Automatic bioprocess control. 2. Implementations and practical experiences.

Our improved implementation for bioprocess control allows flexible responses to many process needs. It is based on computer equipment consisting of three hierarchically ordered levels. On the lowest level, a DDC slave computer handles setpoints and simple tasks generating the chemical and physical environment for the cells. It can be designed manually by the user or automatically by the supervisory computer on the second level. This provides for raw data organization, analysis and interpretation either to support personnel on line in decision making, to select predefined control strategies, or even to search for others. In the coordinating computer on the third level, common tasks of different supervisory computers (bioprocesses) are shared, saving money for the equipment. Tasks and concepts as well as experimental experiences are described to outline the capabilities of the configuration.     

A systematic concept in bioprocess analysis and design

A systematic concept in bioprocess analysis and design is presented according to an integrating strategy as basis for a biotechnological methodology. A comparison illustrates analogies and significant differences between chemical and biological processes. The study of the interactions between environments and organisms includes influences of physical transport phenomena and also enzyme and metabolic regulations. In both cases the macroscopic principle with a formalkinetic approach is recommended for quantification purposes. The determination of the characteristic-time-regime with characteristic rate constants makesit possible to simplify mathematical modeling using the concepts of the rate-determining-step and quasistationarity. Finally, guidelines are summarized for the use of unstructured and structured kinetic models.     

Near-infrared spectroscopy for bioprocess monitoring and control.

This article describes the calibration of a spectroscopic scanning instrument for the measurement of selected contaminants in a complex biological process stream. Its use is for the monitoring of a process in which contaminants are to be removed selectively by flocculation from yeast cell homogenate. The main contaminants are cell debris, protein, and RNA. A low-cost instrument has been developed for sensitivity in the region of the NIR spectrum (from 1900 to 2500 nm) where preliminary work found NIR signatures from cell debris, protein, and RNA. Calibration models have been derived using a multivariate method for concentrations of these contaminants, such as would be found after the flocculation process. Two strategies were compared for calibrating the NIR instrument. In one case, samples were prepared by adding materials representative of the contaminants to clarified yeast homogenate so the contaminant levels were well known but outside the range of interest. In the other case, where samples were like those from the process stream after flocculation and floc removal, there was uncertainty of analysis of contaminant level, but the calibration was in the range of interest. Calibration using process stream samples gave results close to those derived from traditional assays. When the calibration models were used to predict the contaminant concentrations in previously unseen samples, the correlation coefficients between measurements and predictions were above 90% in all cases but one. The prediction errors were similar to the errors in the traditional assays.     

Model-assisted Design of Experiments as a concept for knowledge-based bioprocess development

Bioprocess and Biosystems Engineering - Design of Experiments methods offer systematic tools for bioprocess development in Quality by Design, but their major drawback is the user-defined choice of...     

工业生物过程智能控制原理和方法进展

工业生物过程是一个复杂的系统过程,对活体细胞代谢过程的认识是实现高效工业生物制造的基础。文中首先综述了工业发酵过程多尺度优化控制原理和实践,包括多尺度理论与装备、细胞宏观代谢在线检测传感技术以及生理代谢参数相关分析。在此基础上,对工业生物过程智能控制——感知细胞内生理代谢特性新型传感技术、大数据库建立和数据深度计算以及生物过程智能决策进行了综述和展望。     

Present and potential applications of mass spectrometry for bioprocess research and control.

For on-line monitoring of bioprocesses present applications are mainly restricted to gas analysis, but several techniques have been improved recently: membrane probes, the application of MS/MS techniques, methods of correlating available on-line data like gas reaction rates with bioprocess characteristics using stoichiometric models and other empirical correlations. New ionization and ion separation methods for biomolecules are developing dramatically. Most striking developments in this area are improved desorption techniques, electrospray, the renaissance of time-of-flight instruments and new challenges in ion trap techniques. Enormous progress is made in the analysis of peptides and other biopolymers. Combinations with new separation techniques like capillary electrophoresis and capillary HPLC show new horizons in biomolecule analysis.     

Flow injection analysis and in-line biosensors for bioprocess control: a comparison.

Miniaturization will unify the different approaches chosen for the application of biosensors in bioprocess control. The most versatile system, which in our opinion is flow injection analysis will be the method of choice for the introduction of biosensors in bioprocess control. A lot of experience will be gained for the future development of miniaturized total chemical analysis systems.     

Theory of V.A. Dogiel on polymerization and oligomerization as a general integration concept

The theory of V.A. Dogiel on the significance of polymerization and oligomerization processes in the evolution of Protozoa and Metazoa is compared with the paper of I.I. Schmalhausen (1972) on factors and steps of aromorph evolution. Dogiel’s theory is considered as a general integration conception. Four steps are distinguished in the evolution of biological systems: (1) formation of morphofunctional system by units of the lower structural level, (2) polymerization of morphofunctional units of a system, (3) oligomerization of morphofunctional units of system by means of their reduction, uniting, or differentiation, (4) integration and stabilization of a system owing to development of morphofunctional connections between its parts.     

Microfluidic biolector—microfluidic bioprocess control in microtiter plates

In industrial‐scale biotechnological processes, the active control of the pH‐value combined with the controlled feeding of substrate solutions (fed‐batch) is the standard strategy to cultivate both prokaryotic and eukaryotic cells. On the contrary, for small‐scale cultivations, much simpler batch experiments with no process control are performed. This lack of process control often hinders researchers to scale‐up and scale‐down fermentation experiments, because the microbial metabolism and thereby the growth and production kinetics drastically changes depending on the cultivation strategy applied. While small‐scale batches are typically performed highly parallel and in high throughput, large‐scale cultivations demand sophisticated equipment for process control which is in most cases costly and difficult to handle. Currently, there is no technical system on the market that realizes simple process control in high throughput. The novel concept of a microfermentation system described in this work combines a fiber‐optic online‐monitoring device for microtiter plates (MTPs)—the BioLector technology—together with microfluidic control of cultivation processes in volumes below 1 mL. In the microfluidic chip, a micropump is integrated to realize distinct substrate flow rates during fed‐batch cultivation in microscale. Hence, a cultivation system with several distinct advantages could be established: (1) high information output on a microscale; (2) many experiments can be performed in parallel and be automated using MTPs; (3) this system is user‐friendly and can easily be transferred to a disposable single‐use system. This article elucidates this new concept and illustrates applications in fermentations of Escherichia coli under pH‐controlled and fed‐batch conditions in shaken MTPs. Biotechnol. Bioeng. 2010;107: 497–505. © 2010 Wiley Periodicals, Inc.     

A simple model-based control for Pichia pastoris allows a more efficient heterologous protein production bioprocess

A predictive control algorithm coupled with a PI feedback controller has been satisfactorily implemented in the heterologous Rhizopus oryzae lipase production by Pichia pastoris methanol utilization slow (Mut(s)) phenotype. This control algorithm has allowed the study of the effect of methanol concentration, ranging from 0.5 to 1.75 g/L, on heterologous protein production. The maximal lipolytic activity (490 UA/mL), specific yield (11,236 UA/g(biomass)), productivity (4,901 UA/L . h), and specific productivity (112 UA/g(biomass)h were reached for a methanol concentration of 1 g/L. These parameters are almost double than those obtained with a manual control at a similar methanol set-point. The study of the specific growth, consumption, and production rates showed different patterns for these rates depending on the methanol concentration set-point. Results obtained have shown the need of implementing a robust control scheme when reproducible quality and productivity are sought. It has been demonstrated that the model-based control proposed here is a very efficient, robust, and easy-to-implement strategy from an industrial application point of view.     

Progress toward a general species concept

New insights in the speciation process and the nature of "species" that accumulated in the past decade demand adjustments of the species concept. The standing of some of the most broadly accepted or most innovative species concepts in the light of the growing evidence that reproductive barriers are semipermeable to gene flow, that species can differentiate despite ongoing interbreeding, that a single species can originate polyphyletically by parallel evolution, and that uniparental organisms are organised in units that resemble species of biparental organisms is discussed. As a synthesis of ideas in existing concepts and the new insights, a generalization of the genic concept is proposed that defines species as groups of individuals that are reciprocally characterized by features that would have negative fitness effects in other groups and that cannot be regularly exchanged between groups upon contact. The benefits of this differential fitness species concept are that it classifies groups that keep differentiated and keep on differentiating despite interbreeding as species, that it is not restricted to specific mutations or mechanisms causing speciation, and that it can be applied to the whole spectrum of organisms from uni- to biparentals.     

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.     

Development of a versatile computer integrated control system for bioprocess controls

A general approach is described for the implementation of a networked multi-unit computer integrated control system. The use of data acquisition hardware and graphical programming tools alleviates tedious programming and maintains potency and flexibility. One application of the control system, the control of a mammalian cell perfusion culture based on a key nutrient glucose concentration, was demonstrated. The control system offers customized user interface for all process control parameters and allows the flexibility for continued improvement and implementation of new tailored functions. The temperature, pH, dissolved oxygen and glucose level were accurately controlled. This revised version was published online in June 2006 with corrections to the Cover Date.     

A systems approach to plant bioprocess optimization

A dynamic model for plant cell metabolism was used as a basis for a rational analysis of plant production potential in in vitro cultures. The model was calibrated with data from 3-L bioreactor cultures. A dynamic sensitivity analysis framework was developed to analyse the response curves of secondary metabolite production to metabolic and medium perturbations. Simulation results suggest that a straightforward engineering of cell metabolism or medium composition might only have a limited effect on productivity. To circumvent the problem of the dynamic allocation of resources between growth and production pathways, the sensitivity analysis framework was used to assess the effect of stabilizing intracellular nutrient concentrations. Simulations showed that a stabilization of intracellular glucose and nitrogen reserves could lead to a 116% increase in the specific production of secondary metabolites compared with standard culture protocol. This culture strategy was implemented experimentally using a perfusion bioreactor. To stabilize intracellular concentrations, adaptive medium feeding was performed using model mass balances and estimations. This allowed for a completely automated culture, with controlled conditions and pre-defined decision making algorithm. The proposed culture strategy leads to a 73% increase in specific production and a 129% increase in total production, as compared with a standard batch culture protocol. The sensitivity analysis on a mathematical model of plant metabolism thus allowed producing new insights on the links between intracellular nutritional management and cell productivity. The experimental implementation was also a significant improvement on current plant bioprocess strategies.     

Applications of modelling for bioprocess design and control in industrial production

The on-line measurement of the relevant parameters and the control conception for three production processes for fine chemicals by fermentation and biotransformation at the 15 m³ scale were developed. The models describe the bioprocesses which successfully result in fully automated manufacturing steps. Modelling also proved to be a valuable tool for a better insight into biochemical fundamentals of the processes. Moreover, proper use of data logging, modelling and process control was important for quality, since two processes were controlled on-line and quality relevant deviations were registered early. Finally, combining modelling with simulation, we could drastically reduce both development time and cost.List of Symbols F l/h flux - V l volume - U ₀ g/l nicotinonitrile concentration influx - U g/l actual nicotinonitrile concentration - q _ug/gh specific educt (=nicotinonitrile) transformation rate - x g/l biocatalyst concentration - p ₀ g/l nicotinamide concentration influx - p g/l actual nicotinamide concentration - q _pg/gh specific product (=nicotinamide) formation rate - k parameter loss of activity - q _{u, max}g/gh max. specific educt transformation rate - K _ug/l saturation constant for nicotinonitrile - K _ig/l inhibition constant for nicotinonitrile - K _iig/l inhibition constant for nicotinamide - MW _Ag/mol molecular weight for nicotinonitrile - MW _Bg/mol molecular weight for nicotinamide - NS Nicotinic acid - 6-HNS 6-Hydroxynicotinic acid - r _{NS, 6HNS} g/lh 6-HNS production rate - r _{6HNS, X} g/lh biomass production rate - r _{NS, 6HNS, max} g/lh max. 6-HNS production rate - S _NS g/l actual NS concentration - K _{S, NS} g/l saturation constant for NS - K _{i, 6HNS} g/l inhibition constant for 6-HNS - K _o2 g/l saturation constant for oxygen - r _{6HNS, X, max} g/lh max. biomass production rate - S _6HNS g/l actual 6-HNS concentration - K _{ii, NS} g/l inhibition constant for NS - RQ mol/mol respiration quotient - S _xylg/l actual xylene concentration - K _{i, xyl}g/ inhibition constant for xylene - K _{i, DMPY}g/ inhibition constant for 2,5-dimethylpyrazine - r _Xg/lh biomass production rate - r _{X, max}g/lh max. biomass production rate - K _{s, xyl}g/l saturation constant for xylene - S _DMPYg/l actual concentration of DMPY - K _{i, MPCA}g/ inhibition constant for MPCA - K _O2g/ saturation constant for oxygen - S _MPCAg/l actual MPCA concentration - S _O2g/l actual oxygen concentration - r _MPCAg/lh MPCA production rate - r _{MPCA, max}g/lh max. MPCA production rate - k _lgl inhibition constant for the intermediates - k _{s, DMPY}gl saturation constant for DMPY