Modeling and design of optimal flow perfusion bioreactors for tissue engineering applications

Perfusion bioreactors have been used in different tissue engineering applications because of their consistent distribution of nutrients and flow-induced shear stress within the tissue-engineering scaffold. A widely used configuration uses a scaffold with a circular cross-section enclosed within a cylindrical chamber and inlet and outlet pipes which are connected to the chamber on either side through which media is continuously circulated. However, fluid-flow experiments and simulations have shown that the majority of the flow perfuses through the center. This pattern creates stagnant zones in the peripheral regions as well as in those of high flow rate near the inlet and outlet. This non-uniformity of flow and shear stress, owing to a circular design, results in limited cell proliferation and differentiation in these areas. The focus of this communication is to design an optimized perfusion system using computational fluid dynamics as a mathematical tool to overcome the time-consuming trial and error experimental method. We compared the flow within a circular and a rectangular bioreactor system. Flow simulations within the rectangular bioreactor are shown to overcome the limitations in the circular design. This communication challenges the circular cross-section bioreactor configuration paradigm and provides proof of the advantages of the new design over the existing one.     

Vector design for optimal protein expression

Many DNA constructs are generated for protein expression studies. Translational properties and mRNA stability are crucial aspects that have to be accounted for during DNA construction. An optimized vector for protein overexpression studies is described considering elements in the mature mRNA that influence translatability and stability. Recommendations regarding vector construction for Xenopus laevis embryo injection are provided, based on literature and experimental data. The 5'untranslated region (5'UTR) should be non-regulated, short, unstructured, and without AUG codons. The sequence around the start codon should match the initiation context of the species studied (ACCAUGG, for vertebrates), and the open reading frame should be cloned with its own stop codon, followed by a G or A residue. Furthermore, the 3'UTR should be non-regulated, and a strong polyadenylation signal must be included in DNA vectors. In RNA template vectors, the presence of a poly(A) or AC tail is essential for stability, as well as for translation efficiency in mRNA injection experiments. These aspects result in high-level expression of exactly the desired protein. Easily obtainable examples of the sequences [5'UTR, 3'UTR, and poly(A) signal] are suggested.     

An optimal design problem in rhythmometry

A trigonometric regression model is assumed for a problem involving circadian rhythm exhibited by peak expiratory flow. Experimental designs are sought with a view to estimating a particular nonlinear function of the parameters. Both optimal and nonoptimal, but more practicable, designs are derived and their relative efficiencies are established.     

Evaluating the design of flexible manufacturing systems

Evaluating the design of flexible manufacturing systems is complex. Developing a measure of performance useful for evaluating alternate designs continues to be interesting. Here, total productivity of the system is proposed as an appropriate measure. Specification of parameters based upon strategic considerations for this measure are discussed. Finally, the usefulness of the measure is demonstrated through an example.     

Sliding controller design for a nonlinear fermentation system

The design of a sliding controller for a continuous fermentation process is presented. The results obtained by simulation have proved the control scheme to be very robust. Regulation of substrate concentration at its optimal value has been achieved even though process parameters change their nominal values. Chattering effects are decreased by introducing a minor modification of the control variable around the sliding surface.     

The optimal configuration and workload allocation problem in flexible manufacturing systems

In this article we consider the problem of determining the minimum cost configuration (number of machines and pallets) for a flexible manufacturing system with the constraint of meeting a prespecified throughput, while simultaneously allocating the total workload among the machines (or groups of machines). Our procedure allows consideration of upper and lower bounds on the workload at each machine group. These bounds arise as a consequence of precedence constraints among the various operations and/or limitations on the number or combinations of operations that can be assigned to a machine because of constraints on tool slots or the space required to store assembly components. Earlier work on problems of this nature assumes that the workload allocation is given. For the single-machine-type problem we develop an efficient implicit enumeration procedure that uses fathoming rules to eliminate dominated configurations, and we present computational results. We discuss how this procedure can be used as a building block in solving the problem with multiple machine types.     

On modeling the design of cellular manufacturing systems

A new optimization model is discussed for the design of cellular manufacturing systems. It is based on an integer programming formulation that updates some other models by eliminating redundant machine assignment and cost coefficients dependent on cell configuration. To reduce computational burdens, a simplified integer programming model and a decomposition algorithm are proposed. Several computer solutions were performed to evaluate the performance of the new model. The computational results are discussed.     

Non-parametric optimal design in dose finding studies

We describe a non-parametric optimal design as a theoretical gold standard for dose finding studies. Its purpose is analogous to the Cramer-Rao bound for unbiased estimators, i.e. it provides a bound beyond which improvements are not generally possible. The bound applies to the class of non-parametric designs where the data are not assumed to be generated by any known parametric model. Whenever parametric assumptions really hold it may be possible to do better than the optimal non-parametric design. The goal is to be able to compare any potential dose finding scheme with the optimal non-parametric benchmark. This paper makes precise what is meant by optimal in this context and also why the procedure is described as non-parametric.     

Leukapheresis guidance and best practices for optimal chimeric antigen receptor T-cell manufacturing

Chimeric antigen receptor (CAR) T-cell therapy is an individualized immunotherapy that genetically reprograms a patient's T cells to target and eliminate cancer cells. Tisagenlecleucel is a US Food and Drug Administration-approved CD19-directed CAR T-cell therapy for patients with relapsed/refractory (r/r) B-cell acute lymphoblastic leukemia and r/r diffuse large B-cell lymphoma. Manufacturing CAR T cells is an intricate process that begins with leukapheresis to obtain T cells from the patient's peripheral blood. An optimal leukapheresis product is essential to the success of CAR T-cell therapy; therefore, understanding factors that may affect the quality or T-cell content is imperative. CAR T-cell therapy requires detailed organization throughout the entire multistep process, including appropriate training of a multidisciplinary team in leukapheresis collection, cell processing, timing and coordination with manufacturing and administration to achieve suitable patient care. Consideration of logistical parameters, including leukapheresis timing, location and patient availability, when clinically evaluating the patient and the trajectory of their disease progression must be reflected in the overall collection strategy. Challenges of obtaining optimal leukapheresis product for CAR T-cell manufacturing include vascular access for smaller patients, achieving sufficient T-cell yield, eliminating contaminating cell types in the leukapheresis product, determining appropriate washout periods for medication and managing adverse events at collection. In this review, the authors provide recommendations on navigating CAR T-cell therapy and leukapheresis based on experience and data from tisagenlecleucel manufacturing in clinical trials and the real-world setting.     

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.     

A model management systems approach to manufacturing systems design

Manufacturing systems design involves the solution of a complex series of interrelated problems. This complexity will increase in the future as manufacturing practices change to meet increased global competition. Research within manufacturing systems design has mainly been focused on finding improved models for solving particular problems, or extending existing modeling techniques. This has resulted in numerous modeling tools being available to support manufacturing systems design. However, little research work has been carried out into consolidating the existing theories and models. As a result, a large body of this work has not been applied in industry. Model management has evolved as a research area which investigates methods for storing, modifying, and manipulating models. This article describes a prototype model management system for manufacturing systems design. The objective here is not to develop “another” decision support system for manufacturing design, but to illustrate, through the development of a prototype system, a number of key ideas of how concepts from the area of model management systems can be used to support manufacturing systems design. The prototype model management system utilizes the structured modeling framework and uses an extended version of the structured modeling language. An important aspect of the prototype model management system is the incorporation of the model development task, thus allowing the system to be easily updated and adapted. The prototype system was evaluated using a range of queueing network models for manufacturing systems design.     

Prospect of manufacturing and design based on physiological polymorphism

Modern manufacturing and design should satisfy not only the requirements of high cost performance but also of the user. Besides that, the social environment which surrounds manufacturing is rapidly changing depending on new technologies. To create future products with user satisfaction, the effective use of human physiological data is essential. This is where knowledge of physiological anthropology can be applied. Physiological anthropologists have been pointing out a limit to the interpretation of the physiological data based on its average value. They have begun to notice that the physiological functions of humans show various types according to the blended effect of heredity and the surroundings. Adequate consideration of physiological polymorphism is indispensable to accomplish manufacturing that is well devised for human. In this study the concept of manufacturing and design based on physiological polymorphism is expressed. The target and the methodology for new manufacturing are discussed in seven fields, that is, welfare equipment, clothes, artificial tissue, sporting gear, furniture, building materials, and human interface. Through the above discussion, a procedure to achieve manufacturing and design based on physiological polymorphism is proposed.     

A loop layout design problem for flexible manufacturing systems

The interconnection pattern of the processing modules of a computerized manufacturing system affects its performance. In this article, we discuss a set of requirements that the interconnection network should satisfy. Subsequently, we concentrate on a simple and popular architecture, the loop network. The problem we address is to design the layout of the system so that the number of machines that the part types cross in their manufacturing process is minimized. We formulate the problem mathematically and solve it by a heuristic that obtains consistently better results than an earlier popular method.     

Toward an optimal design principle in relational biology

A vulnerability criterion is posed for a biological system within the context of representing the system as a relational set, i.e., a collection of components whose interdependency is described by means of a set of binary relations. The criterion provides a numerical value for each representation and therefore a means for comparing one representation against another. Choice of the criterion is such that the larger a numerical value a representation has then the less vulnerable to destruction is the system represented. Other things being equal, it is argued that the representation which endows the system with the least vulnerability is more likely to be a valid representation. A selection criterion is thereby achieved for narrowing down the choice ofa priori representations.     

Developing optimal non-linear scoring function for protein design

Motivation. Protein design aims to identify sequences compatible with a given protein fold but incompatible to any alternative folds. To select the correct sequences and to guide the search process, a design scoring function is critically important. Such a scoring function should be able to characterize the global fitness landscape of many proteins simultaneously. RESULTS: To find optimal design scoring functions, we introduce two geometric views and propose a formulation using a mixture of non-linear Gaussian kernel functions. We aim to solve a simplified protein sequence design problem. Our goal is to distinguish each native sequence for a major portion of representative protein structures from a large number of alternative decoy sequences, each a fragment from proteins of different folds. Our scoring function discriminates perfectly a set of 440 native proteins from 14 million sequence decoys. We show that no linear scoring function can succeed in this task. In a blind test of unrelated proteins, our scoring function misclassfies only 13 native proteins out of 194. This compares favorably with about three-four times more misclassifications when optimal linear functions reported in the literature are used. We also discuss how to develop protein folding scoring function.     

Bayesian optimal design for phase II screening trials

Summary .   Most phase II screening designs available in the literature consider one treatment at a time. Each study is considered in isolation. We propose a more systematic decision-making approach to the phase II screening process. The sequential design allows for more efficiency and greater learning about treatments. The approach incorporates a Bayesian hierarchical model that allows combining information across several related studies in a formal way and improves estimation in small data sets by borrowing strength from other treatments. The design incorporates a utility function that includes sampling costs and possible future payoff. Computer simulations show that this method has high probability of discarding treatments with low success rates and moving treatments with high success rates to phase III trial.