The way to adequate control of microbial processes passes via real-time knowledge-based supervision.

Knowledge-based supervision is viewed as a major tool for achieving high-performance control of microbial processes. By providing an adequate insight into the integral state of the cell culture, knowledge-based supervisory systems allow for monitoring and handling various important phenomena which usually remain outside the scope of the conventional control approach. The present paper focuses on the development of a computer system for knowledge-based supervision of bioprocesses. Its application to the control of fed-batch cultivation of recombinant Escherichia coli for phenylalanine production is also discussed.     

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.     

Analyzing and understanding the robustness of bioprocesses

The robustness of bioprocesses is becoming increasingly important. The main driving forces of this development are, in particular, increasing demands on product purities as well as economic aspects. In general, bioprocesses exhibit extremely high complexity and variability. Biological systems often have a much higher intrinsic variability compared with chemical processes, which makes the development and characterization of robust processes tedious task. To predict and control robustness, a clear understanding of interactions between input and output variables is necessary. Robust bioprocesses can be realized, for example, by using advanced control strategies for the different unit operations. In this review, we discuss the different biological, technical, and mathematical tools for the analysis and control of bioprocess robustness.     

Regulatory and metabolic network of rhamnolipid biosynthesis: Traditional and advanced engineering towards biotechnological production

During the last decade, the demand for economical and sustainable bioprocesses replacing petrochemical-derived products has significantly increased. Rhamnolipids are interesting biosurfactants that might possess a broad industrial application range. However, despite of 60 years of research in the area of rhamnolipid production, the economic feasibility of these glycolipids is pending. Although the biosynthesis and regulatory network are in a big part known, the actual incidents on the cellular and process level during bioreactor cultivation are not mastered. Traditional engineering by random and targeted genetic alteration, process design, and recombinant strategies did not succeed by now. For enhanced process development, there is an urgent need of in-depth information about the rhamnolipid production regulation during bioreactor cultivation to design knowledge-based genetic and process engineering strategies. Rhamnolipids are structurally comparable, simple secondary metabolites and thus have the potential to become instrumental in future secondary metabolite engineering by systems biotechnology. This review summarizes current knowledge about the regulatory and metabolic network of rhamnolipid synthesis and discusses traditional and advanced engineering strategies performed for rhamnolipid production improvement focusing on Pseudomonas aeruginosa. Finally, the opportunities of applying the systems biotechnology toolbox on the whole-cell biocatalyst and bioprocess level for further rhamnolipid production optimization are discussed.     

Two-phase partitioning bioreactors in fermentation technology

The two-phase partitioning bioreactor concept appears to have a great potential in enhancing the productivity of many bioprocesses. The proper selection of an organic solvent is the key to successful application of this approach in industrial practice. The integration of fermentation and a primary product separation step has a positive impact on the productivity of many fermentation processes. The controlled substrate delivery from the organic to the aqueous phase opens a new area of application of this strategy to biodegradation of xenobiotics. In this review, the most recent advances in the application of two-liquid phase partitioning bioreactors for product or substrate partitioning are discussed. Modeling and performance optimization studies related to those bioreactor systems are also reviewed.     

Real-time dissolved carbon dioxide monitoring I: Application of a novel in situ sensor for CO2 monitoring and control

Dissolved carbon dioxide (dCO₂) is a well-known critical parameter in bioprocesses due to its significant impact on cell metabolism and on product quality attributes. Processes run at small-scale faces many challenges due to limited options for modular sensors for online monitoring and control. Traditional sensors are bulky, costly, and invasive in nature and do not fit in small-scale systems. In this study, we present the implementation of a novel, rate-based technique for real-time monitoring of dCO₂ in bioprocesses. A silicone sampling probe that allows the diffusion of CO₂ through its wall was inserted inside a shake flask/bioreactor and then flushed with air to remove the CO₂ that had diffused into the probe from the culture broth (sensor was calibrated using air as zero-point calibration). The gas inside the probe was then allowed to recirculate through gas-impermeable tubing to a CO₂ monitor. We have shown that by measuring the initial diffusion rate of CO₂ into the sampling probe we were able to determine the partial pressure of the dCO₂ in the culture. This technique can be readily automated, and measurements can be made in minutes. Demonstration experiments conducted with baker's yeast and Yarrowia lipolytica yeast cells in both shake flasks and mini bioreactors showed that it can monitor dCO₂ in real-time. Using the proposed sensor, we successfully implemented a dCO₂-based control scheme, which resulted in significant improvement in process performance.     

New developments in solid state fermentation: I-bioprocesses and products

The last decade has witnessed an unprecedented increase in interest in solid state fermentation (SSF) for the development of bioprocesses, such as bioremediation and biodegradation of hazardous compounds, biological detoxification of agro-industrial residues, biotransformation of crops and crop-residues for nutritional enrichment, biopulping, and production of value-added products, such as biologically active secondary metabolites, including antibiotics, alkaloids, plant growth factors, etc. enzymes, organic acids, biopesticides, including mycopesticides and bioherbicides, biosurfactants, biofuel, aroma compounds, etc. SSF systems, which during the previous two decades were termed as a ‘low-technology’ systems, appear to be a promising one for the production of value-added ‘low volume-high cost’ products such as biopharmaceuticals. SSF processes offer potential advantages in bioremediation and biological detoxification of hazardous and toxic compounds. With the advent of biotechnological innovations, mainly in the area of enzyme and fermentation technology, many new avenues have opened for the application of SSF. This review discusses more recent developments in the area of SSF leading to the developments of bioprocesses and products.     

Recent developments in the monitoring,modeling and control of biological production systems

Current trends in the development of methods for monitoring, modeling and controlling biological production systems are reviewed from a bioengineering perspective. The ability to measure intracellular conditions in bioprocesses using genomics and other bioinformatics tools is addressed. Devices provided via micromachining techniques and new real-time optical technology are other novel methods that may facilitate biosystem engineering. Mathematical modeling of data obtained from bioinformatics or real-time monitoring methods are necessary in order to handle the dense flows of data that are generated. Furthermore, control methods must be able to cope with these data flows in efficient ways that can be implemented in plant-wide computer communication systems.Mini-review for the proceedings of the M3C conference     

Multilayer adaptive control of continuous bioprocesses using optimising control technique. Case study: Bakers' yeast culture

High costs associated with many fermentation processes in an increasingly competitive industry make any prompt application of modern control techniques to industrial bioprocesses very desirable. However, this is often hampered by the lack of adequate mathematical models, on the one hand, and by the absence of continuous, on-line measurement of the most relevant process variables, on the other hand. This paper addresses these problems and offers a new strategy to control continuous bioprocesses using a hierarchical structure such that neither structured process models nor continuous measurement of all relevant variables have to be available. The control system consists of two layers. The lower layer represents a dynamic adaptive follow-up control of a continuously measured output — in our case dissolved oxygen concentration. This variable is supposed to be strongly correlated with the key output variable — in our case cellular concentration which is not continuously available for measurement. The higher layer is then designed to maintain a desired profile of the process key output using a set-point optimising control technique. The Integrated System Optimisation and Parameter Estimation method used operates on an appropriately chosen steady-state performance criterion. A prerequisite for successful application of the proposed approach is an approximate steady-state model, describing the relationship between the measured output and the process key output variable. Furthermore, occasional in situ, off-line or laboratory measurement values of the key output variable are needed. Promising simulation results of the biomass concentration control, by manipulating the air flow-rate in the continuous bakers' yeast culture are presented.     

Enzyme sensor-FIA-system for on-line monitoring of glucose, lactate and glutamine in animal cell cultures.

Enzyme sensors for glucose, lactate and glutamine were connected via flow-injection analysis (FIA) devices to two different bioprocesses. They were used for on-line process control of perfused bioreactor systems containing mammalian cell lines producing a monoclonal antibody and recombinant interleukin-2. The biosensor system gives direct access to important process data which can be used as control parameters for long term cell cultivation systems.     

Hybrid-EKF: Hybrid model coupled with extended Kalman filter for real-time monitoring and control of mammalian cell culture

In a decade when Industry 4.0 and quality by design are major technology drivers of biopharma, automated and adaptive process monitoring and control are inevitable requirements and model-based solutions are key enablers in fulfilling these goals. Despite strong advancement in process digitalization, in most cases, the generated datasets are not sufficient for relying on purely data-driven methods, whereas the underlying complex bioprocesses are still not completely understood. In this regard, hybrid models are emerging as a timely pragmatic solution to synergistically combine available process data and mechanistic understanding. In this study, we show a novel application of the hybrid-EKF framework, that is, hybrid models coupled with an extended Kalman filter for real-time monitoring, control, and automated decision-making in mammalian cell culture processing. We show that, in the considered application, the predictive monitoring accuracy of such a framework improves by at least 35% when developed with hybrid models with respect to industrial benchmark tools based on PLS models. In addition, we also highlight the advantages of this approach in industrial applications related to conditional process feeding and process monitoring. With regard to the latter, for an industrial use case, we demonstrate that the application of hybrid-EKF as a soft sensor for titer shows a 50% improvement in prediction accuracy compared with state-of-the-art soft sensor tools.     

Dynamic optimization of bioprocesses: efficient and robust numerical strategies

The dynamic optimization (open loop optimal control) of non-linear bioprocesses is considered in this contribution. These processes can be described by sets of non-linear differential and algebraic equations (DAEs), usually subject to constraints in the state and control variables. A review of the available solution techniques for this class of problems is presented, highlighting the numerical difficulties arising from the non-linear, constrained and often discontinuous nature of these systems. In order to surmount these difficulties, we present several alternative stochastic and hybrid techniques based on the control vector parameterization (CVP) approach. The CVP approach is a direct method which transforms the original problem into a non-linear programming (NLP) problem, which must be solved by a suitable (efficient and robust) solver. In particular, a hybrid technique uses a first global optimization phase followed by a fast second phase based on a local deterministic method, so it can handle the nonconvexity of many of these NLPs. The efficiency and robustness of these techniques is illustrated by solving several challenging case studies regarding the optimal control of fed-batch bioreactors and other bioprocesses. In order to fairly evaluate their advantages, a careful and critical comparison with several other direct approaches is provided. The results indicate that the two-phase hybrid approach presents the best compromise between robustness and efficiency.     

The autolysis of industrial filamentous fungi

Fungal autolysis is the natural process of self-digestion of aged hyphal cultures, occurring as a result of hydrolase activity, causing vacuolation and disruption of organelle and cell wall structure. Previously, authors have considered individual aspects of fungal lysis, in terms of either an enzyme, a process or an organism. This review considers both the physiology and morphology of fungal autolysis, with an emphasis on correlations between enzymological profiles and the morphological changes occurring during culture degeneration. The involvement of the main groups of autolytic hydrolases is examined (i.e., proteases, glucanases, and chitinases), in addition to the effects of autolysis on the morphology and products of industrial bioprocesses. We call for a concerted approach to the study of autolysis, as this will be fundamental for research to progress in this field. Increased understanding will allow for greater control of the prevention, or induction of fungal autolysis. Such advances will be applicable in the development of antifungal medicines and enable increased productivity and yields in industrial bioprocesses. Using paradigms in existing model systems, including mammalian cell death and aging in yeast, areas for future study are suggested in order to advance the study of fungal cell death.     

Control strategies of continuous bioprocesses based on biological activities

Based on the material balance principle applied to microbial reactions in continuous bioprocesses, the concept of reaction rate control has been developed theoretically. This concept provides a more direct way of controlling biological activities than the control of physical or chemical parameters in practice today. From an analysis of dynamic and steady-state experiments, two control systems for carbon dioxide production rate control during the continuous culture of baker's yeast have been designed and evaluated experimentally. In these control methods, intracellular NADH concentration is used as an immediate indication of the onset of glucose repression. A more sophisticated master controller based on the respiratory quotient can be combined with these control methods. The resulting control system provides a means to indirectly optimize biomass production while preventing ethanol formation in the continuous culture of baker's yeast.     

Motion control of musculoskeletal systems with redundancy

Motion control of musculoskeletal systems for functional electrical stimulation (FES) is a challenging problem due to the inherent complexity of the systems. These include being highly nonlinear, strongly coupled, time-varying, time-delayed, and redundant. The redundancy in particular makes it difficult to find an inverse model of the system for control purposes. We have developed a control system for multiple input multiple output (MIMO) redundant musculoskeletal systems with little prior information. The proposed method separates the steady-state properties from the dynamic properties. The dynamic control uses a steady-state inverse model and is implemented with both a PID controller for disturbance rejection and an artificial neural network (ANN) feedforward controller for fast trajectory tracking. A mechanism to control the sum of the muscle excitation levels is also included. To test the performance of the proposed control system, a two degree of freedom ankle–subtalar joint model with eight muscles was used. The simulation results show that separation of steady-state and dynamic control allow small output tracking errors for different reference trajectories such as pseudo-step, sinusoidal and filtered random signals. The proposed control method also demonstrated robustness against system parameter and controller parameter variations. A possible application of this control algorithm is FES control using multiple contact cuff electrodes where mathematical modeling is not feasible and the redundancy makes the control of dynamic movement difficult.     

A new microfluidic concept for parallel operated milliliter-scale stirred tank bioreactors

Parallel miniaturized stirred tank bioreactors are an efficient tool for "high-throughput bioprocess design." As most industrial bioprocesses are pH-controlled and/or are operated in a fed-batch mode, an exact scale-down of these reactions with continuous dosing of fluids into the miniaturized bioreactors is highly desirable. Here, we present the development, characterization, and application of a novel concept for a highly integrated microfluidic device for a bioreaction block with 48 parallel milliliter-scale stirred tank reactors (V = 12 mL). The device consists of an autoclavable fluidic section to dispense up to three liquids individually per reactor. The fluidic section contains 144 membrane pumps, which are magnetically driven by a clamped-on actuator section. The micropumps are designed to dose 1.6 μL per pump lift. Each micropump enables a continuous addition of liquid with a flow rate of up to 3 mL h(-1) . Viscous liquids up to a viscosity of 8.2 mPa s (corresponds to a 60% v/v glycerine solution) can be pumped without changes in the flow rates. Thus, nearly all feeding solutions can be delivered, which are commonly used in bioprocesses. The functionality of the first prototype of this microfluidic device was demonstrated by double-sided pH-controlled cultivations of Saccharomyces cerevisiae based on signals of fluorimetric sensors embedded at the bottom of the bioreactors. Furthermore, fed-batch cultivations with constant and exponential feeding profiles were successfully performed. Thus, the presented novel microfluidic device will be a useful tool for parallel and, thus, efficient optimization of controlled fed-batch bioprocesses in small-scale stirred tank bioreactors. This can help to reduce bioprocess development times drastically.     

Biocatalysis: applications and potentials for the chemical industry

The chemical industry is exploring the use of renewable feed stocks to improve sustainability, prompting the exploration of bioprocesses for the production of chemicals. Attractive features of biological systems include versatility, substrate selectivity, regioselectivity, chemoselectivity, enantioselectivity and catalysis at ambient temperatures and pressures. However, a challenge facing bioprocesses is cost competitiveness with chemical processes because capital assets associated with the existing commercial processes are high. The chemical industry will probably use biotechnology with existing feed stocks and processes to extract higher values from feed stocks, process by-products and waste streams. In this decade, bioprocesses that offer either a process or a product advantage over traditional chemical routes will become more widely used.     

红外光谱技术在生物过程监测中的应用

在线监测化学组分的浓度对许多生物过程都是十分必要的。然而,探头需耐高温灭菌的要求和生物体系自身的复杂性给许多分析技术的在线监测带来了困难。近几年,随仪器和数据处理技术的迅速发展,应用红外光谱技术对生物过程的原位或在线监测日益广泛。本文对红外过程分析技术进行了较全面的综述,介绍了红外分析的原理、进展及在生物过程监测中的应用。     

Stable adaptive algorithm for simultaneous estimation of time-varying parameters and state variables in aerobic bioprocesses

The application of modern model based control algorithms in the bioprocesses is hampered by the lack of accurate and cheap on-line sensors, capable of providing on-line measurements of the main process variables and parameters. In this paper, a new approach for estimation of immeasurable time-varying parameters and state variable is presented for a class of aerobic bioprocesses using only on-line measurements of the oxygen uptake rate. The approach consists in the design of a new parameter estimator of biomass growth rate and yield coefficient for oxygen consumption on the basis of the theory of adaptive estimation. The dynamical equation of the measurable reaction rate, oxygen uptake rate, is presented as a linear one with respect to the biomass growth rate and the yield coefficient for oxygen consumption. In this way, the structure of the proposed estimator becomes linear time-varying one. After some mathematical transformations, that structure is presented in a form, allowing to be derived the stability conditions using some theoretical results concerning the stability of adaptive observers. The estimates of the yield coefficient for oxygen consumption, the biomass concentration and specific growth rate are obtained then on the basis of the generated estimates using well known kinetic models of bioprocesses. With respect to previous similar approaches, the new estimation algorithm gives stable estimates not only of immeasurable state variable and reaction rates but likewise of an yield coefficient. The behavior of the proposed estimator is studied under inexact initial conditions, step changes of dilution rate and in the presence of measurement noise by simulations using a process model, which belongs to the investigated class of bioprocesses.