Soft sensors in bioprocessing: a status report and recommendations

The following report with recommendations is the result of an expert panel meeting on soft sensor applications in bioprocess engineering that was organized by the Measurement, Monitoring, Modelling and Control (M3C) Working Group of the European Federation of Biotechnology - Section of Biochemical Engineering Science (ESBES). The aim of the panel was to provide an update on the present status of the subject and to identify critical needs and issues for the furthering of the successful development of soft sensor methods in bioprocess engineering research and for industrial applications, in particular with focus on biopharmaceutical applications. It concludes with a set of recommendations, which highlight current prospects for the extended use of soft sensors and those areas requiring development.     

Designing a blueprint for next-generation stem cell bioprocessing development

The creation of a blueprint for stem cell bioprocess development that it is easily readable and shareable among those involved in the construction of the bioprocess is a necessary step toward full-fledged bioprocess integration. The blueprint provides the culturing tools and methodologies, designed to highlight knowledge gaps within biological sciences and bioengineering. This review highlights a blueprint for stem cell bioprocessing development using a landscape architecture approach that can aid the development of culture technologies and tools that satisfy the demands for stem cell-derived products for use in clinical and industrial applications. This work is intended to provide insights to cell biologists, geneticists, bioengineers, and clinicians seeking knowledge outside of their field of expertise and fosters a leap from a reductionist approach to one, that is, globally integrated in stem cell bioprocessing.     

Prospective inference of bioprocess cell viability through chemometric modeling of fluorescence multiway data

In this investigation, the fermentation step of a standard mammalian cell-based industrial bioprocess for the production of a therapeutic protein was studied, with particular emphasis on the evolution of cell viability. This parameter constitutes one of the critical variables for bioprocess monitoring since it can affect downstream operations and the quality of the final product. In addition, when the cells experiment an unpredictable drop in viability, the assessment of this variable through classic off-line methods may not provide information sufficiently in advance to take corrective actions. In this context, Process Analytical Technology (PAT) framework aims to develop novel strategies for more efficient monitoring of critical variables, in order to improve the bioprocess performance. Thus, in this work, a set of chemometric tools were integrated to establish a PAT strategy to monitor cell viability, based on fluorescence multiway data obtained from fermentation samples of a particular bioprocess, in two different scales of operation. The spectral information, together with data regarding process variables, was integrated through chemometric exploratory tools to characterize the bioprocess and stablish novel criteria for the monitoring of cell viability. These findings motivated the development of a multivariate classification model, aiming to obtain predictive tools for the monitoring of future lots of the same bioprocess. The model could be satisfactorily fitted, showing the non-error rate of prediction of 100%.     

Applications of metabolic modeling to drive bioprocess development for the production of value-added chemicals

Increasing numbers of value added chemicals are being produced using microbial fermentation strategies. Computational modeling and simulation of microbial metabolism is rapidly becoming an enabling technology that is driving a new paradigm to accelerate the bioprocess development cycle. In particular, constraint-based modeling and the development of genome-scale models of industrial microbes are finding increasing utility across many phases of the bioprocess development workflow. Herein, we review and discuss the requirements and trends in the industrial application of this technology as we build toward integrated computational/experimental platforms for bioprocess engineering. Specifically we cover the following topics: (1) genome-scale models as genetically and biochemically consistent representations of metabolic networks; (2) the ability of these models to predict, assess, and interpret metabolic physiology and flux states of metabolism; (3) the model-guided integrative analysis of high throughput ‘omics’ data; (4) the reconciliation and analysis of on- and off-line fermentation data as well as flux tracing data; (5) model-aided strain design strategies and the integration of calculated biotransformation routes; and (6) control and optimization of the fermentation processes. Collectively, constraint-based modeling strategies are impacting the iterative characterization of metabolic flux states throughout the bioprocess development cycle, while also driving metabolic engineering strategies and fermentation optimization.     

我国工业生物过程工程研究进展

工业生物技术的进步离不开工业生物过程工程研究的不断深入及发展,我国作为工业发酵大国在工业生物技术由实验室向产业化转化过程中面对诸多挑战,由此而逐渐发展起来的我国工业生物过程工程发展先后经历了多个阶段,伴随着不同阶段的发展,我国的工业生物技术水平得到不断的提升。本文重点回顾了近三、四十年来我国工业生物过程工程发展的历程,包括早期由化工过程研究引入的动力学模型化研究、基于过程控制的优化理论与方法的应用、基于过程在线监测技术发展起来的参数相关性分析方法、过程多尺度理论的建立、基于现代固态发酵的新型固态发酵罐的设计及优化技术发展等。通过对生物过程工程发展历程的回顾对先进工业生物过程发展面临的技术难题及由此引出的未来发展重点方向进行了探讨。     

Expert systems in the control of animal cell culture processes: Potentials,functions, and perspectives

In recent years, the development of advanced systems for bioprocess monitoring and control has become an area of intensive research. Along with traditional techniques, there are several new approaches which are increasingly being applied to bioprocess operations. Among these, of special note is expert system technology, which provides possibilities for the design of efficient bioprocess control systems with new functional capabilities. This technology has been successfully applied to variety of microbial processes at laboratory and industrial scale. The present paper analyzes the possibility for application of expert systems to animal cell cultures processes whose high complexity is well suited to expert control. The discussion focuses on the organization and the functionality of the intelligent control systems, and covers some practical aspects of their design.     

Industrial bioprocess control and optimization in the context of systems biotechnology

The developments of the systems biotechnology and its application in the industrial process open up new horizons to industrial biotechnology. The unprecedented understanding of the relationships between cellular behaviors and the surrounding environments during the bioprocess has been achieved. In this paper, we review new advances in the strain improvement, bioprocess control and optimization. The holistic viewpoints and ideas applied in industrial bioprocesses and their future prospects are discussed by illustrating some successful cases.     

Industrial biotechnology of <Emphasis Type="Italic">Pseudomonas putida</Emphasis> and related species

Since their discovery many decades ago, Pseudomonas putida and related subspecies have been intensively studied with regard to their potential application in industrial biotechnology. Today, these Gram-negative soil bacteria, traditionally known as well-performing xenobiotic degraders, are becoming efficient cell factories for various products of industrial relevance including a full range of unnatural chemicals. This development is strongly driven by systems biotechnology, integrating systems metabolic engineering approaches with novel concepts from bioprocess engineering, including novel reactor designs and renewable feedstocks.     

Estimating environmental impacts of early-stage bioprocesses

The use of bioprocesses in industrial production promises resource- and energy-efficient processes starting from renewable, nonfossil feedstocks. Thus, the environmental benefits must be demonstrated, ideally in the early development phase with standardized methods such as life cycle assessment (LCA). Herein we discuss selected LCA studies of early-stage bioprocesses, highlighting their potential and contribution to estimating environmental impacts and decision support in bioprocess development. However, LCAs are rarely performed among bioprocess engineers due to challenges such as data availability and process uncertainties. To address this issue, recommendations are provided for conducting LCAs of early-stage bioprocesses. Opportunities are identified to facilitate future applicability, for example, by establishing dedicated bioprocess databases that could enable the use of LCAs as standard tools for bioprocess engineers.     

生物工程设备精品课程建设实践与探索

生物工程设备是现代工业生物技术转化为现实生产力和发挥作用的关键环节,随着工业生物技术的迅速发展,对掌握现代生物工程设备原理、设计和操作的人才的需求日益增加.本文分别从师资队伍建设、教材建设、教学内容及教学体系优化、教学方法革新等方面对生物工程设备精品课程建设经验进行了总结.     

Rapid analysis of the expression of heterologous proteins in Escherichia coli using pyrolysis mass spectrometry and Fourier transform infrared spectroscopy with chemometrics: application to alpha 2-interferon production.

Cell pastes and supernatant Escherichia coli samples, taken from an industrial bioprocess overproducing recombinant alpha 2 IFN were analysed using pyrolysis mass spectrometry (PyMS) and diffuse reflectance-absorbance Fourier transform infrared spectroscopy (FT-IR). PyMS and FT-IR are physico-chemical methods which measure predominantly the bond strengths of molecules and the vibrations of bonds within functional groups, respectively. They therefore give quantitative information about the total biochemical composition of the bioprocess sample. The interpretation of these hyperspectral data, in terms of the quantity of alpha 2 IFN in the cell pastes and supernatant samples was possible only after the application of the 'supervised learning' methods of artificial neural networks (ANNs) and partial least squares (PLS) regression. Both PyMS and FT-IR are novel, rapid and economical methods for the screening and the quantitative analysis of complex biological bioprocess over producing recombinant proteins. Models established using either spectral data set had a similarly satisfactory predictive ability. This shows that whole-reaction mixture spectral methods, which measure all molecules simultaneously, do contain enough information to allow their quantification when the entire spectra are used as the inputs to methods based on supervised learning. Moreover, this is the first study where FT-IR in the mid-IR range has been used to quantify the expression of a heterologous protein directly from fermentation broths and the first study to compare the abilities of PyMS and FT-IR for the quantitative analyses of an industrial bioprocess.     

Ultra scale‐down prediction using microwell technology of the industrial scale clarification characteristics by centrifugation of mammalian cell broths

This article describes how a combination of an ultra scale‐down (USD) shear device feeding a microwell centrifugation plate may be used to provide a prediction of how mammalian cell broth will clarify at scale. In particular a method is described that is inherently adaptable to a robotic platform and may be used to predict how the flow rate and capacity (equivalent settling area) of a centrifuge and the choice of feed zone configuration may affect the solids carry over in the supernatant. This is an important consideration as the extent of solids carry over will determine the required size and lifetime of a subsequent filtration stage or the passage of fine particulates and colloidal material affecting the performance and lifetime of chromatography stages. The extent of solids removal observed in individual wells of a microwell plate during centrifugation is shown to correlate with the vertical and horizontal location of the well on the plate. Geometric adjustments to the evaluation of the equivalent settling area of individual wells (Σ_M) results in an improved prediction of solids removal as a function of centrifuge capacity. The USD centrifuge settling characteristics need to be as for a range of equivalent flow rates as may be experienced at an industrial scale for a machine of different shear characteristics in the entry feed zone. This was shown to be achievable with two microwell‐plate based measurements and the use of varying fill volumes in the microwells to allow the rapid study of a fivefold range of equivalent flow rates (i.e., at full scale for a particular industrial centrifuge) and the effect of a range of feed configurations. The microwell based USD method was used to examine the recovery of CHO‐S cells, prepared in a 5 L reactor, at different points of growth and for different levels of exposure to shear post reactor. The combination of particle size distribution measurements of the cells before and after shear and the effect of shear on the solids remaining after centrifugation rate provide insight into the state of the cells throughout the fermentation and the ease with which they and accumulated debris may be removed by continuous centrifugation. Hence bioprocess data are more readily available to help better integrate cell culture and cell removal stages and resolve key bioprocess design issues such as choice of time of harvesting and the impact on product yield and contaminant carry over. Operation at microwell scale allows data acquisition and bioprocess understanding over a wide range of operating conditions that might not normally be achieved during bioprocess development. Biotechnol. Bioeng. 2009; 104: 321–331 © 2009 Wiley Periodicals, Inc.     

Extracellular vesicles concentration is a promising and important parameter for industrial bioprocess monitoring

Extracellular vesicles (EVs) are membrane vesicles that are produced by cells to be released into their microenvironment. In this study, we present the EV concentration as a new factor for optimization of industrial bioprocess control. The release of EVs depends on many cell properties, including cell activation and stress status, and cell death. Therefore, the EV concentration might provide a readout for identification of the cell state and the conditions during a bioprocess. Our data show that the EV concentration increased during the bioprocess, which indicated deteriorating conditions in the bioreactor. This increase in EV concentration in the fermentation broth was the consequence of two different processes: cell activation, and cell death. However, the release of EVs from activated living cells had a much weaker impact on EV concentration in the bioreactor than those released during cell death. EVs and cells in the bioprocess environment were quantified by flow cytometry. The most accurate data were obtained directly from unprocessed samples, making the monitoring of the EV concentration a rapid, easy, and cheap method. These EV concentrations reflect the conditions in the bioreactor and provide new information regarding the state of the bioprocess. Therefore, we suggest EV concentration as a new and important parameter for the monitoring of industrial bioprocesses.     

Bioprocess model building using artificial neural networks

Artificial neural networks are made upon of highly interconnected layers of simple neuron-like nodes. The neurons act as non-linear processing elements within the network. An attractive property of artificial neural networks is that given the appropriate network topology, they are capable of learning and characterising non-linear functional relationships. Furthermore, the structure of the resulting neural network based process model may be considered generic, in the sense that little prior process knowledge is required in its determination. The methodology therefore provides a cost efficient and reliable process modelling technique. One area where such a technique could be useful is biotechnological systems. Here, for example, the use of a process model within an estimation scheme has long been considered an effective means of overcoming inherent on-line measurement problems. However, the development of an accurate process model is extremely time consuming and often results in a model of limited applicability. Artificial neural networks could therefore prove to be a useful model building tool when striving to improve bioprocess operability. Two large scale industrial fermentation systems have been considered as test cases; a fed-batch penicillin fermentation and a continuous mycelial fermentation. Both systems serve to demonstrate the utility, flexibility and potential of the artificial neural network approach to process modelling.     

Data-based modeling and analysis of bioprocesses: some real experiences

Data-generated models find numerous applications in areas where the speed of collection and logging of data surpasses the ability to analyze it. This work is meant to addresses some of the challenges and difficulties encountered in the practical application of these methods in an industrial setting and, more specifically, in the bioprocess industry. Neural network and principal component models are the two topics that are covered in detail in this paper. A review of these modeling technologies as applied to bioprocessing is provided, and four original case studies using industrial fermentation data are presented that utilize these models in the context of prediction and monitoring of bioprocess performance.     

Identifying analytics for high throughput bioprocess development studies

In recent years, high throughput screening (HTS) studies have been increasingly employed as an integral element of bioprocess development activities. These studies are often limited by an analytical bottleneck; they generate multiple samples for analysis and the available analytical methods cannot always cope with the added analytical burden. A potential solution to this challenge is offered by the deployment of appropriate analytics. This article outlines features of analytical methods that affect their fit to high throughput (HT) applications. These are discussed for a range of analytics frequently used in bioprocess development studies of monoclonal antibodies. It then outlines how these features need to be considered in order to classify analytical methods in terms of their particular application in high throughput scenarios. Biotechnol. Bioeng. 2013; 110: 1924–1935. © 2013 Wiley Periodicals, Inc.     

微生物过氧化氢酶的发酵生产及其在纺织工业的应用

微生物过氧化氢酶是一种重要的工业酶制剂,可以催化分解过氧化氢生成水和氧气。这一酶制剂在食品、纺织、医药等领域表现出广泛的应用潜力。生物工程和基因工程技术的进步推动了微生物过氧化氢酶的发酵生产。以下综述了微生物过氧化氢酶发酵生产的进展及其在纺织工业中的应用,同时讨论了微生物过氧化氢酶的发酵生产和纺织工业应用的未来趋势。     

Affinity Chromatography: An Enabling Technology for Large‐Scale Bioprocessing

Affinity chromatography (AC) has been used in large‐scale bioprocessing for almost 40 years and is considered the preferred method for primary capture in downstream processing of various types of biopharmaceuticals. The objective of this mini‐review is to provide an overview of a) the history of bioprocess AC, b) the current state of platform processes based on affinity capture steps, c) the maturing field of custom developed bioprocess affinity resins, d) the advantages of affinity capture‐based downstream processing in comparison to other forms of chromatography, and e) the future direction for bioprocess scale AC. The use of AC can result in economic advantages by enabling the standardization of process development and the manufacturing processes and the use of continuous operations in flexible multiproduct production suites. These concepts are discussed from a growing field of custom affinity bioprocess resin perspective. The custom affinity resins not only address the need for a capture resin for non‐platformable processes, but also can be employed in polishing applications, where they are used to define and control drug substance composition by separating specific product variants from the desired product form.     

Machine learning in bioprocess development: from promise to practice

Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.     

Economic analysis of pilot‐scale production of B‐phycoerythrin

β‐Phycoerythrin is a color protein with several applications, from food coloring to molecular labeling. Depending on the application, different purity is required, affecting production cost and price. Different production and purification strategies for B‐phycoerythrin have been developed, the most studied are based on the production using Porphyridium cruentum and purified using chromatographic techniques or aqueous two‐phase systems. The use of the latter can result in a less expensive and intensive recovery of the protein, but there is lack of a proper economic analysis to study the effect of using aqueous two‐phase systems in a scaled‐up process. This study analyzed the production of B‐Phycoerythrin using real data obtained during the scale‐up of a bioprocess using specialized software (BioSolve, Biopharm Services, UK). First, a sensitivity analysis was performed to identify critical parameters for the production cost, then a Monte Carlo analysis to emulate real processes by adding uncertainty to the identified parameters. Next, the bioprocess was analyzed to determine its financial attractiveness and possible optimization strategies were tested and discussed. Results show that aqueous two‐phase systems retain their advantages of low cost and intensive recovery (54.56%); the costs of production per gram calculated (before titer optimization: US$15,709 and after optimization: US$2,374) allowed to obtain profit (in the range of US$millions in a 10‐year period) for a potential company taking this production method by comparing the production cost against commercial prices. The bioprocess analyzed is a promising and profitable method for the generation of a highly purified B‐phycoerythrin. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1472–1479, 2016