.doi.org/10.1590/s0104-66322002000400014.

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35

Bédoui, A., M. Farza, M. M'Saad, and M. Ksouri. "Robust nonlinear controllers for bioprocesses." IFAC Proceedings Volumes 41, no. 2 (2008): 15541–46. http://dx.doi.org/10.3182/20080706-5-kr-1001.02628.

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36

Pérez, Julio A. Romero, and José L. Navarro Herrero. "NON-LINEAR OBSERVER FOR BIOPROCESSES." IFAC Proceedings Volumes 35, no. 1 (2002): 425–30. http://dx.doi.org/10.3182/20020721-6-es-1901.01380.

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37

Ruenglertpanyakul, Wiwat, and Karl-heinz Bellgardt. "Physiological phase models for bioprocesses." IFAC Proceedings Volumes 31, no. 8 (1998): 113–16. http://dx.doi.org/10.1016/s1474-6670(17)40169-8.

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38

Xu, Gongxian. "Robust Control of Continuous Bioprocesses." Mathematical Problems in Engineering 2010 (2010): 1–18. http://dx.doi.org/10.1155/2010/627035.

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This paper deals with robust control of continuous bioprocesses. According to the material balance equations of continuous bioprocesses, a uniform framework for mathematical modeling of this class of processes is first presented. Then a robust controller is designed by using theH∞mixed sensitivity method for the biotechnology processes. The corresponding control objective is described as the development of a robust reference-tracking control structure with the best possible disturbance compensation, able to cope with variations in key process parameters. Finally, the proposed robust control st

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39

Guardia, Marı́a Jesus. "Modeling and control in bioprocesses." Trends in Biotechnology 20, no. 1 (2002): 43–44. http://dx.doi.org/10.1016/s0167-7799(01)01812-1.

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40

Scheper, T., B. Hitzmann, E. Stärk, et al. "Bioanalytics: detailed insight into bioprocesses." Analytica Chimica Acta 400, no. 1-3 (1999): 121–34. http://dx.doi.org/10.1016/s0003-2670(99)00612-1.

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41

Chase, Howard Allaker. "Rapid chromatographic monitoring of bioprocesses." Biosensors 2, no. 5 (1986): 269–86. http://dx.doi.org/10.1016/0265-928x(86)80007-4.

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42

Darling, Eric M., and Kyriacos A. Athanasiou. "Articular Cartilage Bioreactors and Bioprocesses." Tissue Engineering 9, no. 1 (2003): 9–26. http://dx.doi.org/10.1089/107632703762687492.

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43

Darling, Eric M., and Kyriacos A. Athanasiou. "Articular Cartilage Bioreactors and Bioprocesses." Tissue Engineering 9, no. 3 (2003): 565. http://dx.doi.org/10.1089/107632703322066750.

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44

Kwiatkowska, Barbara, Jason Bennett, Joe Akunna, Graeme M. Walker, and David H. Bremner. "Stimulation of bioprocesses by ultrasound." Biotechnology Advances 29, no. 6 (2011): 768–80. http://dx.doi.org/10.1016/j.biotechadv.2011.06.005.

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45

Mészáros, A., J. Mikleš, and P. Lednický. "Adaptive Control of Continuous Bioprocesses." IFAC Proceedings Volumes 27, no. 11 (1994): 251–56. http://dx.doi.org/10.1016/s1474-6670(17)47656-7.

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46

Kennes, Christian, Eldon R. Rene, and María C. Veiga. "Bioprocesses for air pollution control." Journal of Chemical Technology & Biotechnology 84, no. 10 (2009): 1419–36. http://dx.doi.org/10.1002/jctb.2216.

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47

Alhusban, Ala A., Michael C. Breadmore, and Rosanne M. Guijt. "Capillary electrophoresis for monitoring bioprocesses." ELECTROPHORESIS 34, no. 11 (2013): 1465–82. http://dx.doi.org/10.1002/elps.201200646.

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48

Fitzpatrick, John, Franck Gloanec, Elisa Michel, Johanna Blondy, and Anais Lauzeral. "Application of Mathematical Modelling to Reducing and Minimising Energy Requirement for Oxygen Transfer in Batch Stirred Tank Bioreactors." ChemEngineering 3, no. 1 (2019): 14. http://dx.doi.org/10.3390/chemengineering3010014.

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In this study, microbial kinetic and oxygen transfer modelling coupled with energy analysis was applied to investigate how manipulation and control of agitator power input and air flowrate can reduce and minimise the total energy requirement in a batch aerobic bioprocess subject to constraints. The study showed that major energy savings can be made by appropriate selection of these variables and how they are controlled throughout a bioprocess. In many bioprocesses, the oxygen concentration in the liquid is controlled at a constant value. This may be achieved by maintaining the agitator power a

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49

Ivušić, Franjo, Tonči Rezić, and Božidar Šantek. "Heterotrophic Cultivation of Euglena gracilis in Stirred Tank Bioreactor: A Promising Bioprocess for Sustainable Paramylon Production." Molecules 27, no. 18 (2022): 5866. http://dx.doi.org/10.3390/molecules27185866.

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Paramylon is a valuable intracellular product of the microalgae Euglena gracilis, and it can accumulate in Euglena cells according to the cultivation conditions. For the sustainable production of paramylon and appropriate cell growth, different bioreactor processes and industrial byproducts can be considered as substrates. In this study, a complex medium with corn steep solid (CSS) was used, and various bioreactor processes (batch, fed batch, semicontinuous and continuous) were performed in order to maximize paramylon production in the microalgae Euglena gracilis. Compared to the batch, fed ba

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50

Abbott, M. S. R., A. P. Harvey, G. Valente Perez, and M. K. Theodorou. "Biological processing in oscillatory baffled reactors: operation, advantages and potential." Interface Focus 3, no. 1 (2013): 20120036. http://dx.doi.org/10.1098/rsfs.2012.0036.

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The development of efficient and commercially viable bioprocesses is essential for reducing the need for fossil-derived products. Increasingly, pharmaceuticals, fuel, health products and precursor compounds for plastics are being synthesized using bioprocessing routes as opposed to more traditional chemical technologies. Production vessels or reactors are required for synthesis of crude product before downstream processing for extraction and purification. Reactors are operated either in discrete batches or, preferably, continuously in order to reduce waste, cost and energy. This review describ

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