There have been several techniques developed in recent years to develop computer models of a variety of disease behaviors. Agent-based modeling is a discrete-based modeling approach used agents to represent individual cells that mechanically interact and secrete, consume or react to soluble products. It has become a powerful modeling approach, widely used by computational researchers. In this research, we utilized agent-based modeling to study and explore disease development, particularly in two applications, breast cancer and bioengineering experiments. We further proposed an error-minimization search approach and used it to estimate cellular parameters from multicellular in vitro data. In this dissertation, in the first study, we developed a 2D agent-based model that attempted to emulate the in vivo structure of breast cancer. The model was applied to describe the progression from DCIS into DCI. This model confirms that the interaction between tumor cells and the surrounding stroma in the duct plays a critical role in tumor growth and metastasis. This interaction depends on many mechanical and chemical factors that work with each other to produce tumor invasion of the surrounding tissue. In the second study, an in silico model was developed and applied to understanding the underlying mechanism of vascular-endothelial growth factor (VEGF) auto-regulation in REP and emulate the in vitro experiments as part of bioengineering research. This model may provide a system with robust predictive modeling and visualization that could enable discovery of the molecular mechanisms involved in age-related macular degeneration (AMD) progression and provide routers to the development of effective treatments. In the third and final study, a searching approach was applied to estimate cellular parameters from spatiotemporal data produced from bioengineered multicellular in vitro experiments. We applied a search method to an integrated cellular and multicellular model of retinal pigment epithelial cells to estimate the auto-regulation parameters of VEGF.

Computational modeling in the field of biomechanics is becoming increasingly popular and successful in practice for its ability to predict function and provide information that would otherwise be unobtainable. Through the application of these new and constantly improving methods, kinematics and joint contact characteristics in pathological conditions of femoroacetabular impingement (FAI) and total hip arthroplasty (THA) were studied using a lower extremity computational model. Patients presenting with FAI exhibit abnormal contact between the femoral neck and acetabular rim leading to surrounding tissue damage in daily use. THA is the replacement of both the proximal femur and acetabular region of the pelvis and is the most common surgical intervention for degenerative hip disorders. A combination of rigid osteoarticular anatomy and force vectors representing soft tissue structures were used in developing this model. Kinematics produced by healthy models were formally validated with experimental data from Burnfield et al. This healthy model was then modified to emulate the desired morphology of FAI and a THA procedure with a range of combined version (CV) angles. All soft tissue structures were maintained constant for each subsequent model. Data gathered from these models did not provide any significant differences between the kinematics of healthy and FAI but did show a large amount of variation in all THA kinematics including incidents of dislocation with cases of lower CV angles. With the results of these computational studies performed with this model, an increased understanding of hip morphology with regards to STS has been achieved.

Among all casting defects, shrinkage porosities could significantly reduce the strength of metal parts. As several critical components in aerospace and automotive industries are manufactured through casting processes, ensuring these parts are free of defects and are structurally sound is an important issue. This study investigates the formation of shrinkage-related defects in alloy solidification. To have a better understanding about the defect formation mechanisms, three sets of experimental studies were performed. In the first experiment, a real-time video radiography technique is used for the observation of pore nucleation and growth in a wedge-shaped A356 aluminum casting. An image-processing technique is developed to quantify the amount of through-thickness porosity observed in the real-time radiographic video. Experimental results reveal that the formation of shrinkage porosity in castings has two stages: 1-surface sink formation and 2- internal porosity evolution. The transition from surface sink to internal porosity is defined by a critical coherency limit of . In the second and third experimental sets, two Manganese-Steel (Mn-Steel) castings with different geometries are selected. Several thermocouples are placed at different locations in the sand molds and castings to capture the cooling of different parts during solidification. At the end of solidification, castings are sectioned to observe the porosity distributions on the cut surfaces. To develop alloys’ thermo-physical properties, MAGMAsoft (a casting simulation software package) is used for the thermal simulations. To assure that the thermal simulations are accurate, the properties are adjusted to get a good agreement between simulated and measured temperatures by thermocouples. Based on the knowledge obtained from the experimental observations, a mathematical model is developed for the prediction of shrinkage porosity in castings. The model, called “advanced feeding model”, includes 3D multi-phase continuity, momentum and pore growth rate equations which inputs the material properties and transient temperature fields, and outputs the feeding velocity, liquid pressure and porosity distributions in castings. To solve the model equations, a computational code with a finite-volume approach is developed for the flow calculations. To validate the model, predicted results are compared with the experimental data. The comparison results show that the advanced feeding model can accurately predict the occurrence of shrinkage porosity defects in metal castings. Finally, the model is optimized by performing several parametric studies on the model variables.

The goal of lung-protective mechanical ventilation is to provide life-sustaining support of gas exchange while minimizing the risk of ventilator-induced lung injury. Multi-frequency oscillatory ventilation (MFOV) was proposed as an alternative lung-protective modality, in which multiple frequencies of pressure and flow oscillations are delivered simultaneously at the airway opening and allowed to distribute throughout the lung in accordance with regional mechanical properties. The distribution of oscillatory flow is frequency-dependent, such that regions overventilated at one frequency may be underventilated at another. Thus the central thesis of this work was that ventilation heterogeneity is frequency-dependent, and therefore ventilation with multiple simultaneous frequencies can be optimized to reduce the risk of ventilator-induced lung injury. Simulations in computational models of distributed oscillatory flow and gas transport demonstrated the sensitivity of regional ventilation heterogeneity to subject size, ventilation frequency, and injury severity. Although the risk of injury in the model associated with strain or strain rate individually was minimized by single-frequency ventilation, the risk of injury associated with mechanical power in lung parenchymal tissue was minimized by MFOV. In an experimental model of acute lung injury, MFOV was associated with reductions in the magnitude and spatial gradient of regional lung strain estimated by four-dimensional CT image registration, as well as increased rates of regional gas transport estimated by wash-in of xenon tracer gas. In conclusion, computational models demonstrated the potential for optimization of MFOV waveforms, and experimental trials demonstrated evidence of improved regional ventilation during MFOV.

We present a novel scheme ("Categorical Basis Functions", CBF) for object class representation in the brain and contrast it to the "Chorus of Prototypes" scheme recently proposed by Edelman. The power and flexibility of CBF is demonstrated in two examples. CBF is then applied to investigate the phenomenon of Categorical Perception, in particular the finding by Bulthoff et al. (1998) of categorization of faces by gender without corresponding Categorical Perception. Here, CBF makes predictions that can be tested in a psychophysical experiment. Finally, experiments are suggested to further test CBF.

Microstructural evolution in a naturally-aged and cold-rolled AA6451 aluminum alloy during a non-isothermal annealing process, which leads to significant grain refinement, is investigated through: (a) conducting a comprehensive experimental analysis and (b) developing a computational modeling technique. The underlying mechanisms of annealing have been investigated through analysing interactive phenomena between precipitation and concurrent recovery and recrystallization. It is shown that the interactions between solute elements, clusters, and fine precipitates with dislocations restrict dynamic and static recovery during deformation and subsequent annealing. Inhibition of recovery favours recrystallization that initiates at 300oC and progresses through a nucleation and growth mechanism. Despite localized inhomogeneities, nucleation mainly occurs in non-recovered high energy sites which are uniformly distributed within the entire structure. Growth of the recrystallized nuclei is restricted by pinning precipitates that undergo a concurrent coarsening process. The fine, uniform distribution of recrystallized nuclei and their limited growth result in the formation of a fine-grained microstructure, after completion of recrystallization. The acquired knowledge has been used to develop a computational modeling technique for simulating microstructural evolution of the alloy. Microstructural states are simulated by integrating analytical approaches in a Monte Carlo algorithm. The effects of deformation-induced and pre-existing inhomogeneities, as well as precipitate coarsening and grain boundary pinning on the competitive recovery-recrystallization process are included in the simulation algorithm. The developed technique is implemented to predict the microstructural evolution during isothermal and non-isothermal annealing of AA6xxx sheets. A good quantitative agreement is found between the model predictions and the results from the experimental investigations.

Stem and progenitor cells have the ability to regulate fate decisions through asymmetric cells divisions. The coordinated choice of cell division symmetry in space and time contributes to the physiological development of tissues and organs. Conversely, deregulation of these decisions can lead to the uncontrolled proliferation of cells as observed in cancer. Understanding the mechanisms of cell fate choices is necessary for the design of biomimetic culture systems and the production of therapeutic cell populations in the context of regenerative medicine. Environmental signals can guide the fate decision process at the single level but the exact nature of these signals remains to be discovered. Gradients of factors are important during development and several methods have been developed to recreate gradients and/or pulses of factors in vitro. In the context of asymmetric cell division, the effect of the soluble factor environment on the polarization of cell surface receptors and intracellular proteins has not been properly investigated. We developed a finite-element model of a single cell in culture in which epidermal growth factor (EGF) was delivered through a micropipette onto a single cell surface. A two-dimensional approach initially allowed for the development of a set of metrics to evaluate the polarization potential with respect to different delivery strategies. We further analyzed a three-dimensional model in which conditions consistent with single cell polarization were identified. The benefits of finite-element modeling were illustrated through the demonstration of complex geometry effects resulting from the culture chamber and neighboring cells. Finally, physiological effects of in vitro polarization were analyzed at the single cell level in HeLa and primary cells. The potential of soluble factor signaling in the context of directed fate control was demonstrated. Long term phenotypical effects were studied using live-cell imaging which demonstrated the degree of heterogeneity of in vitro culture systems and future challenges for the production of therapeutic cell populations.

Previous studies performed on myelinated nerve fibers have shown that a high frequency alternating current stimulus can block impulse conduction. The current threshold at which block occurs increases as the blocking frequency increases. Cable models based on the Hodgkin-Huxley model are consistent with these results. Recent experimental studies on unmyelinated nerve have shown that at higher frequencies, the block threshold decreases. When the block threshold is plotted as a function of frequency the resulting graph is distinctly nonmonotonic. Currently, all published models do not explain this behavior and the physiological mechanisms that create it are unknown. This difference in myelinated vs. unmyelinated block thresholds at high frequencies could have numerous clinical applications, such as chronic pain management. A large body of literature has shown that the specific capacitance of biological tissue decreases at frequencies in the kHz range or higher. Prior research has shown that introducing a frequency-dependent capacitance (FDC) to the Hodgkin-Huxley model will attenuate the block threshold at higher frequencies, but not to the extent that was seen in the experiments. This model was limited by the methods used to solve its higher order partial differential equation. The purpose of this thesis project is to develop a numerically stable method of incorporating the FDC into the model and to examine its effect on block threshold. The final, modified model will also be compared to the original model to ensure that the fundamental characteristics of action potential propagation remain unchanged.

Microstructural evolution in a naturally-aged and cold-rolled AA6451 aluminum alloy during a non-isothermal annealing process, which leads to significant grain refinement, is investigated through: (a) conducting a comprehensive experimental analysis and (b) developing a computational modeling technique. The underlying mechanisms of annealing have been investigated through analysing interactive phenomena between precipitation and concurrent recovery and recrystallization. It is shown that the interactions between solute elements, clusters, and fine precipitates with dislocations restrict dynamic and static recovery during deformation and subsequent annealing. Inhibition of recovery favours recrystallization that initiates at 300oC and progresses through a nucleation and growth mechanism. Despite localized inhomogeneities, nucleation mainly occurs in non-recovered high energy sites which are uniformly distributed within the entire structure. Growth of the recrystallized nuclei is restricted by pinning precipitates that undergo a concurrent coarsening process. The fine, uniform distribution of recrystallized nuclei and their limited growth result in the formation of a fine-grained microstructure, after completion of recrystallization. The acquired knowledge has been used to develop a computational modeling technique for simulating microstructural evolution of the alloy. Microstructural states are simulated by integrating analytical approaches in a Monte Carlo algorithm. The effects of deformation-induced and pre-existing inhomogeneities, as well as precipitate coarsening and grain boundary pinning on the competitive recovery-recrystallization process are included in the simulation algorithm. The developed technique is implemented to predict the microstructural evolution during isothermal and non-isothermal annealing of AA6xxx sheets. A good quantitative agreement is found between the model predictions and the results from the experimental investigations.

The goal of cancer immunotherapies is to boost the immune system's ability to detect tumor antigens and mount an effective anti-tumor immune response. Currently, adoptive T cell transfer therapy (ACT), the administration of ex vivo expanded autologous tumor-specific T cells, is one of the most promising immunotherapies under development; however, its efficacy has been limited so far with a mere 10% complete remission rate in the most successful clinical trials. The prolonged ex vivo culture process is a potential reason for this ineffectiveness because the transfused cells may reach replicative senescence and immunosenescence prior to patient transfer. The objective of this thesis is to offer two approaches towards an improvement of treatment efficacy. First, we generated a 'senescence metric' from the identification of biomarkers that can be used in the clinic towards predicting age and responsiveness of ex vivo expanded T cells. The second approach is to understand at the molecular level the changes that occur during ex vivo expansion to devise improved ACT protocols. In particular, we focused on the shift towards a pro-oxidizing environment and its potential effects on calcium signaling. The combined development and application of microfluidic technologies and computational models in this thesis facilitated our investigations of the phenotypic and signaling changes occurring in T cells during the progression towards immunosenescence. Our findings of altered T cell properties over long term culture provide insight for the design of future cancer immunotherapy protocols.