This manuscript addresses the problem of determining input excitation for data driven model identification appropriate for cell culture bio-processes in general, and for an industrial bioreactor used ...for the production of monoclonal antibodies, in particular. The design space is set up to give us the operating parameters for the key objective of demonstrating the feasibility of using far more perturbations than typically done in bio process identification, although significantly less than other applications, to yield data rich enough for the purpose of data driven modelling (and subsequently, control). A proprietary mechanistic model developed by Sartorius for their Cellca cell line is first introduced to serve as a test bed, based on AMBR 250® (Sartorius registered trademark for integrated high throughput bioreactor systems). Subsequently, this test bed is used to address the question of determining the frequency of input perturbation sufficient to identify a data driven dynamic model. To this end, the test bed is used to generate data at various frequencies and a linear time invariant model identified. The predictive capability of the identified model is used to ascertain the frequency of changes in data generation such that the changes are acceptable from a biological standpoint, and yet generate sufficiently rich data. In particular, a frequency of perturbations at once every three days is found to balance these tradeoffs for the monoclonal antibody process under consideration. The results from the manuscript are meaningful both from a specific results standpoint (as illustrated by subsequent adoption by Sartorius), but also by providing a mechanism to ascertain such information for other bio-processes.
There is a growing interest in continuous processing of the biopharmaceutical industry. However, the technology transfer from traditional batch-based processes is considered a challenge as protocol ...and tools still remain to be established for their usage at the manufacturing scale. Here, we present a model-based approach to design optimized perfusion cultures of Chinese Hamster Ovary cells using only the knowledge captured during small-scale fed-batch experiments. The novelty of the proposed model lies in the simplicity of its structure. Thanks to the introduction of a new catch-all variable representing a bulk of by-products secreted by the cells during their cultivation, the model was able to successfully predict cellular behavior under different operating modes without changes in its formalism. To our knowledge, this is the first experimentally validated model capable, with a single set of parameters, to capture culture dynamic under different operating modes and at different scales.
This manuscript addresses the problem of controlling a bioreactor to maximize the production of a desired product while respecting the constraints imposed by the nature of the bio‐process. The ...approach is demonstrated by first building a data‐driven model and then formulating a model predictive controller (MPC) with the results illustrated by implementing a detailed monoclonal antibody production model (the test bed) created by Sartorius Inc. In particular, a recently developed data‐driven modelling approach using an adaptation of subspace identification techniques is utilized that enables the incorporation of known physical relationships in the data‐driven model development (constrained subspace model identification), making the data‐driven model process aware. The resultant controller implementation demonstrates a significant improvement in production compared to the existing proportional integral (PI) controller strategy used in the monoclonal antibody production. Simulation results also demonstrate the superiority of the process‐aware or constrained subspace MPC compared to traditional subspace MPC. Finally, the robustness of the controller design is illustrated via the implementation of a model developed using data from a test bed with a different set of parameters, thus showing the ability of the controller design to maintain good performance in the event of changes such as a different cell line or feed characteristics.
The application of a supervised deep convolutional autoencoder was tested against partial least-squares-discriminant analysis (PLS-DA) for fault detection and diagnosis in a penicillin fed-batch ...process. In silico data was generated with a comprehensive simulator (IndPenSim) of an industrial-scale penicillin fed-batch simulator of operation under normal batch conditions and 8 fault batch conditions. A composite face-centered design response surface was applied to optimize key bioreactor design parameters based on a profit function that was directly dependent on fault detection results. The application of PLS-DA and the NN modeling resulted in an average fault detection rate (FDR) across all faults and process parameter configurations of 72.5% and 95.9%, respectively. In classifying complex fault conditions, the deep learning model greatly surpassed the PLS-DA model, and this improvement translated into a 25.0% increase in realized profit with the supervised deep convolutional autoencoder when compared to PLS-DA monitoring.
Background
Monoclonal antibodies (mAbs) are leading types of ‘blockbuster’ biotherapeutics worldwide; they have been successfully used to treat various cancers and chronic inflammatory and autoimmune ...diseases. Biotherapeutics process development and manufacturing are complicated due to lack of understanding the factors that impact cell productivity and product quality attributes. Understanding complex interactions between cells, media, and process parameters on the molecular level is essential to bring biomanufacturing to the next level. This can be achieved by analyzing cell culture metabolic levels connected to vital process parameters like viable cell density (VCD). However, VCD and metabolic profiles are dynamic parameters and inherently correlated with time, leading to a significant correlation without actual causality. Many time‐series methods deal with such issues. However, with metabolic profiling, the number of measured variables vastly exceeds the number of experiments, making most of existing methods ill‐suited and hard to interpret.
Methods and Major Results
Here we propose an alternative workflow using hierarchical dimension reduction to visualize and interpret the relation between evolution of metabolic profiles and dynamic process parameters. The first step of proposed method is focused on finding predictive relation between metabolic profiles and process parameter at all time points using OPLS regression. For each time point, the p(corr) obtained from OPLS model is considered as a differential metabogram and is further assessed using principal components analysis (PCA).
Conclusions
Compared to traditional batch modeling, applying proposed methodology on metabolic data from Chinese Hamster Ovary (CHO) antibody production characterized the dynamic relation between metabolic profiles and critical process parameters.
Graphical and Lay Summary
Understanding of complex interactions between cells, media, and bioprocess parameters and how they influence final process yield and product quality is essential to bring biomanufacturing to the next level. Proposed hierarchical approach, based on OPLS and PCA, enables modelling of dynamic relation between metabolic profiles and process parameters, taking under account confounding time effect and high correlation structure of the data.
This manuscript addresses the problem of modeling an industrial (Sartorius) bioreactor using process data. In the context of the Sartorius Bioreactor, it is important to appropriately address the ...problem of dealing with a large number of variables, which are not always measured or are measured at different sampling rates, without taking recourse to simpler interpolation- or imputation-based approaches. To this end, a dynamic model for the Sartorius Bioreactor is developed via appropriately adapting a recently presented subspace model identification technique, which in turn uses nonlinear iterative partial least squares (NIPALS) algorithms to gracefully handle the missing data. The other key contribution is evaluating the ability of the identification approach to provide insight into the process by computing interpretable variables such as metabolite rates. The results demonstrate the ability of the proposed approach to model data from the Sartorius Bioreactor.
This thesis is concerned with apprentice footballers' understanding of identity and Personal Development (PD). For those young footballers offered apprenticeships with professional football clubs ...aged 16, by the age of 21, five out of every six will be out of the game professionally (Professional Footballers Association, 2019). Rejection from such a career can lead to transition and adjustment issues due to the impact on identity (Lavallee, 2005, Gilbourne and Richardson, 2006, Roderick, 2006b, Brown and Potrac, 2009, Brownrigg et al., 2012). Personal Development (PD), defined through an emphasis for the continued growth of the whole person (Danish et al., 1993), not just the athlete, is one concept through which athletes are encouraged to develop their understanding of identity, beyond their athlete status, by engaging within practical activities outside of sport, whilst concurrently employed within sport (Stambulova et al., 2014). Study 1 of this thesis consisted of qualitative interviews with 12 apprentice footballers, to explore their understanding of identity and PD. Multiple players interpreted their club environments as encouraging them to view themselves as footballers, constantly, and become a 24hr (24/7) footballer. Concurrent to Study 1, a relationship formed with League Football Education (LFE) which led to the thesis occupying a lead role in the planning and delivery (Study 2, Part A) and evaluation (Study 2, Part B) of an informative workshop delivered to apprentice footballers for the 2015/16 season entitled My Future Today (MFT). The workshop aimed to support and develop apprentices' understanding of identity and PD. To achieve this, the MFT narrative was created to challenge the 24/7 narrative (Douglas and Carless, 2014). Study 2, Part B, consisted of qualitative interviews with 12 apprentice players who had attended MFT, to explore their understanding of identity and PD. Through MFT, interviewees' understanding of identity and PD was supported and developed, beyond that of the 24/7 footballer, during their career. Their understanding was underpinned in relation to their futures after football (i.e., appreciating the potential loss of identity that might accompany being released) and/or through a belief that this understanding would support them and their performances, as footballers, in the immediate sense. Indeed, empirical data suggested that the 24/7 approach was not seen as conducive to performance. The outcome of this understanding also resulted in numerous interviewees becoming more proactive in their post-apprenticeship transition planning.
Checking that a process is doing what it is supposed to is of critical importance in manufacturing, economics, environmental monitoring, patient monitoring, and more. Given sufficient and adequate ...analytical and process measurements made during the history of the well functioning process, a multivariate model of the process variation around a multivariate dynamic trajectory will, in principle, form a good basis for this checking. Such systems are often labeled process monitoring, real-time quality control (RTQC), PAT level 4, and advanced process control/fault detection and classification (APC/FDC). Here PAT stands for process analytical technology indicating the reliance on adequate and multiple data for this checking.
In practice, there are many difficulties in making an RTQC/PAT-4 system work well. Starting from an industrial example, the problems of constructing and implementing a well working checking system are discussed in relation to its different parts — analytical and process data, chemometrical and other methods for their modeling and analysis, and various forms of data management to handle the data flow and synchronization, as well as storage and retrieval. The display and interpretability of diagnostics and results are emphasized.
This paper presents results of a controlled trial of 3 child stuttering treatment strategies in 97 subjects. All 3 treatments (electromyography feedback, intensive smooth speech, and home-based ...smooth speech) were very successful in the long term for 70% of the group, with electromyography and home-based treatment appearing to be especially effective. (Author/PB)
The topic of study within includes the development and application of nonlinear control technologies on sampled systems. Discrete control structures are introduced that expand on existing ...differential geometric and predictive control methods. The differential geometric techniques are described from the error trajectory context, which are typically only derived for continuous application. The discrete error trajectory controllers introduced have one of two configurations. The first configuration requires satisfaction of the error trajectory objective at the next sampling interval through prediction of system behaviour over the controller sampling interval. This objective found limited success and it is observed that satisfaction of the error trajectory objective at discrete intervals does not generally result in the intended response. The second configuration minimizes the integrated distance from the error manifold defined by the error trajectory objective over the entire controller sampling interval. It is observed that this integrated error trajectory controller best emulates the intent of the continuous controller in the discrete domain. Techniques borrowed from predictive control are incorporated into the integrated error trajectory controller such as input move suppression and constraints to produce an optimal error trajectory controller, further improving performance.
The predictive control method introduced utilizes a transformation of the input space. The differentiating property of input transformation predictive control (ITPC) from other methods is the prediction technique that is capable of estimating the future behaviour of nonlinear systems through elementary matrix operations similar to the dynamic matrix control (DMC) prediction technique. This is achieved by separation of the steady state and dynamic system properties and the introduction of an intermediate state prediction layer. This allows for the nonlinear prediction of system behaviour without the need to numerically integrate the system model.
Two example systems are used to demonstrate application of the discrete error trajectory and ITPC on nonlinear controllers. Performance for these control structures is compared to technologies accepted within the control community for a broad range for characteristics including, computation efficiency, design effort and other nonlinear performance criteria, with favourable results.
Thesis
Master of Engineering (MEngr)
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