COMPUTER SIMULATION OF A HOLLOW-FIBER BIOREACTOR: HEPARAN REGULATED GROWTH FACTORS-RECEPTORS BINDING AND DISSOCIATION ANALYSIS

This thesis demonstrates the use of numerical simulation in predicting the behavior of proteins in a flow environment. A novel convection-diffusion-reaction computational model is first introduced to simulate fibroblast growth factor (FGF-2) binding to its receptor (FGFR) on cell surfaces and regulated by heparan sulfate proteoglycan (HSPG) under flow in a bioreactor. The model includes three parts: (1) the flow of medium using incompressible Navier-Stokes equations; (2) the mass transport of FGF-2 using convection-diffusion equations; and (3) the cell surface binding using chemical kinetics. The model consists of a set of coupled nonlinear partial differential equations (PDEs) for flow and mass transport, and a set of coupled nonlinear ordinary differential equations (ODEs) for binding kinetics. To handle pulsatile flow, several assumptions are made including neglecting the entrance effects and an approximate analytical solution for axial velocity within the fibers is obtained. To solve the time-dependent mass transport PDEs, the second order implicit Euler method by finite volume discretization is used. The binding kinetics ODEs are stiff and solved by an ODE solver (CVODE) using Newton’s backward differencing formula. To obtain a reasonable accuracy of the biochemical reactions on cell surfaces, a uniform mesh is used. This basic model can be used to simulate any growth factor-receptor binding on cell surfaces on the wall of fibers in a bioreactor, simply by replacing binding kinetics ODEs. Circulation is an important delivery method for natural and synthetic molecules, but microenvironment interactions, regulated by endothelial cells and critical to the molecule’s fate, are difficult to interpret using traditional approaches. Growth factor capture under flow is analyzed and predicted using computer modeling mentioned above and a three-dimensional experimental approach that includes pertinent circulation characteristics such as pulsatile flow, competing binding interactions, and limited bioavailability. An understanding of the controlling features of this process is desired. The experimental module consists of a bioreactor with synthetic endotheliallined hollow fibers under flow. The physical design of the system is incorporated into the model parameters. FGF-2 is used for both the experiments and simulations. The computational model is based on the flow and reactions within a single hollow fiber and is scaled linearly by the total number of fibers for comparison with experimental results. The model predicts, and experiments confirm, that removal of heparan sulfate (HS) from the system will result in a dramatic loss of binding by heparin-binding proteins, but not by proteins that do not bind heparin. The model further predicts a significant loss of bound protein at flow rates only slightly higher than average capillary flow rates, corroborated experimentally, suggesting that the probability of capture in a single pass at high flow rates is extremely low. Several other key parameters are investigated with the coupling between receptors and proteoglycans shown to have a critical impact on successful capture. The combined system offers opportunities to examine circulation capture in a straightforward quantitative manner that should prove advantageous for biological or drug delivery investigations. For some complicated binding systems, where there are more growth factors or proteins with competing binding among them moving through hollow fibers of a bioreactor coupled with biochemical reactions on cell surfaces on the wall of fibers, a complex model is deduced from the basic model mentioned above. The fluid flow is also modeled by incompressible Navier-Stokes equations as mentioned in the basic model, the biochemical reactions in the fluid and on the cell surfaces are modeled by two distinctive sets of coupled nonlinear ordinary differential equations, and the mass transports of different growth factors or complexes are modeled separately by different sets of coupled nonlinear partial differential equations. To solve this computationally intensive system, parallel algorithms are devised, in which all the numerical computations are solved in parallel, including the discretization of mass transport equations and the linear system solver Stone’s Implicit Procedure (SIP). A parallel SIP solver is designed, in which pipeline technique is used for LU factorization and an overlapped Jacobi iteration technique is chosen for forward and backward substitutions. For solving binding equations ODEs in the fluid and on cell surfaces, a parallel scheme combined with a sequential CVODE solver is used. The simulation results are obtained to demonstrate the computational efficiency of the algorithms and further experiments need to be conducted to verify the predictions

Fermentation: Metabolism, Kinetic Models, and Bioprocessing

Biochemical and metabolic interpretation of microbial growth is an important topic in bioreactor design. We intend to address valuable information about the relation of critical operation variables and the simulation of bioprocesses with unstructured and structured kinetic models. Process parameters such as nutrient supply, pH, dissolved oxygen, and metabolic end-products directly impact the physiology and metabolism of microorganisms. Changes in the membrane as well as cell viability are of interest since protein expression and maturation in prokaryota are directly related to membrane integrity. This chapter intends to deliver an insight of different alternatives in kinetic modeling

Development and software implementation of modelling tools for rapid fermentation process development using a parallel mini-bioreactor system

In order to establish a generic framework for the rapid development and optimisation of scalable fermentation processes, a novel methodology for simplifying model building was explored. This approach integrates small-scale fermentations with model-based experimental design (DoE) and predictive control strategies. In this study, four 1.4 litre vessels were characterised for power input, volumetric oxygen transfer coefficient (KLa) and mixing, to assess its potential for replicating cell culture rapidly. Engineering characterisation results showed excellent propeller operation over a range of 400-1200 rpm and up to the maximum motor output and under various air flow rates in fluid densities up to 4.21 Cp/mPa s (1.211 g/cm3 ). Limits were reached using glycerol (99%) at fluid viscosities of 500Cp/mPa s (1.253g/cm3 ) at 800 rpm and no air flow, hence experiencing the most resistance. This was the most taxing condition in terms of energy input into the system. Furthermore, we determined the efficient gas dispersion which is considered important for oxygen bubble dispersion in viscous fluids. The potential gas dispersion could be calculated as a function of both impeller speed, airflow rate, and the fluid viscosity. The calculations provided a working impeller speed of >263 rpm for >0.5 vvm air flow rate as preliminary parameters in our advanced modelling section. The key outcome of the KLa study was that the results showed suitable potential for mass transfer for high cell density fermentations, for each of the parallel stirred tank bioreactors. To assess the usability of the parallel bioreactors be used for bioprocess rapid development purposes Escherichia coli W3110 was characterised in the 1L WV vessels. So overall the experiments included testing the performance of the vessels engineering parameters and also the biological fermentations confirming that the system was suitable for parallel operation with high reproducibility. For model building, especially suited for the 4-reactor set up the parallel bioreactors a fractional factorial design was used, in which models could be rapidly built and implemented for further research. The screening and model optimisation helped to reduce the development time by using the parallel equipment. Batches of four reactors could be completed in parallel in which comparable experimental results were obtained rapidly for new fermentation models. Optical density measurements provided a quick off-line analysis of the growth curve of microbial populations, as compared to cell plate counts or dry weights that require more time. For the model development and the establishment of our integrated software modelling tool, a modified logistic model was developed to predict microbial growth kinetics. First-order kinetic models, logistic, and Gompertz models were used and comparatively analysed to assess the model fit to test batch data. The logistic model was favourable for mapping and simulating the later phases of bacterial growth, while the well-established exponential growth model predicted the early lag phase in our stoichiometric growth simulation software tool better. The initialisation of the previous fermentation model allowed us to build a statistical model, which was based on the engineering characteristics for optimisation of biomass. Therefore, batch nutrient supply with the aid of stoichiometric models could be tested and modelled. DoE model data was improved with metabolic flux analysis to develop an advanced feeding strategy by testing various metabolic pathways and the nutrients used in experimentation. Bacterial growth predictions and media optimisation were tested for maximising microbial biomass yields. We then modelled the dissolved oxygen concentration and substrate utilisation. The techniques and principles of dynamic flux balance analysis, mechanistic modelling, and stoichiometric mass balancing were used. The aim was to create and validate our integrated software based on advanced modelling for the parallel bioreactor systems and tested through application for E. coli fermentations. Optimising microbial biomass was the main target in this project, with the data collected from fermentation being the strongest comparator and validator. A new software for the integration of DoE and Dynamic flux balance analysis (DFBA) techniques with the intention of creating a working fermentation platform for the Multifors equipment via simulation and fermentation optimisation was the novel outcome of this research. The tool could provide functions for speeding up development time and control of parallel bioreactors

Development of small-scale fluidised bed bioreactor for 3D cell culture

Three-dimensional cell culture has gained significant importance by producing physiologically relevant in vitro models with complex cell-cell and cell-matrix interactions. However, current constructs lack vasculature, efficient mass transport and tend to reproduce static or short-term conditions. The work presented aimed to design a benchtop fluidised bed bioreactor (sFBB) for hydrogel encapsulated cells to generate perfusion for homogenous diffusion of nutrients and, host substantial biomass for long-term evolution of tissue-like structures and “per cell” performance analysis. The sFBB induced consistent fluidisation of hydrogel spheres while maintaining their shape and integrity. Moreover, this system expanded into a multiple parallel units’ setup with equivalent performances enabling simultaneous comparisons. Long term culture of alginate encapsulated hepatoblastoma cells under dynamic environment led to proliferation of highly viable cell spheroids with a 2-fold increase in cellular density over static (27.3 vs 13.4 million cells/mL beads). Upregulation of hepatic phenotype markers (transcription factor C/EBP-α and drug-metabolism CYP3A4) was observed from an early stage in dynamic culture. This environment also affected ERK1/2 signalling pathway, progressively reducing its activation while increasing it in static conditions. Furthermore, culture of primary human mesenchymal stem cells was evaluated. Cell proliferation was not observed but continuous perfusion sustained their viability and undifferentiated phenotype, enabling differentiation into chondrogenic and adipogenic lineages after de-encapsulation. These biological readouts validated the sFBB as a robust dynamic platform and the prototype design was optimised using computer-aided design and computational fluid dynamics, followed by experimental tests. This thesis proved that dynamic environment promoted by fluidisation sustains biomass viability in long-term cell culture and leads 3D cell constructs with physiologically relevant phenotype. Therefore, this bioreactor would constitute a simple and versatile tool to generate in vitro tissue models and test their response to different agents, potentially increasing the complexity of the system by modifying the scaffold or co-culturing relevant cell types

An integrated modelling/experimental framework for protein-producing cell cutures

Imperial Users onl

Kinetic models in industrial biotechnology - Improving cell factory performance

An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed

Approaches to in vitro tissue regeneration with application for human disease modeling and drug development

Reliable in vitro human disease models that capture the complexity of in vivo tissue behaviors are crucial to gain mechanistic insights into human disease and enable the development of treatments that are effective across broad patient populations. The integration of stem cell technologies, tissue engineering, emerging biomaterials strategies and microfabrication processes, as well as computational and systems biology approaches, is enabling new tools to generate reliable in vitro systems to study the molecular basis of human disease and facilitate drug development. In this review, we discuss these recently developed tools and emphasize opportunities and challenges involved in combining these technologies toward regenerative science.National Institute for Biomedical Imaging and Bioengineering (U.S.) (Grant 5R01EB010246-02)National Center for Advancing Translational Sciences (U.S.) (Grant 1UH2TR000496)United States. Defense Advanced Research Projects Agency (Cooperative Agreement W911NF-12-2-0039