Design of methanol Feed control in Pichia pastoris fermentations based upon a growth model

The methylotrophic yeast Pichia pastoris is an effective system for recombinant protein productions that utilizes methanol as an inducer, and also as carbon and energy source for a Mut(+) (methanol utilization plus) strain. Pichia fermentation is conducted in a fed-batch mode to obtain a high cell density for a high productivity. An accurate methanol control is required in the methanol fed-batch phase (induction phase) in the fermentation. A simple "on-off" control strategy is inadequate for precise control of methanol concentrations in the fermentor. In this paper we employed a PID (proportional, integral and derivative) control system for the methanol concentration control and designed the PID controller settings on the basis of a Pichia growth model. The closed-loop system was built with four components: PID controller, methanol feed pump, fermentation process, and methanol sensor. First, modeling and transfer functions for all components were derived, followed by frequency response analysis, a powerful method for calculating the optimal PID parameters K(c) (controller gain), tau(I) (controller integral time constant), and tau(D) (controller derivative time constant). Bode stability criteria were used to develop the stability diagram for evaluating the designed settings during the entire methanol fed-batch phase. Fermentations were conducted using four Pichia strains, each expressing a different protein, to verify the control performance with optimal PID settings. The results showed that the methanol concentration matched the set point very well with only small overshoot when the set point was switched, which indicated that a very good control performance was achieved. The method developed in this paper is robust and can serve as a framework for the design of other PID feedback control systems in biological processes.     

Model-based development of neuroprosthesis for paraplegic patients.

In paraplegic patients with upper motor neuron lesions the signal path from the central nervous system to the muscles is interrupted. Functional electrical stimulation applied to the lower motor neurons can replace the lacking signals. A so-called neuroprosthesis may be used to restore motor function in paraplegic patients on the basis of functional electrical stimulation. However, the control of multiple joints is difficult due to the complexity, nonlinearity, and time-variance of the system involved. Furthermore, effects such as muscle fatigue, spasticity, and limited force in the stimulated muscle further complicate the control task. Mathematical models of the human musculoskeletal system can support the development of neuroprosthesis. In this article a detailed overview of the existing work in the literature is given and two examples developed by the author are presented that give an insight into model-based development of neuroprosthesis for paraplegic patients. It is shown that modelling the musculoskeletal system can provide better understanding of muscular force production and movement coordination principles. Models can also be used to design and test stimulation patterns and feedback control strategies. Additionally, model components can be implemented in a controller to improve control performance. Eventually, the use of musculoskeletal models for neuroprosthesis design may help to avoid internal disturbances such as fatigue and optimize muscular force output. Furthermore, better controller quality can be obtained than in previous empirical approaches. In addition, the number of experimental tests to be performed with human subjects can be reduced. It is concluded that mathematical models play an increasing role in the development of reliable closed-loop controlled, lower extremity neuroprostheses.     

Properties of central control of body temperature in the rabbit

The structure of the central temperature controller in rabbits has been analysed. On the one hand, experiments were carried out to obtain the necessary data for system analysis; on the other hand, a mathematical model of the passive system was developed which describes the thermal characteristics of the body in accordance with the experimental results. In applying the model, different controller equations for the effector mechanisms involved were tested to fit the experimental data best. They are compared with already existing models of metabolic control. In addition, mechanisms of the effector coordination are discussed. It is shown that the three effectors make use of a similar controller structure thet feeds core temperature as well as skin temperature back into the controller. The system is insensitive to variations of the controller gains, whereas a slight change in the controller reference temperature causes significant changes of the controlled core temperature. Furthermore it is shown that any mutual effector blockings are dispensible.     

Risk-Sensitive Optimal Feedback Control Accounts for Sensorimotor Behavior under Uncertainty

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models.     

An optimal control model of diabetes mellitus

Engineering optimal control theory is applied to equations describing insulin and glucose interactions. The nature of the optimal controller is established. It is shown how the results can be utilized in a closed loop feedback control system.     

Automatic control of biomass gasifiers using fuzzy inference systems

A fuzzy controller for biomass gasifiers is proposed. Although fuzzy inference systems do not need models to be tuned, a plant model is proposed which has turned out very useful to prove different combinations of membership functions and rules in the proposed fuzzy control. The global control scheme is shown, including the elements to generate the set points for the process variables automatically. There, the type of biomass and its moisture content are the only data which need to be introduced to the controller by a human operator at the beginning of operation to make it work autonomously. The advantages and good performance of the fuzzy controller with the automatic generation of set points, compared to controllers utilising fixed parameters, are demonstrated.     

Hybrid adaptive optimal control of anaerobic fluidized bed bioreactor for the de-icing waste treatment

Hybrid adaptive control strategy was developed and tested for the degradation of propylene glycol, a major component in de-icing waste, in an anaerobic fluidized bed bioreactor (AFBR). A linearized model with time-varying parameters was first employed to describe the dynamic behavior of the AFBR using a recursive off-line system identification method. A hybrid adaptive control strategy was then tested using a recursive off-line system identification routine followed by an on-line adaptive optimal control algorithm. The objective of the controller was to achieve the desired set point value of the propionate concentration (stand-alone control output variable) by manipulating the dilution rate (control input variable). To do so, the optimal control law was developed by minimizing a cost function with constraint equations. This novel idea was successfully applied to the underlying system for 200 h. The set point (700 mg HPrl(-1)) was achieved even in the case where the feed concentration suddenly increased by 50% (9000 mg HPrl(-1) to 13500 mg HPrl(-1)).     

Winching up heavy loads with a compliant arm: a new local joint controller

A closed kinematic chain, like an arm that operates a crank, has a constrained movement space. A meaningful movement of the chain’s endpoint is only possible along the free movement directions which are given implicitly by the contour of the object that confines the movement of the chain. Many technical solutions for such a movement task, in particular those used in robotics, use central controllers and force–torque sensors in the arm’s wrist or a leg’s ankle to construct a coordinate system (task frame formalism) at the local point of contact the axes of which coincide with the free and constrained movement directions. Motivated by examples from biology, we introduce a new control system that solves a constrained movement task. The control system is inspired by the control architecture that is found in stick insects like Carausius morosus. It consists of decentral joint controllers that work on elastic joints (compliant manipulator). The decentral controllers are based on local positive velocity feedback (LPVF). It has been shown earlier that LPVF enables contour following of a limb in a compliant motion task without a central controller. In this paper we extend LPVF in such a way that it is even able to follow a contour if a considerable counter force drags the limb away along the contour in a direction opposite to the desired. The controller extension is based on the measurement of the local mechanical power generated in the elastic joint and is called power-controlled relaxation LPVF. The new control approach has the following advantages. First, it still uses local joint controllers without knowledge of the kinematics. Second, it does not need a force or torque measurement at the end of the limb. In this paper we test power-controlled relaxation LPVF on a crank turning task in which a weight has to be winched up by a two-joint compliant manipulator.     

Kalman filter based glucose control at small set points during fed-batch cultivation of Saccharomyces cerevisiae

A glucose control system is presented, which is able to control cultivations of Saccharomyces cerevisiae even at low glucose concentrations. Glucose concentrations are determined using a special flow injection analysis (FIA) system, which does not require a sampling module. An extended Kalman filter is employed for smoothing the glucose measurements as well as for the prediction of glucose and biomass concentration, the maximum specific growth rate, and the volume of the culture broth. The predicted values are utilized for feedforward/feedback control of the glucose concentration at set points of 0.08 and 0.05 g/L. The controller established well-defined conditions over several hours up to biomass concentrations of 13.5 and 20.7 g/L, respectively. The specific glucose uptake rates at both set points were 1.04 and 0.68 g/g/h, respectively. It is demonstrated that during fed-batch cultivation an overall pure oxidative metabolism of glucose is maintained at the lower set point and a specific ethanol production rate of 0.18 g/g/h at the higher set point.     

On-line monitoring and control of substrate concentrations in biological processes by flow injection analysis systems

Concentrations of substrates, glucose, and ammionia in biological processes have been on-line monitored by using glucose-flow injection (FIA) and ammonia-FIA systems. Based on the on-line monitored data the concentrations of substrates have been controlled by an on-off controller, a PID controller, and a neural network (NN) based controller. A simulation program has been developed to test the control quality of each controller and to estimate the control parameters. The on-off controller often produced high oscillations at the set point due to its low robustness. The control quality of a PID controller could have been improved by a high analysis frequency and by a short residence time of sample in a FIA system. A NN-based controller with 3 layers has been developed, and a 3(input)-2(hidden)-1(output) network structure has been found to be optimal for the NN-based controller. The performance of the three controllers has been tested in a simulated process as well as in a cultivation process ofSaccharomyces cerevisiae, and the performance has also been compared to simulation results. The NN-based controller with the 3-2-1 network structure was robust and stable against some disturbances, such as a sudden injection of distilled water into a biological process.     

A knowledge-based system for the strategic control level of robots in flexible manufacturing cells

The strategic control level synthesis for robots is related to a hierarchical robot control problem. The main control problem at the strategic control level is to select the model and algorithm to be used by the lower control level to execute the given robot task. Usually there are several lower control level models and algorithms that can be used by the robot control system for every robot task. Strategic control level synthesis depends on the particular robot system application. In a typical application, when the robot system is used in a flexible manufacturing system for manipulating various part types, the robot tasks executed by the robot system depend on the manufacturing processes in the system. If the robot system is applied in another flexible manufacturing system, dedicated to other manufacturing processes, another set of robot tasks might be needed to perform the necessary operations. Therefore, the quantity and the kind of knowledge required in the system for the strategic control level differ from one application to another. Such a fact creates the appropriate conditions for employing some artificial intelligence techniques. This article describes a knowledge-based system approach to the strategic control level synthesis problem.     

A mathematical model to optimize the available control measures of COVID – 19

In the absence of valid medicine or vaccine for treating the pandemic Coronavirus (COVID-19) infection, other control strategies like; quarantine, social distancing, self- isolation, sanitation and use of personal protective equipment are effective tool used to prevent and curtail the spread of the disease. In this paper, we present a mathematical model to study the dynamics of COVID-19. We then formulate an optimal control problem with the aim to study the most effective control strategies to prevent the proliferation of the disease. The existence of an optimal control function is established and the Pontryagin maximum principle is applied for the characterization of the controller. The equilibrium solutions (DFE & endemic) are found to be locally asymptotically stable and subsequently the basic reproduction number is obtained. Numerical simulations are carried out to support the analytic results and to explicitly show the significance of the control. It is shown that Quarantine/isolating those infected with the disease is the best control measure at the moment.     

人机共融的主动型下肢假肢分层控制策略探讨

为研究当前主动型下肢假肢控制问题的解决策略,提出了主动型下肢假肢设计和分类的通用控制框架,包括3个分层结构:上层控制器、中层控制器、底层控制器。其中,上层控制器感知运动意图;中层控制器将运动意图转换为预期的装置状态,用于底层控制器的跟踪参考;底层控制器通过反馈控制或者前馈控制计算出预期装置状态与当前装置状态的误差,驱动假肢执行这些命令,形成控制闭环。结果表明,该通用控制框架可完整阐释主动型下肢假肢的人—机—环境共融关系,明确了分层控制策略的层级任务,为未来主动型下肢假肢的发展提供了理论指导。     

Control and optimization of apheresis procedures in a COBE 2997 cell separator

To obtain more efficient operation of a COBE Model 2997 clinical cell separator using either a Single Stage II (SS II) or a Dual Stage separation chamber, modifications were made to allow complete computer control. Product cell density was detected using an optical sensor and controlled by automatic feedback through a microcomputer interface. Control was accomplished by automatically adjusting the red blood cell (RBC) and plasma product flow rates using a proportional-integral (PI) algorithm. Results show that, using either chamber, the product cell density can be maintained at a preselected value for extended periods of time without operator intervention. This system allowed investigation of optimal operating regions for plateletpheresis and leukapheresis procedures. The effects of centrifuge rpm and controller set point on centrifuge operation were investigated using a second order factorial experimental design. Theoretical significance of model parameters was assessed with the aid of a hindered settling model and simple reasoning about the interface position relative to the collection port. The results suggest that, in either chamber, the optimum operating region for plateletpheresis procedures occurs at moderate controller set points and high centrifuge rpm. The resultant operating efficiency and product purity values are approximately 63 percent and 0.65 respectively in the SS II chamber and approximately 70 percent and 0.70 respectively in the Dual Chamber. In the SS II, the optimum operating region for leukapheresis procedures occurred at high controller set point values for any centrifuge rpm above 1200 with an operating efficiency near 100 percent. However, in the Dual Chamber, the optimum operating region for leukapheresis procedures occurred at high controller set points and high centrifuge rpm's, again providing an operating efficiency near 100 percent.     

MODEM: a multi-agent hierarchical structure to model the human motor control system

In this study, based on behavioral and neurophysiological facts, a new hierarchical multi-agent architecture is proposed to model the human motor control system. Performance of the proposed structure is investigated by simulating the control of sit to stand movement. To develop the model, concepts of mixture of experts, modular structure, and some aspects of equilibrium point hypothesis were brought together. We have called this architecture MODularized Experts Model (MODEM). Human motor system is modeled at the joint torque level and the role of the muscles has been embedded in the function of the joint compliance characteristics. The input to the motor system, i.e., the central command, is the reciprocal command. At the lower level, there are several experts to generate the central command to control the task according to the details of the movement. The number of experts depends on the task to be performed. At the higher level, a “gate selector” block selects the suitable subordinate expert considering the context of the task. Each expert consists of a main controller and a predictor as well as several auxiliary modules. The main controller of an expert learns to control the performance of a given task by generating appropriate central commands under given conditions and/or constraints. The auxiliary modules of this expert learn to scrutinize the generated central command by the main controller. Auxiliary modules increase their intervention to correct the central command if the movement error is increased due to an external disturbance. Each auxiliary module acts autonomously and can be interpreted as an agent. Each agent is responsible for one joint and, therefore, the number of the agents of each expert is equal to the number of joints. Our results indicate that this architecture is robust against external disturbances, signal-dependent noise in sensory information, and changes in the environment. We also discuss the neurophysiological and behavioral basis of the proposed model (MODEM).     

F' transfer from Escherichia coli to Proteus mirabilis]

The task of this work was the establishment of an effective transfer system for F'-plasmids from Escherichia coli to Proteus mirabilis. It is shown that cells of PG VI act as recipients in crosses with E. coli F' strains but with a low transfer rate of the plasmid. The presumption that a restriction -- modification system in P. mirabilis was the only reason for the low transfer could not be confirmed. An indirect selection method was developed to isolate P. mirabilis cells which are better recipients. Conjugation experiments showed that the isolated mutants had a better recipient capacity (increase of about 100). This is true not only for the transfer of a F'-plasmid but also for a R-plasmid. The stability of these plasmids in the mutant cells, however, was much lower than in the wild type.     

A Mixture of Delta-Rules Approximation to Bayesian Inference in Change-Point Problems

Error-driven learning rules have received considerable attention because of their close relationships to both optimal theory and neurobiological mechanisms. However, basic forms of these rules are effective under only a restricted set of conditions in which the environment is stable. Recent studies have defined optimal solutions to learning problems in more general, potentially unstable, environments, but the relevance of these complex mathematical solutions to how the brain solves these problems remains unclear. Here, we show that one such Bayesian solution can be approximated by a computationally straightforward mixture of simple error-driven ‘Delta’ rules. This simpler model can make effective inferences in a dynamic environment and matches human performance on a predictive-inference task using a mixture of a small number of Delta rules. This model represents an important conceptual advance in our understanding of how the brain can use relatively simple computations to make nearly optimal inferences in a dynamic world.     

Schema generation in recurrent neural nets for intercepting a moving target

The grasping of a moving object requires the development of a motor strategy to anticipate the trajectory of the target and to compute an optimal course of interception. During the performance of perception-action cycles, a preprogrammed prototypical movement trajectory, a motor schema, may highly reduce the control load. Subjects were asked to hit a target that was moving along a circular path by means of a cursor. Randomized initial target positions and velocities were detected in the periphery of the eyes, resulting in a saccade toward the target. Even when the target disappeared, the eyes followed the target’s anticipated course. The Gestalt of the trajectories was dependent on target velocity. The prediction capability of the motor schema was investigated by varying the visibility range of cursor and target. Motor schemata were determined to be of limited precision, and therefore visual feedback was continuously required to intercept the moving target. To intercept a target, the motor schema caused the hand to aim ahead and to adapt to the target trajectory. The control of cursor velocity determined the point of interception. From a modeling point of view, a neural network was developed that allowed the implementation of a motor schema interacting with feedback control in an iterative manner. The neural net of the Wilson type consists of an excitation-diffusion layer allowing the generation of a moving bubble. This activation bubble runs down an eye-centered motor schema and causes a planar arm model to move toward the target. A bubble provides local integration and straightening of the trajectory during repetitive moves. The schema adapts to task demands by learning and serves as forward controller. On the basis of these model considerations the principal problem of embedding motor schemata in generalized control strategies is discussed.     

Improving cultivation processes for recombinant protein production

An new cascade control system is presented that reproducibly keeps the cultivation part of recombinant protein production processes on its predetermined track. While the system directly controls carbon dioxide production mass and carbon dioxide production rates along their setpoint profiles in fed-batch cultivation, it simultaneously keeps the specific biomass growth rates and the biomass profiles on their desired paths. The control scheme was designed and tuned using a virtual plant environment based on the industrial process control system SIMATIC PCS 7 (Siemens AG). It is shown by means of validation experiments that the simulations in this straightforward approach directly reflect the experimentally observed controller behaviour. Within the virtual plant environment, it was shown that the cascade control is considerably better than previously used control approaches. The controller significantly improved the batch-to-batch reproducibility of the fermentations. Experimental tests confirmed that it is particularly suited for cultivation processes suffering from long response times and delays. The performance of the new controller is demonstrated during its application in Escherichia coli fed-batch cultivations as well as in animal cell cultures with CHO cells. The technique is a simple and reliable alternative to more sophisticate model-supported controllers.     

Multi-criteria optimization of regulation in metabolic networks

Determining the regulation of metabolic networks at genome scale is a hard task. It has been hypothesized that biochemical pathways and metabolic networks might have undergone an evolutionary process of optimization with respect to several criteria over time. In this contribution, a multi-criteria approach has been used to optimize parameters for the allosteric regulation of enzymes in a model of a metabolic substrate-cycle. This has been carried out by calculating the Pareto set of optimal solutions according to two objectives: the proper direction of flux in a metabolic cycle and the energetic cost of applying the set of parameters. Different Pareto fronts have been calculated for eight different "environments" (specific time courses of end product concentrations). For each resulting front the so-called knee point is identified, which can be considered a preferred trade-off solution. Interestingly, the optimal control parameters corresponding to each of these points also lead to optimal behaviour in all the other environments. By calculating the average of the different parameter sets for the knee solutions more frequently found, a final and optimal consensus set of parameters can be obtained, which is an indication on the existence of a universal regulation mechanism for this system.The implications from such a universal regulatory switch are discussed in the framework of large metabolic networks.