A logic-reasoning based system to harness bioprocess experimental data and knowledge for design

Bioprocess design requires substantial resources during the experimental investigation of the options for each bioprocess step. This is both time-consuming and expensive. The amount of data available has increased exponentially since the expansion of new biological drug development. Data are heterogeneous, sometimes inconsistent and incomplete, making them difficult to be systematically utilised for analysis for any new bioprocess design. In this paper, we report a novel computational method that harnesses the bioprocess experimental data to assist design decision making, and perhaps identify further needed experiments. First, we develop a new data representation structure to capture the experimental data systematically. Then the ontology for modelling the relationship of data properties is created. A computational system has been developed to search relevant data, or to predict required process conditions, or to suggest a new set of experiments for process development. A prototype for harnessing centrifugation experimental data has been built, and is then used to illustrate the method and demonstrate the type of results that can be obtained. Evaluations show that such a system has significant potential to mine the relevant experimental data to assist new drug bioprocess development, which should reduce process development time and cost.     

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.     

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.     

Microscale bioprocess optimisation

Microscale processing techniques offer the potential to speed up the delivery of new drugs to the market, reducing development costs and increasing patient benefit. These techniques have application across both the chemical and biopharmaceutical sectors. The approach involves the study of individual bioprocess operations at the microlitre scale using either microwell or microfluidic formats. In both cases the aim is to generate quantitative bioprocess information early on, so as to inform bioprocess design and speed translation to the manufacturing scale. Automation can enhance experimental throughput and will facilitate the parallel evaluation of competing biocatalyst and process options.     

Hybrid modeling in bioprocess dynamics: Structural variabilities,implementation strategies,and practical challenges

Hybrid modeling, with an appropriate blend of the mechanistic and data-driven framework, is increasingly being adopted in bioprocess modeling, model-based experimental design (digital-twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data-driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an “optimal” model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data-driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid-model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.     

Dynamic process conditions in bioprocess development

In this review, we summarise recent studies that purposefully employed dynamic conditions, such as shifts, pulses, ramps and oscillations, for fast physiological strain characterisation and bioprocess development. We show the broad applicability of dynamic conditions and the various objectives that can thereby be investigated in a short time. Dynamic processes reveal information about the analysed system faster than traditional strategies, like continuous cultivations, as process parameters can directly be linked to platform and product parameters. Furthermore, we demonstrate that dynamic operations can result in increased productivity and high product quality, making this strategy a valuable tool for bioprocess development. With this review, we would like to encourage bioprocess engineers to an increased use of dynamic conditions in bioprocess development.     

Micro biochemical engineering to accelerate the design of industrial-scale downstream processes for biopharmaceutical proteins

The article examines how a small set of easily implemented micro biochemical engineering procedures combined with regime analysis and bioprocess models can be used to predict industrial scale performance of biopharmaceutical protein downstream processing. This approach has been worked on in many of our studies of individual operations over the last 10 years and allows preliminary evaluation to be conducted much earlier in the development pathway because of lower costs. It then permits the later large scale trials to be more highly focused. This means that the risk of delays during bioprocess development and of product launch are reduced. Here we draw the outcomes of this research together and illustrate its use in a set of typical operations; cell rupture, centrifugation, filtration, precipitation, expanded bed adsorption, chromatography and for common sources, E. coli, two yeasts and mammalian cells (GS-NSO). The general approach to establishing this method for other operations is summarized and new developments outlined. The technique is placed against the background of the scale-down methods that preceded it and complementary ones that are being examined in parallel. The article concludes with a discussion of the advantages and limitations of the micro biochemical engineering approach versus other methods.     

Systems biotechnology of animal cells: the road to prediction

A central concern of biopharmaceutical R&D is the production of sufficient quantities of recombinant products from manufacturing processes based on animal cell culture. The way in which bioprocess researchers have addressed this question experienced a tremendous shift over the years, progressing from almost empirical to more rational approaches. A step further is the application of systems biotechnology: recent technological advances for large-scale cell state characterization and creative methods for host cell modeling are becoming crucial for next-generation bioprocess optimization. Here we provide an overview of the main trends towards this goal, with a focus on metabolic models as central scaffolds for data integration and prediction of bioprocess outcomes.     

Process adapted calibration improves fluorometric pH sensor precision in sophisticated fermentation processes

Miniaturization and automation have become increasingly popular in bioprocess development in recent years, enabling rapid high‐throughput screening and optimization of process conditions. In addition, advances in the bioprocessing industry have led to increasingly complex process designs, such as pH and temperature shifts, in microbial fed‐batch fermentations for optimal soluble protein expression in a range of hosts. However, in order to develop an accurate scale‐down model for bioprocess screening and optimization, small‐scale bioreactors must be able to accurately reproduce these complex process designs. Monitoring methods, such as fluorometric‐based pH sensors, provide elegant solutions for the miniaturization of bioreactors, however, previous research suggests that the intrinsic fluorescence of biomass alters the sigmoidal calibration curve of fluorometric pH sensors, leading to inaccurate pH control. In this article, we present results investigating the impact of biomass on the accuracy of a commercially available fluorometric pH sensor. Subsequently, we present our calibration methodology for more precise online measurement and provide recommendations for improved pH control in sophisticated fermentation processes.     

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。     

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.     

Covariation of GC content and the silent site substitution rate in rodents: implications for methodology and for the evolution of isochores

Many attempts to test selectionist and neutralist models employ estimates of synonymous (Ks) and non-synonymous (Ka) substitution rates of orthologous genes. For example, a stronger Ka-Ks correlation than expected under neutrality has been argued to indicate a role for selection and the absence of a Ks-GC4 correlation has been argued to be inconsistent with neutral models for isochore evolution. However, both of these results, we have shown previously, are sensitive to the method by which Ka and Ks are estimated. Using a maximum likelihood (ML) estimator (GY94) we found a positive correlation between Ks and GC4 and only a weak correlation between Ka and Ks, lower than expected under neutral expectations. This ML method is computationally slow. Recently, a new ad hoc approximation of this ML method has been provided (YN00). This is effectively an extension of Li's protocol but that also allows for codon usage bias. This method is computationally near-instantaneous and therefore potentially of great utility for analysis of large datasets. Here we ask whether this method might have such applicability. To this end we ask whether it too recovers the two unusual results. We report that when the ML and earlier ad hoc methods disagree, YN00 recovers the results described by the ML methods, i.e. a positive correlation between GC4 and Ks and only a weak correlation between Ks and Ka. If the ML method can be trusted, then YN00 can also be considered an adequately reliable method for analysis of large datasets. Assuming this to be so we also analyze further the patterns. We show, for example, that the positive correlation between GC4 and Ks is probably in part a mutational bias, there being more methyl induced CpG-->TpG mutations in GC rich regions. As regards the evolution of isochores, it seems inappropriate to use the claimed lack of a correlation between GC and Ks as definitive evidence either against or for any model. If the positive correlation is real then, we argue, this is hard to reconcile with the biased gene conversion model for isochore formation as this predicts a negative correlation.     

Universal label‐free in‐process quantification of influenza virus‐like particles

Virus‐like particles (VLPs) are becoming established as vaccines, in particular for influenza pandemics, increasing the interest in the development of VLPs manufacturing bioprocess. However, for complex VLPs, the analytical tools used for quantification are not yet able to keep up with the bioprocess progress. Currently, quantification for Influenza relies on traditional methods: hemagglutination assay or Single Radial Immunodiffusion. These analytical technologies are time‐consuming, cumbersome, and not supportive of efficient downstream process development and monitoring. Hereby we report a label‐free tool that uses Biolayer interferometry (BLI) technology applied on an Octet platform to quantify Influenza VLPs at all stages of bioprocess. Human (α2,6‐linked sialic acid) and avian (α2,3‐linked sialic acid) biotinylated receptors associated with streptavidin biosensors were used, to quantify hemagglutinin content in several mono‐ and multivalent Influenza VLPs. The applied method was able to quantify hemagglutinin from crude samples up to final bioprocessing VLP product. BLI technology confirmed its value as a high throughput analytical tool with high sensitivity and improved detection limits compared to traditional methods. This simple and fast method allowed for real‐time results, which are crucial for in‐line monitoring of downstream processing, improving process development, control and optimization.     

Cover Image,Volume 116, Number 9, September 2019

The new and rapid advancement in the complexity of biologics drug discovery has been driven by a deeper understanding of biological systems combined with innovative new therapeutic modalities, paving the way to breakthrough therapies for previously intractable diseases. These exciting times in biomedical innovation require the development of novel technologies to facilitate the sophisticated, multifaceted, high-paced workflows necessary to support modern large molecule drug discovery. A high-level aspiration is a true integration of “lab-on-a-chip” methods that vastly miniaturize cellulmical experiments could transform the speed, cost, and success of multiple workstreams in biologics development. Several microscale bioprocess technologies have been established that incrementally address these needs, yet each is inflexibly designed for a very specific process thus limiting an integrated holistic application. A more fully integrated nanoscale approach that incorporates manipulation, culture, analytics, and traceable digital record keeping of thousands of single cells in a relevant nanoenvironment would be a transformative technology capable of keeping pace with today's rapid and complex drug discovery demands. The recent advent of optical manipulation of cells using light-induced electrokinetics with micro- and nanoscale cell culture is poised to revolutionize both fundamental and applied biological research. In this review, we summarize the current state of the art for optical manipulation techniques and discuss emerging biological applications of this technology. In particular, we focus on promising prospects for drug discovery workflows, including antibody discovery, bioassay development, antibody engineering, and cell line development, which are enabled by the automation and industrialization of an integrated optoelectronic single-cell manipulation and culture platform. Continued development of such platforms will be well positioned to overcome many of the challenges currently associated with fragmented, low-throughput bioprocess workflows in biopharma and life science research.     

Essential prerequisites for successful bioprocess development of biological CH4 production from CO2 and H2

The production and storage of energy from renewable resources steadily increases in importance. One opportunity is to utilize carbon dioxide (CO₂)-type hydrogenotrophic methanogens, which are an intriguing group of microorganisms from the domain Archaea, for conversion of hydrogen and CO₂ to methane (CH₄). This review summarizes the current state of the art of bioprocess development for biological CH₄ production (BMP) from pure cultures with pure gasses. The prerequisites for successful quantification of BMP by using closed batch, as well as fed-batch and chemostat culture cultivation, are presented. This review shows that BMP is currently a much underexplored field of bioprocess development, which mainly focuses on the application of continuously stirred tank reactors. However, some promising alternatives, such as membrane reactors have already been adapted for BMP. Moreover, industrial-based scale-up of BMP to pilot scale and larger has not been conducted. Most crucial parameters have been found to be those, which influence gas-limitation fundamentals, or parameters that contribute to the complex effects that arise during medium development for scale-up of BMP bioprocesses, highly stressing the importance of holistic BMP quantification by the application of well-defined physiological parameters. The much underexplored number of different genera, which is mainly limited to Methanothermobacter spp., offers the possibility of additional scientific and bioprocess development endeavors for the investigation of BMP. This indicates the large potential for future bioprocess development considering the possible application of bioprocessing technological aspects for renewable energy storage and power generation.     

Novel machine learning approaches revolutionize protein knowledge

Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no longer limited by time and resources. The most recent method to be top ranked by the Critical Assessment of Structure Prediction (CASP) assessment, AlphaFold 2 (AF2), is capable of building structural models with an accuracy comparable to that of experimental structures. Annotations of 3D models are keeping pace with the deposition of the structures due to advancements in protein language models (pLMs) and structural aligners that help validate these transferred annotations. In this review we describe how recent developments in ML for protein science are making large-scale structural bioinformatics available to the general scientific community.     

Novel strategy to improve hepatocyte differentiation stability through synchronized behavior-driven mechanical memory of iPSCs

Cellular homeostasis is assumed to be regulated by the coordination of dynamic behaviors. Lack of efficient methods for synchronizing large quantities of cells makes studying cell culture strategies for bioprocess development challenging. Here, we demonstrate a novel application of botulinum hemagglutinin (HA), an E-cadherin function-blocking agent, to synchronize behavior-driven mechanical memory in human induced pluripotent stem cell (hiPSC) cultures. Application of HA to hiPSCs resulted in a decrease in actin bundling and disruption of colony formation in a concentration-and time-dependent manner. Interestingly, cytoskeleton rearrangement in cells with prolonged exposure to HA resulted in mechanical memory synchronization with Yes-associated protein, which increased pluripotent cell homogeneity. Synchronized hiPSCs have higher capability to differentiate into functional hepatocytes than unsynchronized hiPSCs, resulting in improved efficiency and robustness of hepatocyte differentiation. Thus, our strategy for cell behavior synchronization before differentiation induction provides an approach against the instability of differentiation of pluripotent cells.