Penicillin Fermentation: Mechanisms and Models for Industrial-Scale Bioreactors

ABSTRACT:?

Even after many years of research and industrial practice, the production of penicillin G in fed-batch fermentation by Penicillium crysogenum continues to attract research interest. There are many reasons: the commercial and therapeutic importance of penicillin and its derivatives, the complexity of cell growth, and the impact of engineering variables, the last of which are significant in large bioreactors but are not yet fully understood. Extensive research has generated new information on the mechanisms of cellular reactions and morphological features of the mycelia and their role in the synthesis of the product. Given a choice of mechanisms, models of different degrees of complexity, for both cellular differentiation and bioreactor performance, have been proposed. The more complex models require and provide more information. They are also more difficult to evaluate and apply in automatic control systems for production-scale bioreactors. The present review considers the evolution of recent knowledge and models from this perspective.     

Three-dimensional culture and bioreactors for cellular therapies

A bioreactor is defined as a specifically designed vessel to facilitate the growth of organisms and cells through application of physical and/or electrical stimulus. When cells with therapeutic potential were first discovered, they were initially cultured and expanded in two-dimensional (2-D) culture vessels such as plates or T-flasks. However, it was soon discovered that bioreactors could be used to expand and maintain cultures more easily and efficiently. Since then, bioreactors have come to be accepted as an indispensable tool to advance cell and tissue culture further. A wide array of bioreactors has been developed to date, and in recent years businesses have started supplying bioreactors commercially. Bioreactors in the research arena range from stirred tank bioreactors for suspension culture to those with various mechanical actuators that can apply different fluidic and mechanical stresses to tissues and three-dimensional (3-D) scaffolds. As regenerative medicine gains more traction in the clinic, bioreactors for use with cellular therapies are being developed and marketed. While many of the simpler bioreactors are fit for purpose, others fail to satisfy the complex requirements of tissues in culture. We have examined the use of different types of bioreactors in regenerative medicine and evaluated the application of bioreactors in the realization of emerging cellular therapies.     

新型生物反应器结构研究进展

生物反应器是生物工程的核心设备,其结构的合理性直接决定反应器生物加工的效率。生物反应器的研究一直是生物工程的核心问题之一。随着青霉素的工业化生产,机械搅拌式生物反应器应运而生,此后,随着动植物细胞培养,高等真菌培养,藻类培养等生物过程的发展,人们相应开发了大量的生物反应器,其中以机械搅拌式生物反应器和气升式样生物反应器尤为突出,本文总结了近年来文献报道的新型生物反应器,主要阐述了机械搅拌式和气升式两类生物反应器结构的研究进展,对目前国内外报道的11种新型反应器典型结构进行了总结与分析。     

Exploiting biological complexity for strain improvement through systems biology

Cellular complexity makes it difficult to build a complete understanding of cellular function but also offers innumerable possibilities for modifying the cellular machinery to achieve a specific purpose. The exploitation of cellular complexity for strain improvement has been a challenging goal for applied biological research because it requires the coordinated understanding of multiple cellular processes. It is therefore pursued most efficiently in the framework of systems biology. Progress in strain improvement will depend not only on advances in technologies for high-throughput measurements but, more importantly, on the development of theoretical methods that increase the information content of these measurements and, as such, facilitate the elucidation of mechanisms and the identification of genetic targets for modification.     

Quantitative Modeling of Tumor Dynamics and Radiotherapy

Cancer is a complex disease, necessitating research on many different levels; at the subcellular level to identify genes, proteins and signaling pathways associated with the disease; at the cellular level to identify, for example, cell-cell adhesion and communication mechanisms; at the tissue level to investigate disruption of homeostasis and interaction with the tissue of origin or settlement of metastasis; and finally at the systems level to explore its global impact, e.g. through the mechanism of cachexia. Mathematical models have been proposed to identify key mechanisms that underlie dynamics and events at every scale of interest, and increasing effort is now being paid to multi-scale models that bridge the different scales. With more biological data becoming available and with increased interdisciplinary efforts, theoretical models are rendering suitable tools to predict the origin and course of the disease. The ultimate aims of cancer models, however, are to enlighten our concept of the carcinogenesis process and to assist in the designing of treatment protocols that can reduce mortality and improve patient quality of life. Conventional treatment of cancer is surgery combined with radiotherapy or chemotherapy for localized tumors or systemic treatment of advanced cancers, respectively. Although radiation is widely used as treatment, most scheduling is based on empirical knowledge and less on the predictions of sophisticated growth dynamical models of treatment response. Part of the failure to translate modeling research to the clinic may stem from language barriers, exacerbated by often esoteric model renderings with inaccessible parameterization. Here we discuss some ideas for combining tractable dynamical tumor growth models with radiation response models using biologically accessible parameters to provide a more intuitive and exploitable framework for understanding the complexity of radiotherapy treatment and failure.     

Monitoring bioreactors using principal component analysis: production of penicillin G acylase as a case study

The complexity of biological processes often makes impractical the development of detailed, structured phenomenological models of the cultivation of microorganisms in bioreactors. In this context, data pre-treatment techniques are useful for bioprocess control and fault detection. Among them, principal component analysis (PCA) plays an important role. This work presents a case study of the application of this technique during real experiments, where the enzyme penicillin G acylase (PGA) was produced by Bacillus megaterium ATCC 14945. PGA hydrolyzes penicillin G to yield 6-aminopenicilanic acid (6-APA) and phenyl acetic acid. 6-APA is used to produce semi-synthetic β-lactam antibiotics. A static PCA algorithm was implemented for on-line detection of deviations from the desired process behavior. The experiments were carried out in a 2-L bioreactor. Hotteling’s T ² was the discrimination criterion employed in this multivariable problem and the method showed a high sensibility for fault detection in all real cases that were studied.     

From components to regulatory motifs in signalling networks.

The developments in biochemistry and molecular biology over the past 30 years have produced an impressive parts list of cellular components. It has become increasingly clear that we need to understand how components come together to form systems. One area where this approach has been growing is cell signalling research. Here, instead of focusing on individual or small groups of signalling proteins, researchers are now using a more holistic perspective. This approach attempts to view how many components are working together in concert to process information and to orchestrate cellular phenotypic changes. Additionally, the advancements in experimental techniques to measure and visualize many cellular components at once gradually grow in diversity and accuracy. The multivariate data, produced by experiments, introduce new and exciting challenges for computational biologists, who develop models of cellular systems made up of interacting cellular components. The integration of high-throughput experimental results and information from legacy literature is expected to produce computational models that would rapidly enhance our understanding of the detail workings of mammalian cells.     

Non‐Newtonian rheology in suspension cell cultures significantly impacts bioreactor shear stress quantification

The fields of regenerative medicine and tissue engineering require large‐scale manufacturing of stem cells for both therapy and recombinant protein production, which is often achieved by culturing cells in stirred suspension bioreactors. The rheology of cell suspensions cultured in stirred suspension bioreactors is critical to cell growth and protein production, as elevated exposure to shear stress has been linked to changes in growth kinetics and genetic expression for many common cell types. Currently, little is understood on the rheology of cell suspensions cultured in stirred suspension bioreactors. In this study, we present the impact of three common cell culture parameters, serum content, cell presence, and culture age, on the rheology of a model cell line cultured in stirred suspension bioreactors. The results reveal that cultures containing cells, serum, or combinations thereof are highly shear thinning, whereas conditioned and unconditioned culture medium without serum are both Newtonian. Non‐Newtonian viscosity was modeled using a Sisko model, which provided insight on structural mechanisms driving the rheological behavior of these cell suspensions. A comparison of shear stress estimated by using Newtonian and Sisko relationships demonstrated that assuming Newtonian viscosity underpredicts both mean and maximum shear stress in stirred suspension bioreactors. Non‐Newtonian viscosity models reported maximum shear stresses exceeding those required to induce changes in genetic expression in common cell types, whereas Newtonian models did not. These findings indicate that traditional shear stress quantification of cell or serum suspensions is inadequate and that shear stress quantification methods based on non‐Newtonian viscosity must be developed to accurately quantify shear stress.     

A cellular automata model for simulating fed-batch penicillin fermentation process

A cellular automata model to simulate penicillin fed-batch fermentation process(CAPFM)was established in this study,based on a morphologically structured dynamic penicillin production model,that is in turn based on the growth mechanism of penicillin producing microorganisms and the characteristics of penicillin fed-batch fermentation.CAPFM uses the three-dimensional cellular automata as a growth space,and a Moore-type neighborhood as the cellular neighborhood.The transition roles of CAPFM are designed based on mechanical and structural kinetic models of penicillin batch-fed fermentation processes.Every cell of CAPFM represents a single or specific number of penicillin producing microorganisms,and has various state.The simulation experimental results show that CAPFM replicates the evolutionary behavior of penicillin batch-fed fermentation processes described by the structured penicillin production kinetic model accordingly.     

Performance of industrial scale bioreactors with modified RUSHTON turbine agitators

The data presented here with respect to the behaviour of industrial scale stirred tank bioreactors equipped with modified RUSHTON turbine agitators in the biosynthesis processes of antibiotics are valid for that case that the power consumption is the same as it is in standard RUSHTON turbine agitators. Each modified RUSHTON turbine agitator was obtained through the variation of the blade surface by adding perforations so that the ratio between the perforation surface area and the full surface area (or the surface fraction of the perforations) is 0.36. In the fermentations of Streptomyces aureofaciens, Streptomyces rimosus and Penicillium chrysogenum producing tetracycline, oxytetracyline and penicillin, respectively, in bioreactors equipped with modified RUSHTON turbine agitators, the relative antibiotic production is increased by more than 30% compared to standard bioreactors.     

Remote Neurodegeneration: Multiple Actors for One Play

Remote neurodegeneration significantly influences the clinical outcome in many central nervous system (CNS) pathologies, such as stroke, multiple sclerosis, and traumatic brain and spinal cord injuries. Because these processes develop days or months after injury, they are accompanied by a therapeutic window of opportunity. The complexity and clinical significance of remote damage is prompting many groups to examine the factors of remote degeneration. This research is providing insights into key unanswered questions, opening new avenues for innovative neuroprotective therapies. In this review, we evaluate data from various remote degeneration models to describe the complexity of the systems that are involved and the importance of their interactions in reducing damage and promoting recovery after brain lesions. Specifically, we recapitulate the current data on remote neuronal degeneration, focusing on molecular and cellular events, as studied in stroke and brain and spinal cord injury models. Remote damage is a multifactorial phenomenon in which many components become active in specific time frames. Days, weeks, or months after injury onset, the interplay between key effectors differentially affects neuronal survival and functional outcomes. In particular, we discuss apoptosis, inflammation, oxidative damage, and autophagy—all of which mediate remote degeneration at specific times. We also review current findings on the pharmacological manipulation of remote degeneration mechanisms in reducing damage and sustaining outcomes. These novel treatments differ from those that have been proposed to limit primary lesion site damage, representing new perspectives on neuroprotection.     

Introduction to advantages and problems of shaken cultures

Shaking bioreactors are the most frequently used reaction vessels in biotechnology and have been so for many decades. In spite of their large practical importance, very little is known about the characteristic properties of shaken cultures from an engineering point of view. The few publications available contain to some extent contradicting statements and conflicting advice concerning the correct operating conditions of shaking bioreactors. Depending on the investigated microbial system, the engineering parameters may more or less significantly influence the experimental results in a quantitative as well as in a qualitative manner. Unfortunately, these kind of interactions are often overlooked or ignored by scientists. Precise knowledge about the controlling hydrodynamic phenomena in shaking bioreactors and quantitative information about the physical parameters influencing the cultures are needed to assure reproducible and meaningful operating conditions. In this introduction, the state of the art of culturing microorganisms in shaking bioreactors is reviewed and some issues of their practical application in screening and process development projects are addressed.     

Use of microgravity bioreactors for development of an in vitro rat salivary gland cell culture model

During development, salivary gland (SG) cells both secrete factors which modulate cellular behavior and express specific hormone receptors. Whether SG cell growth is modulated by an autocrine epidermal growth factor (EGF) receptor-mediated signal transduction pathway is not clearly understood. SG tissue is the synthesis site for functionally distinct products including growth factors, digestive enzymes, and homeostasis maintaining factors. Historically, SG cells have proven difficult to grow and may be only maintained as limited three-dimensional ductal-type structures in collagen gels or on reconstituted basement membrane gels. A novel approach to establishing primary rat SG cultures is use of microgravity bioreactors originally designed by NASA as low-shear culture systems for predicting cell growth and differentiation in the microgravity environment of space. These completely fluid-filled bioreactors, which are oriented horizontally and rotate, have proven advantageous for Earth-based culture of three-dimensional cell assemblies, tissue-like aggregates, and glandular structures. Use of microgravity bioreactors for establishing in vitro models to investigate steroid-mediated secretion of EGF by normal SG cells may also prove useful for the investigation of cancer and other salivary gland disorders. These microgravity bioreactors promise challenging opportunities for future applications in basic and applied cell research. © 1993 Wiley-Liss, Inc.     

Towards an e-biology of ageing: integrating theory and data

Ageing is a highly complex process; it involves interactions between numerous biochemical and cellular mechanisms that affect many tissues in an organism. Although work on the biology of ageing is now advancing quickly, this inherent complexity means that information remains highly fragmented. We describe how a new web-based modelling initiative is seeking to integrate data and hypotheses from diverse biological sources.     

Computer simulations of in vitro morphogenesis

One of the most important challenges of contemporary biology is understanding how cells assemble into tissues. The complexity of morphogenesis calls for computational tools able to identify the dominant mechanisms involved in shaping tissues. This narrative review presents individual-based computational models that proved useful in simulating phenomena of interest in tissue engineering (TE), a research field that aims to create tissue replacements in the laboratory. First, we briefly describe morphogenetic mechanisms. Then, we present several computational models of cellular and subcellular resolution, along with applications that illustrate their potential to address problems of TE. Finally, we analyze experiments that may be used to validate computational models of tissue constructs made of cohesive cells. Our analysis shows that the models available in the literature are not exploited to their full potential. We argue that, upon validation, a computational model can be used to optimize cell culture conditions and to design new experiments.     

Drosophila as an In Vivo Model for Human Neurodegenerative Disease

With the increase in the ageing population, neurodegenerative disease is devastating to families and poses a huge burden on society. The brain and spinal cord are extraordinarily complex: they consist of a highly organized network of neuronal and support cells that communicate in a highly specialized manner. One approach to tackling problems of such complexity is to address the scientific questions in simpler, yet analogous, systems. The fruit fly, Drosophila melanogaster, has been proven tremendously valuable as a model organism, enabling many major discoveries in neuroscientific disease research. The plethora of genetic tools available in Drosophila allows for exquisite targeted manipulation of the genome. Due to its relatively short lifespan, complex questions of brain function can be addressed more rapidly than in other model organisms, such as the mouse. Here we discuss features of the fly as a model for human neurodegenerative disease. There are many distinct fly models for a range of neurodegenerative diseases; we focus on select studies from models of polyglutamine disease and amyotrophic lateral sclerosis that illustrate the type and range of insights that can be gleaned. In discussion of these models, we underscore strengths of the fly in providing understanding into mechanisms and pathways, as a foundation for translational and therapeutic research.     

Complexity of calcium signaling in synaptic spines

Long-term potentiation and long-term depression are thought to be cellular mechanisms contributing to learning and memory. Although the physiological phenomena have been well characterized, little consensus of their underlying molecular mechanisms has emerged. One reason for this may be the under-appreciated complexity of the signaling pathways that can arise if key signaling molecules are discretely localized within the synapse. Recent findings suggest an unanticipated degree of structural organization at the synapse, and improved methods in cellular imaging of living tissue have provided much-needed information about the intracellular dynamics of Ca(2+), thought to be critical for both LTP and LTD. In this review, we briefly summarize some of these developments, and show that a more complete understanding of cellular signaling depends on the successful integration of traditional biochemistry and molecular biology with the spatial and temporal details of synaptic ultrastructure. Biophysically realistic computer simulations can have an important role in bridging these disciplines.