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.     

Cybernetic model predictive control of a continuous bioreactor with cell recycle

The control of poly-beta-hydroxybutyrate (PHB) productivity in a continuous bioreactor with cell recycle is studied by simulation. A cybernetic model of PHB synthesis in Alcaligenes eutrophus is developed. Model parameters are identified using experimental data, and simulation results are presented. The model is interfaced to a multirate model predictive control (MPC) algorithm. PHB productivity and concentration are controlled by manipulating dilution rate and recycle ratio. Unmeasured time varying disturbances are imposed to study regulatory control performance, including unreachable setpoints. With proper controller tuning, the nonlinear MPC algorithm can track productivity and concentration setpoints despite a change in the sign of PHB productivity gain with respect to dilution rate. It is shown that the nonlinear MPC algorithm is able to track the maximum achievable productivity for unreachable setpoints under significant process/model mismatch. The impact of model uncertainty upon controller performance is explored. The multirate MPC algorithm is tested using three controllers employing models that vary in complexity of regulation. It is shown that controller performance deteriorates as a function of decreasing biological complexity.     

Multi-input multi-output control of a continuous fermenter using nonlinear model based controllers

Internal Model Control (IMC) and Model Predictive Control (MPC), the two most important members of model based controllers, are favourable alternatives for control of nonlinear processes. However, the performance of these controllers deteriorates drastically in the presence of substantial process-model mismatch. Hu and Rangaiah (1998) proposed feedback augmentation for nonlinear IMC (hence named Augmented IMC, AuIMC) for improving control in the presence of modelling errors, and demonstrated its success on a neutralization process. In the present study, IMC, MPC and AuIMC strategies are tested in a more difficult case of multi-input multi-output (MIMO) operation of a highly nonlinear continuous fermenter. A new control configuration is introduced as the conventional configuration is not applicable. Simulation results for different modelling errors show that IMC is better than MPC for fermenter control. The advantage of augmentation as in AuIMC manifests in the significantly improved regulatory control of the fermenter.     

Machine learning-based model predictive controller design for cell culture processes

The biopharmaceutical industry continuously seeks to optimize the critical quality attributes to maintain the reliability and cost-effectiveness of its products. Such optimization demands a scalable and optimal control strategy to meet the process constraints and objectives. This work uses a model predictive controller (MPC) to compute an optimal feeding strategy leading to maximized cell growth and metabolite production in fed-batch cell culture processes. The lack of high-fidelity physics-based models and the high complexity of cell culture processes motivated us to use machine learning algorithms in the forecast model to aid our development. We took advantage of linear regression, the Gaussian process and neural network models in the MPC design to maximize the daily protein production for each batch. The control scheme of the cell culture process solves an optimization problem while maintaining all metabolites and cell culture process variables within the specification. The linear and nonlinear models are developed based on real cell culture process data, and the performance of the designed controllers is evaluated by running several real-time experiments.     

Closed-loop control of fed-batch cultures of recombinant Escherichia coli using on-line HPLC

This article describes a fully automated system for the on-line monitoring and closed-loop control of a fed-batch fermentation of recombinant Escherichia coli, and presents two case studies of its used in limiting production of unwanted byproducts such as acetic in fed-batch fermentations. The system had two components. The first components, on-line monitoring, comprised an aseptic sampling device, a microcentrifuge, and HPLC System. These instruments removed a Sample from a fermentor, spun it at high speed to separate solid and liquid components, and then automatically injected the supernatant onto an HPLC column for analysis. The second component consisted of control algorithms programmed using the LabView visual programming environment in a control computer that was linked via a remote components were linked so that results from the on-line HPLC were captured and used by the control algorithm was designed to demonstrate coarse feedback control to confirm the operability of the controller. The second case study showed how the system could be used in a more sophisticated feedings strategy providing fine control and limiting acetate concentration to a low level throughout the fermentation. (c) 1994 John Wiley & Sons, Inc.     

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.     

Embryonic Lethality of Mitochondrial Pyruvate Carrier 1 Deficient Mouse Can Be Rescued by a Ketogenic Diet

Mitochondrial import of pyruvate by the mitochondrial pyruvate carrier (MPC) is a central step which links cytosolic and mitochondrial intermediary metabolism. To investigate the role of the MPC in mammalian physiology and development, we generated a mouse strain with complete loss of MPC1 expression. This resulted in embryonic lethality at around E13.5. Mouse embryonic fibroblasts (MEFs) derived from mutant mice displayed defective pyruvate-driven respiration as well as perturbed metabolic profiles, and both defects could be restored by reexpression of MPC1. Labeling experiments using ¹³C-labeled glucose and glutamine demonstrated that MPC deficiency causes increased glutaminolysis and reduced contribution of glucose-derived pyruvate to the TCA cycle. Morphological defects were observed in mutant embryonic brains, together with major alterations of their metabolome including lactic acidosis, diminished TCA cycle intermediates, energy deficit and a perturbed balance of neurotransmitters. Strikingly, these changes were reversed when the pregnant dams were fed a ketogenic diet, which provides acetyl-CoA directly to the TCA cycle and bypasses the need for a functional MPC. This allowed the normal gestation and development of MPC deficient pups, even though they all died within a few minutes post-delivery. This study establishes the MPC as a key player in regulating the metabolic state necessary for embryonic development, neurotransmitter balance and post-natal survival.     

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.     

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.     

Using adaptive model predictive control to customize maintenance therapy chemotherapeutic dosing for childhood acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is a common childhood cancer in which nearly one-quarter of patients experience a disease relapse. However, it has been shown that individualizing therapy for childhood ALL patients by adjusting doses based on the blood concentration of active drug metabolite could significantly improve treatment outcome. An adaptive model predictive control (MPC) strategy is presented in which maintenance therapy for childhood ALL is personalized using routine patient measurements of red blood cell mean corpuscular volume as a surrogate for the active drug metabolite concentration. A clinically relevant mathematical model is developed and used to describe the patient response to the chemotherapeutic drug 6-mercaptopurine, with some model parameters being patient-specific. During the course of treatment, the patient-specific parameters are adaptively identified using recurrent complete blood count measurements, which sufficiently constrain the patient parameter uncertainty to support customized adjustments of the drug dose. While this work represents only a first step toward a quantitative tool for clinical use, the simulated treatment results indicate that the proposed mathematical model and adaptive MPC approach could serve as valuable resources to the oncologist toward creating a personalized treatment strategy that is both safe and effective.     

On-line monitoring and controlling system for fermentation processes

A personal computer-based on-line monitoring and controlling system was developed for the fermentation of microorganism. The on-line HPLC system for the analysis of glucose and ethanol in the fermentation broth was connected to the fermenter via an auto-sampling equipment, which could perform the pipetting, filtration and dilution of the sample and final injection onto the HPLC through automation based on a programmed procedure. The A/D and D/A interfaces were equipped in order to process the signals from electrodes and from the detector of HPLC, and to direct the feed pumps, the motor of stirrer and gas flow-rate controller. The software that supervised the control of the stirring speed, gas flow-rate, pH value, feed flow-rate of medium, and the on-line measurement of glucose and ethanol concentration was programmed by using Microsoft Visual Basic under Microsoft Windows. The signal for chromatographic peaks from on-line HPLC was well captured and processed using an RC filter and a smoothing algorithm. This monitoring and control system was demonstrated to be effective in the ethanol fermentation of Zymomonas mobilis operated in both batch and fed-batch modes. In addition to substrate and product concentrations determined by on-line HPLC, the biomass concentration in Z. mobilis fermentation could also be on-line estimated by using the pH control and an implemented software sensor. The substrate concentration profile in the fed-back fermentation followed well the set point profile due to the fed-back action of feed flow-rate control.     

A probing feeding strategy for Escherichia coli cultures

A strain-independent feeding strategy for fed-batch cultures of Escherichia coli is presented. By superimposing short pulses in the glucose feed rate, on-line detection of acetate formation can be made using a standard dissolved oxygen sensor. A simple feedback algorithm is then used to adjust the feed rate to avoid acetate formation. The feasibility of the strategy is demonstrated by both simulation and experiments.     

A Novel ELISA Format for the Rapid and Sensitive Detection of Staphylococcal Enterotoxin A

Staphylococcal food poisoning is one of the leading causes of bacterial food poisoning each year. Detection kits for staphylococcal enterotoxins are commercially available and the assays can require from one and a half to twenty-four hours to complete with detection limits ranging from 0.5 to 2 ng enterotoxin per gram of food. We have successfully demonstrated a microsphere-packed capillary (MPC) ELISA for the detection of staphylococcal enterotoxin A (SEA) and have compared it to two commercially available kits. The MPC assay detected a lower amount of SEA in ham, chicken, cheese, and bean sprouts than either of the two commercially available kits. In addition, the novel MPC assay was completed in less than ten minutes, as compared to three and twenty-four hours for the two commercially available kits. This research also demonstrated that the MPC ELISA can contain integrated positive and negative controls and has the potential to simultaneously detect and identify multiple enterotoxins.     

Skeletal muscle atrophy leads to loss and dysfunction of muscle precursor cells

Atrophy of skeletal muscle leads to decreases in myofiber size and nuclear number; however, the effects of atrophic conditions on muscle precursor cells (MPC) are largely unknown. MPC lie outside myofibers and represent the main source of additional myonuclei necessary for muscle growth and repair. In the present study, we examined the properties of MPC after hindlimb suspension (HS)-induced atrophy and subsequent recovery of the mouse hindlimb muscles. We demonstrated that the number of MPC in atrophied muscles was decreased. RT-PCR analysis of cells isolated from atrophied muscles indicated that several mRNA characteristic of the myogenic program in MPC were absent. Cells isolated from atrophied muscles failed to properly proliferate and undergo differentiation into multinucleated myotubes. Thus atrophy led to a decrease in MPC and caused dysfunction in those MPC that remained. Upon regrowth of the atrophied muscles, these deleterious effects were reversed. Our data suggest that preventing loss or dysfunction of MPC may be a new pharmacological target during muscle atrophy. satellite cells; hindlimb suspension; proliferation; differentiation; myotubes     

Multi-angle light scattering as a process analytical technology measuring real-time molecular weight for downstream process control

For many protein therapeutics including monoclonal antibodies, aggregate removal process can be complex and challenging. We evaluated two different process analytical technology (PAT) applications that couple a purification unit performing preparative hydrophobic interaction chromatography (HIC) to a multi-angle light scattering (MALS) system. Using first principle measurements, the MALS detector calculates weight-average molar mass, M_w and can control aggregate levels in purification. The first application uses an in-line MALS to send start/stop fractionation trigger signals directly to the purification unit when preset M_w criteria are met or unmet. This occurs in real-time and eliminates the need for analysis after purification. The second application uses on-line ultra-high performance size-exclusion liquid chromatography to sample from the purification stream, separating the mAb species and confirming their M_w using a µMALS detector. The percent dimer (1.5%) determined by the on-line method is in agreement with the data from the in-line application (M_w increase of approximately 2750 Da). The novel HIC-MALS systems demonstrated here can be used as a powerful tool for real-time aggregate monitoring and control during biologics purification enabling future real time release of biotherapeutics.