Physiological state control of fermentation processes

In this article a novel approach to the control of fermentation processes is introduced. A "physiological state control approach" has been developed using the concept of representing fermentation processes through the current physiological state of the cell culture. No conventional mathematical model is required for the synthesis of such a control system.The main idea is based on the fact that during batch, feed-batch, or even continuous cultivation the physiological characteristics of the cell population, jointly expressed by the term "physiological state", are not constant but rather variable, which is reflected in expected or unexpected changes in the behavior of the control plant, and which requires flexible alteration of the current control strategy. The proposed approach involves decomposition of the physiological state space into several subspaces called "physiological situations." In every physiological situation the cell population expresses stable characteristics, and therefore an invariant control strategy can be effectively applied. The on-line functions of the physiological state control system consist of the calculation of physiological state variables, determination of the current physiological situation as an element of a previously defined set of known physiological situations, switching of the relevant control strategy, and calculation of the control action. Attention is focused on the synthesis of the novel and nonstandard part of the control system - the algorithm for online recognition of the current physiological state. To this end an effective approach, based on artificial intelligence methods, particularly fuzzy sets theory and pattern recognition theory, was developed. Its practical realization is demonstrated using data from a continuous fermentation process for single cell protein production.     

On-line stoichiometry and identification of metabolic state under dynamic process conditions

A method for the on-line calculation of conversion rates and yield coefficients under dynamic process conditions was developed. The method is based on cumulated mass balances using a moving average method. Elemental balances were used to test the measured cumulated quantities for gross errors and inappropriate stoichiometry definition followed by data reconciliation and estimation of non-measured conversion rates, using a bioprocess set-up including multiple on-line analysis techniques. The quantitative potential of the proposed method is demonstrated by executing transient experiments in aerobic cultures of Saccharomyces cerevisiae on glucose. Rates and yield coefficients could be consistently quantified in shift-up, shift-down, and accelerostat experiments. The method shows the capability to describe quantitatively transient changes in metabolism including uncoupling of catabolism and anabolism, also for the case when multiple components of metabolism are not measured. The validity of the experiment can be evaluated on-line. Additionally, the method detects with high sensitivity inappropriate stoichiometry definition, such as a change in state of metabolism. It was shown that concentration values can be misleading for the identification of the metabolic state. In contrast, the proposed method provides a clear picture of the metabolic state and new physiological regulations could be revealed. Hence, the novelty of the proposed method is the on-line availability of consistent stoichiometric coefficients allowing a significant speed up in strain characterization and bioprocess development using minimal knowledge of the metabolism. Additionally, it opens up the use of transient experiments for physiological studies.     

Monitoring and control of the physiological state of cell cultures

Advances in bioprocess engineering depends ultimately on the level of understanding and control of the physiological state of the cell population. Process efficiency is strongly influenced by changes in the cellular state which should be monitored, interpreted, and, if possible, properly manipulated. In most control systems this function is not explicitly considered, which hampers process development and optimization. Conventional control logic is based on direct mapping of the growth environment into process efficiency, thereby bypassing the cell state as an intermediate control objective. Today, this limitation is well realized, and explicit monitoring and control of cellular physiology are considered to be among the most challenging tasks of modern bioprocess engineering. We present here a generic methodology for the design of systems capable of performing these advanced monitoring and control functions.The term "physiological state" is quantified by a vector composed of several process variables that convey significant information about cellular state. These variables can be selected among different classes, including specific metabolic rates, metabolic rate ratios, degees of limitation, and others. The real-time monitoring of many of these is possible using commercial sensors. The definition and calculation of representative sets of physiological state variables is demonstrated with examples from several fermentor cultures: recombinant Escherichia coli for phenylalanine production, bioluminescent E. coli (harboring lux genes driven by a heat shock protein promoter) for detection of environmental pollutants, plant cell culture of Perilla frutescensfor anthocyanin production, and perfusion cultures of recombinant mammalian cells (NS0 and CHO) for therapeutic protein production.If the physiological state vector is on-line calculated, the fermentation process can be described by its trajectory in a space defined by the vector components. Then, the goal of the control system is to maintain the physiological state of the cell as close as possible to the trajectory, providing maximum efficiency. A control structure meant to perform this function is proposed, along with the mechanism for its design. In contrast to conventional systems which work in a closed loop in respect to the cell environment, this scheme operates in a closed loop in respect to the cell state. The discussed control concept has been successfully applied to the recombinant phenylalanine production, resulting in physiologically consistent operation, total computer control, and high process efficiency. Initial results from the application of the method to perfusion mammalian cell cultures are also presented. (c) 1996 John Wiley & Sons, Inc.     

Monitoring of recombinant protein production using bioluminescence in a semiautomated fermentation process

On-line optimization of fermentation processes can be greatly aided by the availability of information on the physiological state of the cell. The goal of our "BioLux" research project was to design a recombinant cell capable of intracellular monitoring of product synthesis and to use it as part of an automated fermentation system. A recombinant plasmid was constructed containing an inducible promoter that controls the gene coding for a model protein and the genes necessary for bioluminescence. The cells were cultured in microfermenters equipped with an on-line turbidity sensor and a specially designed on-line light sensor capable of continuous measurement of bioluminescence. Initial studies were done under simple culture conditions, and a linear correlation between luminescence and protein production was obtained. Such specially designed recombinant bioluminescent cells can potentially be applied for model-based inference of intracellular product formation, as well as for optimization and control of recombinant fermentation processes.     

Metabolism analysis and on-line physiological state diagnosis of acetone-butanol fermentation

Fermentation equations for acetone-butanol (AB) were applied in a metabolic analysis of the reaction network under various conditions; that is, at different pHs and a high NADH2 turnover rate using methyl viologen, in a Clostridium acetobutylicum culture. The results disclosed variations in the pattern of rate changes that reflected changes in the physiological state. A linear relationship was found to exist between NADH2 generation and butanol production rate. By coupling an automated measurement system with the fermentation model, on-line estimation of the culture state was accomplished. Based on the AB fermentation model, new parameters were defined for on-line diagnosis of the physiological state and determination of the best timing for amplifying NADH2 generation by the addition of methyl viologen to obtain a high level of butanol productivity. A potential means of achieving optimal control for a high level of solvent production, involving the correlation of certain rates, is proposed.     

On-line estimation of the specific growth rate based on viable biomass measurements: experimental validation

The application of model based control techniques to biotechnological processes is often hampered due to the lack of reliable on-line sensors. This problem can be tackled by the application of software sensors, in which the available hardware measurements are combined with the model equations. The resulting estimates serve as additional measurements useful for process monitoring and control. In this paper, an observer based estimator for the specific growth rate based on on-line viable biomass measurements is studied. Several fed-batch experiments with baker's yeast in a stirred tank bioreactor illustrate the design, tuning, and implementation from a practical point of view. The main contributions of this paper are to illustrate (i) the implementation and validation of the presented algorithm in real-time, (ii) the use of an advanced on-line biomass measurement, and (iii) the design and tuning of the algorithm from a practical point of view. Real-time knowledge of the specific growth rate is important because it yields information on the viability of the cells and it can be used in real-time feedback control algorithms.     

应用动力学方法在线检测Vero细胞培养过程中的摄氧率

流加和灌注培养已被广泛应用于动物细胞培养 ,以获得高活性、高密度的细胞和高的产物得率。在这些培养过程中 ,一般通过离线检测关键参数 (如细胞密度、营养和代谢产物的浓度 )来人为调整灌注速率和补料策略 ,但是 ,当细胞密度较高时 ,由于细胞代谢旺盛使得培养的微环境变化很快 ,这就需要更加频繁快速地调整操作条件 ,从而导致因频繁取样和离线分析所带来的污染危险及大量人力、物力的浪费。这在大规模细胞培养过程中是不可取的。因此 ,要建立大规模、高效动物细胞培养过程 ,有必要研究和探索在线检测技术 ,以实时掌握细胞培养过程所处的状…     

Optimal annual routines: behaviour in the context of physiology and ecology

Organisms in a seasonal environment often schedule activities in a regular way over the year. If we assume that such annual routines have been shaped by natural selection then life-history theory should provide a basis for explaining them. We argue that many life-history trade-offs are mediated by underlying physiological variables that act on various time scales. The dynamics of these variables often preclude considering one period of the year in isolation. In order to capture the essence of annual routines, and many life-history traits, a detailed model of changes in physiological state over the annual cycle is required. We outline a modelling approach based on suitable physiological and ecological state variables that can capture this underlying biology, and describe how models based on this approach can be used to generate a range of insights and predictions.     

Maximizing productivity of a continuous fermenter using nonlinear adaptive optimizing control

The control of a continuously operated fermenter at its maximum productivity level gives rise to a difficult control problem as the location of the optimum operating point changes due to the disturbances. In addition, the fermenter exhibits a change in the sign of the steady state gain near the optimum operating point. This study is aimed at developing an on-line optimizing control scheme that can track the changing location of the steady state optimum so as to maximize the fermenter productivity. A nonlinear Laguerre model, whose parameters are estimated on-line, is used for tracking the optimum operating point. The control at the optimum point is achieved using an adaptive nonlinear MPC strategy that uses the nonlinear Laguerre model for prediction. The efficiency of the proposed algorithm is demonstrated by simulating the control of a continuous fermenter that exhibits shift in the location of the optimum operating point in response to the changes in the maximum specific growth rate. The proposed on-line optimizing control strategy is shown to result in a considerable improvement in the closed loop performance even in the presence of measurement noise.     

Improvement of bioprocess monitoring: development of novel concepts

The advancement of bioprocess monitoring will play a crucial role to meet the future requirements of bioprocess technology. Major issues are the acceleration of process development to reduce the time to the market and to ensure optimal exploitation of the cell factory and further to cope with the requirements of the Process Analytical Technology initiative. Due to the enormous complexity of cellular systems and lack of appropriate sensor systems microbial production processes are still poorly understood. This holds generally true for the most microbial production processes, in particular for the recombinant protein production due to strong interaction between recombinant gene expression and host cell metabolism. Therefore, it is necessary to scrutinise the role of the different cellular compartments in the biosynthesis process in order to develop comprehensive process monitoring concepts by involving the most significant process variables and their interconnections. Although research for the development of novel sensor systems is progressing their applicability in bioprocessing is very limited with respect to on-line and in-situ measurement due to specific requirements of aseptic conditions, high number of analytes, drift, and often rather low physiological relevance. A comprehensive survey of the state of the art of bioprocess monitoring reveals that only a limited number of metabolic variables show a close correlation to the currently explored chemical/physical principles. In order to circumvent this unsatisfying situation mathematical methods are applied to uncover "hidden" information contained in the on-line data and thereby creating correlations to the multitude of highly specific biochemical off-line data. Modelling enables the continuous prediction of otherwise discrete off-line data whereby critical process states can be more easily detected. The challenging issue of this concept is to establish significant on-line and off-line data sets. In this context, online sensor systems are reviewed with respect to commercial availability in combination with the suitability of offline analytical measurement methods. In a case study, the aptitude of the concept to exploit easily available online data for prediction of complex process variables in a recombinant E. coli fed-batch cultivation aiming at the improvement of monitoring capabilities is demonstrated. In addition, the perspectives for model-based process supervision and process control are outlined.     

Fermentation fault management and on-line state estimation using methods of cluster analysis

The results of the cluster analysis of fermentation data are used for the supervision and on-line state estimation. The results of the classification are presented as the average over all fermentation runs belonging to the class as well as the standard deviation. With the help of the class information the on-line fermentation is associated with the best suiting class. Faults in the data such as spikes or total failure of the sensors are detected as the class information automatically supplies tolerance regions for the measurements. In case of a fault a reliable extrapolation for the time of the fault can be calculated. The approach is implemented in the real-time expert system tool G2 and is applied to data of the carbon dioxide evolution rate (CER) of an industrial antibiotic fermentation process.     

人体和动物模型的体表物理信息地形图的研究

对人体头面、躯干、四肢、耳廓各局部几十个及整个人体等体表部位正、背面等210个部位进行超微弱冷光和温度测量,输入电子计算机,经特殊的自编程序处理,获得十分清晰的,由3000多数据构成的各个局部或人体整体的冷光和温度地形图。 对家兔左、右耳廓、胸腹部、背部都分别观察32个部位的冷光与体表温度,经计算机分析处理,每观察区域获得约由2000个数据构成的精确的冷光、温度地形分市图。并可见不同生理、病理状态及不同病程家兔体表冷光、温度等地形图呈有规律的改变。 此外,我们还编制了以体表左右相应对称部位差值为分析数据进行地形图分析的程序,用以人体和动物体表物理信息对称规律的研究。 本工作以图形的形式显示物理参量在体表的广泛的分布规律,以揭示机体内部的不同生理、病理状态。本方法定位准确、直观醒目,为研究体表信息及机体生命活动规律提供了与逐点直接测量方法相互补充的有益的新手段。     

Discovery of multiple level heart-sound morphological variability resulting from changes in physiological states

Heart sounds carry information about the mechanical activity of the cardiovascular system. This information includes the specific physiological state of the subject, and short term variability related to the respiratory cycle. The interpretation of the sounds and extraction of changes in the physiological state, while monitoring short term variability is still an open problem and is the subject of this paper.We present a novel computational framework for analysis of data with multi-level variability, caused by externally induced changes. The framework presented includes an initial clustering of the first heart sound (S1) according to the morphology, and further aggregation of clusters into super-clusters. The clusters and super clusters are two methods of data segmentation, each reflecting a different level of variability in the data.The framework is applied to heart sounds recorded during laparoscopic surgeries of six patients. Procedures of this kind include anesthesia and abdominal insufflation, which together with the respiratory cycle, induce changes to the heart sound signal. We demonstrate a separation of the heart sound morphology according to different physiological states. The physiological states considered are the respiratory cycle, and the stages of the surgery. We achieve results of 90 ± 4% classification accuracy of heart beats to operation stages.The proposed framework is general and can be used to analyze data characterized by multi-level variability for various other (biomedical) applications.     

From inverse problems in mathematical physiology to quantitative differential diagnoses

The improved capacity to acquire quantitative data in a clinical setting has generally failed to improve outcomes in acutely ill patients, suggesting a need for advances in computer-supported data interpretation and decision making. In particular, the application of mathematical models of experimentally elucidated physiological mechanisms could augment the interpretation of quantitative, patient-specific information and help to better target therapy. Yet, such models are typically complex and nonlinear, a reality that often precludes the identification of unique parameters and states of the model that best represent available data. Hypothesizing that this non-uniqueness can convey useful information, we implemented a simplified simulation of a common differential diagnostic process (hypotension in an acute care setting), using a combination of a mathematical model of the cardiovascular system, a stochastic measurement model, and Bayesian inference techniques to quantify parameter and state uncertainty. The output of this procedure is a probability density function on the space of model parameters and initial conditions for a particular patient, based on prior population information together with patient-specific clinical observations. We show that multimodal posterior probability density functions arise naturally, even when unimodal and uninformative priors are used. The peaks of these densities correspond to clinically relevant differential diagnoses and can, in the simplified simulation setting, be constrained to a single diagnosis by assimilating additional observations from dynamical interventions (e.g., fluid challenge). We conclude that the ill-posedness of the inverse problem in quantitative physiology is not merely a technical obstacle, but rather reflects clinical reality and, when addressed adequately in the solution process, provides a novel link between mathematically described physiological knowledge and the clinical concept of differential diagnoses. We outline possible steps toward translating this computational approach to the bedside, to supplement today's evidence-based medicine with a quantitatively founded model-based medicine that integrates mechanistic knowledge with patient-specific information.     

Performance index as an indicator of the physiological state of trees in urban forest ecosystems

Based on the measurements of fluorescence of bark chloroplasts by means of PAM and PEA fluorometers, the information capacity of the methods for assessing the physiological state of Tilia cordata L. from the maximum quantum efficiency of PS II photochemistry (Fv/Fm) and the performance index (PI) has been compared. The measurements were performed on annual shoots of linden trees growing in different environment. It was shown that the chlorophyll content in the bark of shoots growing near the busy urban street was twice less compared with trees growing out of the city. On the trees from the unsafe environment, a small decrease in the relative fluorescence variable (Fv/Fm) was registered, and there was a significant statistical deviation of this value compared to control trees. It was found that the PI and its constituent parameters calculated on the basis of light fluorescence induction curve (PEA-method) are more informative and allow one to recognize changes in the primary energy transformation processes in PS II when they are comparatively small. The results of our work show that PI can be used as a sensitive and a rapid test to evaluate the physiological state of trees and other plant objects even under minor environmental changes.     

On-line determination of optimum time for switching from growth phase to production phase in tissue plasminogen activator production in a microcarrier cell culture

To maximize the productivity of tissue plasminogen activator (TPA) by a mammalian cell culture, on-line determination of the optimum time to switch from the cell growth phase to the TPA production phase was investigated. By measuring the TPA production activity of the cells during the cell growth culture, it was shown that this optimum time was not necessarily the same as the time at which the cell concentration was maximized, and that the optimum time varied with growth culture batch. The TPA production activity of the cells during the growth culture could be estimated by on-line regression analysis using physiological data of the current state, including the oxygen consumption rate (Io₂) and cell concentration, as well as data from past batches. Applying this on-line estimation, the optimum switching time was determined to be the time at which the TPA production activity of the cells in the growth culture became highest, or higher than a certain value according to determined criteria.     

Control of IgG glycosylation by in situ and real-time estimation of specific growth rate of CHO cells cultured in bioreactor

The cell-specific growth rate (µ) is a critical process parameter for antibody production processes performed by animal cell cultures, as it describes the cell growth and reflects the cell physiological state. When there are changes in these parameters, which are indicated by variations of µ, the synthesis and the quality of antibodies are often affected. Therefore, it is essential to monitor and control the variations of µto assure the antibody production and achieve high product quality. In this study, a novel approach for on-line estimation of µ was developed based on the process analytical technology initiative by using an in situ dielectric spectroscopy. Critical moments, such as significant µ decreases, were successfully detected by this method, in association with changes in cell physiology as well as with an accumulation of nonglycosylated antibodies. Thus, this method was used to perform medium renewals at the appropriate time points, maintaining the values of µ close to its maximum. Using this method, we demonstrated that the physiological state of cells remained stable, the quantity and the glycosylation quality of antibodies were assured at the same time, leading to better process performances compared with the reference feed-harvest cell cultures carried out by using off-line nutrient measurements.     

Generally applicable fed-batch culture concept based on the detection of metabolic state by on-line balancing

In many microorganisms, flux limitations in oxidative metabolism lead to the formation of overflow metabolites even under fully aerobic conditions. This can be avoided if the specific growth rate is controlled at a low enough value. This is usually accomplished by controlling the substrate feeding profile in a fed-batch process. The present work proposes a control concept which is based on the on-line detection of metabolic state by on-line calculation of mass and elemental balances. The advantages of this method are: 1) the check of measurement consistency based on all of the available measurements, 2) the minimum requirement of a priori knowledge of metabolism, and 3) the exclusive use of simple and established on-line techniques which do not require direct measurement of the metabolite in question. The control concept has been linked to a simple adaptive controller and applied to fed-batch cultures of S. cerevisiae and E. coli, organisms which express different overflow metabolites, ethanol and acetic acid, respectively. Oxidative and oxidoreductive states of S. cerevisiae and E. coli cultures were detected with high precision. As demonstrated by the formation of acetic acid in E. coli cultures, metabolic states could be correctly distinguished for systems for which traditional methods, such as respiratory quotient (RQ), are insensitive. Hence, it could be shown that the control concept allowed avoidance of overflow metabolite formation and operation at maximum oxidative biomass productivity and oxidative conversion of substrate into biomass. Based on mass and elemental balances, the proposed method additionally provides a richness of additional information, such as yield coefficients and estimation of concentrations and specific conversion rates. These data certainly help the operator to additionally evaluate the state of the process on-line.     

Information-Theoretic Analysis of the Dynamics of an Executable Biological Model

To facilitate analysis and understanding of biological systems, large-scale data are often integrated into models using a variety of mathematical and computational approaches. Such models describe the dynamics of the biological system and can be used to study the changes in the state of the system over time. For many model classes, such as discrete or continuous dynamical systems, there exist appropriate frameworks and tools for analyzing system dynamics. However, the heterogeneous information that encodes and bridges molecular and cellular dynamics, inherent to fine-grained molecular simulation models, presents significant challenges to the study of system dynamics. In this paper, we present an algorithmic information theory based approach for the analysis and interpretation of the dynamics of such executable models of biological systems. We apply a normalized compression distance (NCD) analysis to the state representations of a model that simulates the immune decision making and immune cell behavior. We show that this analysis successfully captures the essential information in the dynamics of the system, which results from a variety of events including proliferation, differentiation, or perturbations such as gene knock-outs. We demonstrate that this approach can be used for the analysis of executable models, regardless of the modeling framework, and for making experimentally quantifiable predictions.