An Approach for the Estimation of Concentrations of Soluble Compounds in E. coli Bioprocesses.

Accurate estimations of the concentrations of soluble compounds are crucial for optimizing bioprocesses involving Escherichia coli (E. coli). This study proposes a hybrid model structure that leverages off-gas analysis data and physiological parameters, including the average biomass age and specific growth rate, to estimate soluble compounds such as acetate and glutamate in fed-batch cultivations We used a hybrid recurrent neural network to establish the relationships between these parameters. To enhance the precision of the estimates, the model incorporates ensemble averaging and information gain. Ensemble averaging combines varying model inputs, leading to more robust representations of the underlying dynamics in E. coli bioprocesses. Our hybrid model estimates acetates with 1% and 8% system precision using data from the first site and the second site at GSK plc, respectively. Using the data from the second site, the precision of the approach for other solutes was as fallows: isoleucine -8%, lactate and glutamate -9%, and a 13% error for glutamine., These results, demonstrate its practical potential.

Viable cell estimation of mammalian cells using off-gas-based oxygen uptake rate and aging-specific functional.

This study developed an estimation routine for counting the viable cells in an in vitro fed-batch Chinese hamster ovary cultivation that relies on off-gas information and inlet gas mixture knowledge. We computed the oxygen uptake rate bound to the bioreactor exhaust gas outlet when the inlet gas mixture was stationary. Our mammalian biosynthesis analysis determined the stoichiometric parameters as a function of the average population age. We cross-validated an identical algorithm for mammalian and microbial cultivations and found that the' 99% confidence band of the model generally overlapped with the error bars defined from observations. The resulting RMSE and MAE averages were 0.188 and 0.14e9cells L-1, respectively, when estimating the viable mammalian cell count. The validation for the estimation of total bacterial biomass yielded an MAE and RMSE of 1.78 g L-1 and 2.53 g L-1, respectively. Moreover, our proposed approach provides an online estimation of the average population age for both aerobically cultivated microorganisms.

An oxygen-uptake-rate-based estimator of the specific growth rate in Escherichia coli BL21 strains cultivation processes.

The cell cultivation process in a bioreactor is a high-value manufacturing process that requires excessive monitoring and control compatibility. The specific cell growth rate is a crucial parameter that describes the online quality of the cultivation process. Most methods and algorithms developed for online estimations of the specific growth rate controls in batch and fed-batch microbial cultivation processes rely on biomass growth models. In this paper, we present a soft sensor - a specific growth rate estimator that does not require a particular bioprocess model. The approach for online estimation of the specific growth rate is based on an online measurement of the oxygen uptake rate. The feasibility of the estimator developed in this study was determined in two ways. First, we used numerical simulations on a virtual platform, where the cell culture processes were theoretically modeled. Next, we performed experimental validation based on laboratory-scale (7, 12, 15 L) bioreactor experiments with three different Escherichia coli BL21 cell strains.

Bridging Offline Functional Model Carrying Aging-Specific Growth Rate Information and Recombinant Protein Expression: Entropic Extension of Akaike Information Criterion.

This study presents a mathematical model of recombinant protein expression, including its development, selection, and fitting results based on seventy fed-batch cultivation experiments from two independent biopharmaceutical sites. To resolve the overfitting feature of the Akaike information criterion, we proposed an entropic extension, which behaves asymptotically like the classical criteria. Estimation of recombinant protein concentration was performed with pseudo-global optimization processes while processing offline recombinant protein concentration samples. We show that functional models including the average age of the cells and the specific growth at induction or the start of product biosynthesis are the best descriptors for datasets. We also proposed introducing a tuning coefficient that would force the modified Akaike information criterion to avoid overfitting when the designer requires fewer model parameters. We expect that a lower number of coefficients would allow the efficient maximization of target microbial products in the upstream section of contract development and manufacturing organization services in the future. Experimental model fitting was accomplished simultaneously for 46 experiments at the first site and 24 fed-batch experiments at the second site. Both locations contained 196 and 131 protein samples, thus giving a total of 327 target product concentration samples derived from the bioreactor medium.

Kinetic modeling of human induced pluripotent stem cell expansion in suspension culture.

To date, practical application of mathematical models for model-based design of stem cell expansion processes is limited. Nevertheless, the first attempts show vast potential of this approach for the improvement of expansion process performance. This article presents the developed dynamic kinetic model of the human induced pluripotent stem cell expansion process in suspension culture. The model predicts cell growth, consumption of glucose and production of lactic acid, as well as the average aggregate size. The latter process variable is of particular importance for achieving high cell density. By adding botulinum hemagglutinin, an E-cadherin inhibitor and subsequent aggregate break-up, one can significantly increase performance of cell expansion process. After defining the appropriate optimization criteria and additional modification of the model, the latter can be further applied for model-based optimization of the final cell concentration by calculating optimal aggregate break-up and glucose/glutamine feeding strategies.

Projection Mapping User Interface for Disabled People.

Difficulty in communicating is one of the key challenges for people suffering from severe motor and speech disabilities. Often such person can communicate and interact with the environment only using assistive technologies. This paper presents a multifunctional user interface designed to improve communication efficiency and person independence. The main component of this interface is a projection mapping technique used to highlight objects in the environment. Projection mapping makes it possible to create a natural augmented reality information presentation method. The user interface combines a depth sensor and a projector to create camera-projector system. We provide a detailed description of camera-projector system calibration procedure. The described system performs tabletop object detection and automatic projection mapping. Multiple user input modalities have been integrated into the multifunctional user interface. Such system can be adapted to the needs of people with various disabilities.

From Physics to Bioengineering: Microbial Cultivation Process Design and Feeding Rate Control Based on Relative Entropy Using Nuisance Time.

For historic reasons, industrial knowledge of reproducibility and restrictions imposed by regulations, open-loop feeding control approaches dominate in industrial fed-batch cultivation processes. In this study, a generic gray box biomass modeling procedure uses relative entropy as a key to approach the posterior similarly to how prior distribution approaches the posterior distribution by the multivariate path of Lagrange multipliers, for which a description of a nuisance time is introduced. The ultimate purpose of this study was to develop a numerical semi-global convex optimization procedure that is dedicated to the calculation of feeding rate time profiles during the fed-batch cultivation processes. The proposed numerical semi-global convex optimization of relative entropy is neither restricted to the gray box model nor to the bioengineering application. From the bioengineering application perspective, the proposed bioprocess design technique has benefits for both the regular feed-forward control and the advanced adaptive control systems, in which the model for biomass growth prediction is compulsory. After identification of the gray box model parameters, the options and alternatives in controllable industrial biotechnological processes are described. The main aim of this work is to achieve high reproducibility, controllability, and desired process performance. Glucose concentration measurements, which were used for the development of the model, become unnecessary for the development of the desired microbial cultivation process.

Hybrid Approach to State Estimation for Bioprocess Control.

An improved state estimation technique for bioprocess control applications is proposed where a hybrid version of the Unscented Kalman Filter (UKF) is employed. The underlying dynamic system model is formulated as a conventional system of ordinary differential equations based on the mass balances of the state variables biomass, substrate, and product, while the observation model, describing the less established relationship between the state variables and the measurement quantities, is formulated in a data driven way. The latter is formulated by means of a support vector regression (SVR) model. The UKF is applied to a recombinant therapeutic protein production process using Escherichia coli bacteria. Additionally, the state vector was extended by the specific biomass growth rate µ in order to allow for the estimation of this key variable which is crucial for the implementation of innovative control algorithms in recombinant therapeutic protein production processes. The state estimates depict a sufficiently low noise level which goes perfectly with different advanced bioprocess control applications.

Current state and perspectives in modeling and control of human pluripotent stem cell expansion processes in stirred-tank bioreactors.

Implementation of model-based practices for process development, control, automation, standardization, and validation are important factors for therapeutic and industrial applications of human pluripotent stem cells. As robust cultivation strategies for pluripotent stem cell expansion and differentiation have yet to be determined, process development could be enhanced by application of mathematical models and advanced control systems to optimize growth conditions. Therefore, it is important to understand both the potential of possible applications and the apparent limitations of existing mathematical models to improve pluripotent stem cell cultivation technologies. In the present review, the authors focus on these issues as they apply to stem cell expansion processes. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:355-364, 2017.

User Adaptive Text Predictor for Mentally Disabled Huntington's Patients.

This paper describes in detail the design of the specialized text predictor for patients with Huntington's disease. The main aim of the specialized text predictor is to improve the text input rate by limiting the phrases that the user can type in. We show that such specialized predictor can significantly improve text input rate compared to a standard general purpose text predictor. Specialized text predictor, however, makes it more difficult for the user to express his own ideas. We further improved the text predictor by using the sematic database to extract synonym, hypernym, and hyponym terms for the words that are not present in the training data of the specialized text predictor. This data can then be used to compute reasonable predictions for words that are originally not known to the text predictor.

Bioreactor control improves bioprocess performance.

The performance of bioreactors is not only determined by productivity but also by process quality, which is mainly determined by variances in the process variables. As fluctuations in these quantities directly affect the variability in the product properties, combatting distortions is the main task of practical quality assurance. The straightforward way of reducing this variability is keeping the product formation process tightly under control. Purpose of this keynote is to show that there is enough evidence in literature showing that the performance of the fermentation processes can significantly be improved by feedback control. Most of the currently used open loop control procedures can be replaced by relatively simple feedback techniques. It is shown by practical examples that such a retrofitting does not require significant changes in the well-established equipment. Feedback techniques are best in assuring high reproducibility of the industrial cultivation processes and thus in assuring the quality of their products. Many developments in supervising and controlling industrial fermentations can directly be taken over in manufacturing processes. Even simple feedback controllers can efficiently improve the product quality. It's the time now that manufacturers follow the developments in most other industries and improve process quality by automatic feedback control.

Data-based optimization of protein production processes.

While data-based modeling is possible in various ways, data-based optimization has not been previously described. Here we present such an optimization technique. It is based on dynamic programming principles and uses data directly from exploratory experiments where the influence of the adjustable variables u were tested at various values. Instead of formulating the performance index J as a function of time t within a cultivation process it is formulated as a function of the biomass x. The advantage of this representation is that in most biochemical production processes J(x) only depends of the vector u of the adjustable variables. This given, mathematical programming techniques allow determining the desired optimal paths u(opt)(x) from the x-derivatives of J(x). The resulting u(opt)(x) can easily be transformed back to the u(t) profiles that can then be used in an improved fermentation run. The optimization technique can easily be explained graphically. With numerical experiments the feasibility of the method is demonstrated. Then, two optimization runs for recombinant protein formations in E. coli are discussed and experimental validation results are presented.

Increasing batch-to-batch reproducibility of CHO-cell cultures using a model predictive control approach.

By means of a model predictive control strategy it was possible to ensure a high batch-to-batch reproducibility in animal cell (CHO-cell) suspensions cultured for a recombinant therapeutic protein (EPO) production. The general control objective was derived by identifying an optimal specific growth rate taking productivity, protein quality and process controllability into account. This goal was approached indirectly by controlling the oxygen mass consumed by the cells which is related to specific biomass growth rate and cell concentration profile by manipulating the glutamine feed rate. Process knowledge represented by a classical model was incorporated into the model predictive control algorithm. The controller was employed in several cultivation experiments. During these cultivations, the model parameters were adapted after each sampling event to cope with changes in the process' dynamics. The ability to predict the state variables, particularly for the oxygen consumption, led to only moderate changes in the desired optimal operational trajectories. Hence, nearly identical oxygen consumption profiles, cell and protein titers as well as sialylation patterns were obtained for all cultivation runs.

Simplified off-gas analyses in animal cell cultures for process monitoring and control purposes.

Batch-to-batch reproducibility of animal cell cultures can significantly be enhanced using process control procedures. Most informative signals for advanced process control can be derived from the volume fractions of oxygen and carbon dioxide in the vent line of the reactors. Here we employed simple low-cost sensors, previously not considered for off-gas analysis at a laboratory-scale cell cultures, and compared them with a simultaneously used quadrupole mass spectrometer, i.e., the standard equipment. A decisive advantage is that the sensors did not need any calibration and are easy to use. We show that monitoring and advanced control of cell cultures can significantly be simplified using the devices tested here and that the same batch-to-batch reproducibility can be obtained with much less effort than before.

Simple control of fed-batch processes for recombinant protein production with E. coli.

A very simple but effective process control technique is proposed that leads to a high batch-to-batch reproducibility with respect to biomass concentration as well as the specific biomass growth rate profiles in E. coli fermentations performed during recombinant protein production. It makes use of the well-established temperature controllers in currently used fermenters, but takes its information from the difference between the controlled culture temperature T (cult) and the temperature T (coolin) of the coolant fed to the fermenter's cooling jacket as adjusted by the fermenter temperature controller. For process control purposes this measured difference is corrected regarding stirrer influences and cumulated before it is used as a new process control variable. As a spin-off of this control, it becomes possible to estimate online the oxygen mass transfer rates and the corresponding k(L)a values during the real cultivation process.

Selective expression of the soluble product fraction in Escherichia coli cultures employed in recombinant protein production processes.

Recombinant proteins produced in Escherichia coli hosts may appear within the cells' cytoplasm in form of insoluble inclusion bodies (IB's) and/or as dissolved functional protein molecules. If no efficient refolding procedure is available, one is interested in obtaining as much product as possible in its soluble form. Here, we present a process engineering approach to maximizing the soluble target protein fraction. For that purpose, a dynamic process model was developed. Its essential kinetic component, the specific soluble product formation rate, if represented as a function of the specific growth rate and the culture temperature, depicts a clear maximum. Based on the dynamic model, optimal specific growth rate and temperature profiles for the fed-batch fermentation were determined. In the course of the study reported, the mass of desired soluble protein was increased by about 25%. At the same time, the formation of inclusion bodies was essentially avoided. As the optimal cultivation procedure is rather susceptible to distortions, control measures are necessary to guarantee that the real process can be kept on its desired path. This was possible with robust closed loop control. Experimental process validation revealed that, in this way, high dissolved product fractions could be obtained at an excellent batch-to-batch reproducibility.

Product formation kinetics in genetically modified E. coli bacteria: inclusion body formation.

A data-driven model is presented that can serve two important purposes. First, the specific growth rate and the specific product formation rate are determined as a function of time and thus the dependency of the specific product formation rate from the specific biomass growth rate. The results appear in form of trained artificial neural networks from which concrete values can easily be computed. The second purpose is using these results for online estimation of current values for the most important state variables of the fermentation process. One only needs online data of the total carbon dioxide production rate (tCPR) produced and an initial value x of the biomass, i.e., the size of the inoculum, for model evaluation. Hence, given the inoculum size and online values of tCPR, the model can directly be employed as a softsensor for the actual value of the biomass, the product mass as well as the specific biomass growth rate and the specific product formation rate. In this paper the method is applied to fermentation experiments on the laboratory scale with an E. coli strain producing a recombinant protein that appears in form of inclusion bodies within the cells' cytoplasm.

Control of cultivation processes for recombinant protein production: a review.

The current state-of-the-art in control of cultivation processes for recombinant protein production is examined including the quantitative knowledge that can be activated for this purpose and the measurement techniques that can be employed for control at industrial manufacturing sites.

Improving the batch-to-batch reproducibility of microbial cultures during recombinant protein production by regulation of the total carbon dioxide production.

Batch-to-batch reproducibility of fermentation processes performed during the manufacturing processes of biologics can be increased by operating the cultures at feed rate profiles that are robust against typically arising disturbances. Remaining randomly appearing deviations from the desired path should be suppressed automatically by manipulating the feed rate. With respect to the cells' physiology it is best guiding the cultivations along an optimal profile of the specific biomass growth rate mu(t). However, there are two problems that speak for further investigations: Upon severe disturbances that may happen during the fermentation, the biomass concentration X may significantly deviate from its desired value, then a fixed mu-profile leads to a diminished batch-to-batch reproducibility. Second, the specific growth rate cannot easily be estimated online to a favourably high accuracy, hence it is difficult to determine the deviations in mu from the desired profile. The alternative discussed here solves both problems by keeping the process at the corresponding total cumulative carbon dioxide production-profile: it is robust against distortions in X and the controlled variable can accurately be measured online during cultivations of all relevant sizes. As compared to the fermentation practice currently used in industry, the experimental results, presented at the example of a recombinant protein production with Escherichia coli cells, show that CPR-based corrections lead to a considerably improved batch-to-batch reproducibility.

Improving the batch-to-batch reproducibility in microbial cultures during recombinant protein production by guiding the process along a predefined total biomass profile.

In industry Escherichia coli is the preferred host system for the heterologous biosynthesis of therapeutic proteins that do not need posttranslational modifications. In this report, the development of a robust high-cell-density fed-batch procedure for the efficient production of a therapeutic hormone is described. The strategy is to guide the process along a predefined profile of the total biomass that was derived from a given specific growth rate profile. This profile might have been built upon experience or derived from numerical process optimization. A surprisingly simple adaptive procedure correcting for deviations from the desired path was developed. In this way the batch-to-batch reproducibility can be drastically improved as compared to the process control strategies typically applied in industry. This applies not only to the biomass but, as the results clearly show, to the product titer also.