Modeling The Spatiotemporal Dynamics Of Cells In The Lung

Multiple research problems related to the lung involve a need to take into account the spatiotemporal dynamics of the underlying component cells. Two such problems involve better understanding the nature of the allergic inflammatory response to explore what might cause chronic inflammatory diseases such as asthma, and determining the rules underlying stem cells used to engraft decellularized lung scaffolds in the hopes of growing new lungs for transplantation. For both problems, we model the systems computationally using agent-based modeling, a tool that enables us to capture these spatiotemporal dynamics by modeling any biological system as a collection of agents (cells) interacting with each other and within their environment. This allows to test the most important pieces of biological systems together rather than in isolation, and thus rapidly derive biological insights from resulting complex behavior that could not have been predicted beforehand, which we can then use to guide wet lab experimentation. For the allergic response, we hypothesized that stimulation of the allergic response with antigen results in a response with formal similarity to a muscle twitch or an action potential, with an inflammatory phase followed by a resolution phase that returns the system to baseline. We prepared an agent-based model (ABM) of the allergic inflammatory response and determined that antigen stimulation indeed results in a twitch-like response. To determine what might cause chronic inflammatory diseases where the twitch presumably cannot resolve back to baseline, we then tested multiple potential defects to the model. We observed that while most of these potential changes lessen the magnitude of the response but do not affect its overall behavior, extending the lifespan of activated pro-inflammatory cells such as neutrophils and eosinophil results in a prolonged inflammatory response that does not resolve to baseline. Finally, we performed a series of experiments involving continual antigen stimulation in mice, determining that there is evidence in the cytokine, cellular and physiologic (mechanical) response consistent with our hypothesis of a finite twitch and an associated refractory period. For stem cells, we made a 3-D ABM of a decellularized scaffold section seeded with a generic stem cell type. We then programmed in different sets of rules that could conceivably underlie the cell\u27s behavior, and observed the change in engraftment patterns in the scaffold over selected timepoints. We compared the change in those patterns against the change in experimental scaffold images seeded with C10 epithelial cells and mesenchymal stem cells, two cell types whose behaviors are not well understood, in order to determine which rulesets more closely match each cell type. Our model indicates that C10s are more likely to survive on regions of higher substrate while MSCs are more likely to proliferate on regions of higher substrate

REALISTIC CADAVER MECHANICAL TESTING & QUANTITATIVE MAGNETIC RESONANCE IMAGING FOR EVALUATING KNEES THROUGHOUT WALKING

Introduction: Knees are subjected to daily physical activities, injuries and diseases, such as osteoarthritis (OA). Such complications represent significant costs (billions and thousands of USD/year for countries and individuals, respectively). Moreover, there is no OA cure and its risk factors (obesity, malalignment and injury) affect joints’ mechanical loading. Thus, knees must be studied under realistic loading conditions. Unfortunately, due to joints’ complexity (geometry, mechanical properties and loading), current experimental methods seldom achieve this. Quantitative magnetic resonance imaging (qMRI) potentially offers a non-invasive evaluation of tissue structure, biochemistry and mechanics, thereby facilitating injury or disease tracking if links between these properties and imaging outcomes were well established. However, the connections between tissue health and mechanical properties remain unclear, as is the relation between tissue- and joint-level biomechanics. Objective: Determine if tissue structure and joint function are related in whole cadaver knees under physiologically realistic loading conditions applied via a novel MRI-safe loading device. Methods: A novel MRI-safe knee loading device was designed, built and its repeatability assessed. Physiologic loading conditions (simulating walking) suitable for mechanical tests were determined via musculoskeletal (MSK) modelling, verified and validated against published data, and applied to a cadaver knee. To measure tibio- and patello-femoral (T-F and P-F) contact responses, a pressure sensing system was used in conjunction with the instrumented loading device. Then, to search for T2 relaxation-deformation associations, tibial and patellar cartilage deformations and T2 relaxation responses of other six ex-vivo knees subjected to axial compression (simulating standing) were measured and correlation analyses performed. Results & Discussion: The MRI-safe loading system developed was able to simulate healthy or pathologic gait with adequate repeatability (e.g., 1.23 to 2.91 CV% for compression, comparable to existing simulators), leading to generally consistent contact responses in agreement with published experimental and finite element studies. Cartilage thickness and T2 relaxation time magnitudes measured fell within expected values, while their loading-induced changes agreed with previous studies but exhibited larger variability. Moreover, a moderate negative correlation (r = -0.402, p = 0.019) was found between unloaded tibial cartilage thickness and T2 relaxation time, which may be linked to cartilage composition (relating collagen fibers and water content)

Numerical and Experimental Characterisation of Articular Cartilage – A Study on Biomechanics and Biotribology, Osteoarthritis and Tissue Engineering Solutions

Articular Cartilage (AC) is a soft tissue covering the articulating surface of human and animal joints. The tissue has remarkable and highly complex mechanical and wear properties allowing the joint to undergo complex kinematics and function correctly for several decades. However, trauma and degenerative joint diseases such as osteoarthritis (OA) can cause damage and excessive wear of the tissue and due to its limited regenerative capabilities, can severely compromise joint movement and impair the quality of life. OA is the most common type of degenerative joint disease and the primary cause of joint replacement surgery leading to high associated healthcare costs. Although the exact cause of this pathology remains unknown, it is thought to be mechanically induced via excessive and abnormal stresses and strains in AC which cause altered biochemical properties and a gradual decrease in the mechanical quality of the tissue. There is currently no available cure for OA and the disease is currently being diagnosed only via imaging techniques which are based upon morphological changes of the tissue, when the pathology is already in its advanced stages and has caused irreversible changes to the AC. In this respect, one of the greatest challenges to now remains the early diagnosis of OA, potentially by assessing biochemical and mechanical changes, allowing early treatments and prevention of disability thus improving the patient’s life. Hence, there is a need to apply fundamental engineering principles to the medical world in order to shed light on the pathogenesis and progression of OA. Furthermore, the need for artificial substitutes of AC has called for a deep understanding of the mechanical behaviour of the tissue in order to design and mimic the response of the real tissue in the most accurate manner. In this research a combination of numerical (finite element) and experimental techniques involving mechanical and tribological tests were used to fully characterise the mechanical behaviour of the tissue. Selective degradation of the AC constituents was then induced to simulate OA (OA-like AC) and the effect of different stages of degradation on the mechanical and tribological response as well as the wear properties of the tissue was investigated. The mechanical properties of osteoarthritic AC were then evaluated and compared to the OA-like AC in order to correlate similarities in the variations to the structure and the mechanical response as a result of degradation. Quantifying the mechanical response of the tissue at different stages of OA and different levels of degradation was done to ensure both a thorough understanding of the effect of the pathology’s progression on AC as well as to provide a potential map of mechanical quality and degradation, contributing to the potential future diagnosis of OA via mechanical parameters rather than morphological alone. Having investigated structural and mechanical variation in early OA, a promising solution to treat localised early OA and AC defects was also investigated as part of this research. In particular, novel micro fibrous tissue engineered scaffolds have been mechanically and tribologically assessed and compared to AC demonstrating the strong potential of matrix-assisted autologous chondrocyte implantation (MACI). Finally, the numerical models developed to characterise the AC using numerical – experimental methods, namely advanced biphasic models incorporating fine material descriptions such as intrinsic viscoelasticity as well as transverse isotropy, were applied to a patient specific 3D menisectomised tibio-femoral joint contact model in order to demonstrate the implications that the implementation of different AC models have for the prediction of the joint response to repeated walking cycles. The results obtained from the models were then used to predict the most likely location for the origin of mechanical damage and OA