<p>Breast cancer affects 12.5\% of women over their life time and tissue expansion (TE) is the most common technique for breast reconstruction after mastectomy. However, the rate of complications with TE can be as high as 15\%. Even though the first documented case of TE happened in 1957, there has yet to be a standardized procedure established due to the variations among patients and the TE protocols are currently designed based on surgeon's experience. There are several studies of computational and theoretical framework modeling skin growth in TE but these tools are not used in the clinical setting. This dissertation focuses on bridging the gap between the already existing skin growth modeling efforts and it's potential application in the clinical setting.</p> <p><br></p> <p>We started with calibrating a skin growth model based on porcine skin expansions data. We built a predictive finite element model of tissue expansion. Two types of model were tested, isotropic and anisotropic models. Calibration was done in a probabilistic framework, allowing us to capture the inherent biological uncertainty of living tissue. We hypothesized that the skin growth rate was proportional to stretch. Indeed, the Bayesian calibration process confirmed that this conceptual model best explained the data. </p> <p><br></p> <p>Although the initial model described the macroscale response, it did not consider any activity on the cellular level. To account for the underlying cellular mechanisms at the microscopic scale, we have established a new system of differential equations that describe the dynamics of key mechanosensing pathways that we observed to be activated in the porcine model. We calibrated the parameters of the new model based on porcine skin data. The refined model is still able to reproduce the observed macroscale changes in tissue growth, but now based on mechanistic knowledge of the cell mechanobiology.  </p> <p><br></p> <p>Lastly, we demonstrated how our skin growth model can be used in a clinical setting. We created TE simulations matching the protocol used in human patients and compared the results with clinical data with good agreement. Then we established a personalized model built from 3D scans of a patient unique geometry. We verified our model by comparing the skin growth area with the area of the skin harvested in the procedure, again with good agreement.</p> <p><br></p> <p>Our work shows that skin growth modeling can be a powerful tool to aid surgeons design TE procedures before they are actually performed. The simulations can help with optimizing the protocol to guarantee the correct amount of skin is growth in the shortest time possible without subjecting the skin to deformations that can compromise the procedure.</p>

<p dir="ltr">During many biological processes, cells move through their surrounding environment by exerting mechanical forces. The mechanical forces are mainly generated by molecular interactions between actin filaments (F-actins) and myosin motors within the actin cytoskeleton. Forces are transmitted to the surrounding extracellular matrix via adhesions. In this thesis, we employed agent-based computational models to study the interactions between F-actins and myosin in the motility assay and the cell migration process. In the first project, the myosin motility assay was employed to study the collective behaviors of F-actins. Unlike most of the previous computational models, we explicitly represent myosin motors. By running simulations under various conditions, we showed how the length, bending stiffness, and concentration affect the collective behavior of F-actins. We found that four distinct structures formed: homogeneous networks, flocks, bands, and rings. In addition, we showed that mobile motors lead to the formation of distinct F-actin clusters that were not observed with immobile motors. In the second project, we developed a 3D migration model to define how cells mechanically interact with their 3D environment during migration. Unlike cells migrating on a surface, cells within 3D extracellular matrix (ECM) must remodel the ECM and/or squeeze their body through the ECM, which causes 3D cell migration to be significantly more challenging than 2D migration. Our model describes realistic structural and rheological properties of ECM, cell protrusion, and focal adhesions between cells and the ECM.</p>

<div>Excessive deformation and stress of skin following reconstructive surgery plays a crucial role in wound healing, often leading to complications. Yet, despite of this concern, surgeries are still planned and executed based on each surgeon's training and experience rather than quantitative engineering tools. The limitations of current treatment planning and execution stem in part from the difficulty in predicting the mechanical behavior of skin, challenges in directly measuring stress in the operating room, and inability to predict the long term adaptation of skin following reconstructive surgery. Computational modeling of soft tissue mechanics has emerged as an ideal candidate to determine stress contours over sizable skin regions in realistic situations. Virtual surgeries with computational mechanics tools will help surgeons explore different surgeries preoperatively, make prediction of stress contours, and eventually aid the surgeon in planning for optimal wound healing. While there has been significant progress on computational modeling of both reconstructive surgery and skin mechanical and mechanobiological behavior, there remain major gaps preventing computational mechanics to be widely used in the clinical setting. At the preoperative stage, better calibration of skin mechanical properties for individual patients based on minimally invasive mechanical tests is still needed. One of the key challenges in this task is that skin is not stress-free in vivo. In many applications requiring large skin flaps, skin is further grown with the tissue expansion technique. Thus, better understanding of skin growth and the resulting stress-free state is required. The other most significant challenge is dealing with the inherent variability of mechanical properties and biological response of biological systems. Skin properties and adaptation to mechanical cues changes with patient demographic, anatomical location, and from one individual to another. Thus, the precise model parameters can never be known exactly, even if some measurements are available. Therefore, rather than expecting to know the exact model describing a patient, a probabilistic approach is needed. To bridge the gaps, this dissertation aims to advance skin biomechanics and computational mechanics tools in order to make virtual surgery for clinical use a reality in the near future. In this spirit, the dissertation constitutes three parts: skin growth and its incompatibility, acquisition of patient-specific geometry and skin mechanical properties, and uncertainty analysis of virtual surgery scenarios.</div><div>Skin growth induced by tissue expansion has been widely used to gain extra skin before reconstructive surgery. Within continuum mechanics, growth can be described with the split of the deformation gradient akin to plasticity. We propose a probabilistic framework to do uncertainty analysis of growth and remodeling of skin in tissue expansion. Our approach relies on surrogate modeling through multi-fidelity Gaussian process regression. This work is being used calibrate the computational model against animal model data. Details of the animal model and the type of data obtained are also covered in the thesis. One important aspect of the growth and remodeling process is that it leads to residual stress. It is understood that this stress arises due to the nonhomogeneous growth deformation. In this dissertation we characterize the geometry of incompatibility of the growth field borrowing concepts originally developed in the study of crystal plasticity. We show that growth produces unique incompatibility fields that increase our understanding of the development of residual stress and the stress-free configuration of tissues. We pay particular attention to the case of skin growth in tissue expansion.</div><div>Patient-specific geometry and material properties are the focus on the second part of the thesis. Minimally invasive mechanical tests based on suction have been developed which can be used in vivo, but these tests offer only limited characterization of an individual's skin mechanics. Current methods have the following limitations: only isotropic behavior can be measured, the calibration problem is done with inverse finite element methods or simple analytical calculations which are inaccurate, the calibration yields a single deterministic set of parameters, and the process ignores any previous information about the mechanical properties that can be expected for a patient. To overcome these limitations, we recast the calibration problem in a Bayesian framework. To sample from the posterior distribution of the parameters for a patient given a suction test, the method relies on an inexpensive Gaussian process surrogate. For the patient-specific geometry, techniques such as magnetic resonance imaging or computer tomography scans can be used. Such approaches, however, require specialized equipment and set up and are not affordable in many scenarios. We propose to use multi-view stereo (MVS) to capture patient-specific geometry.</div><div>The last part of the dissertation focuses on uncertainty analysis of the reconstructive procedure itself. To achieve uncertainty analysis in the clinical setting we propose to create surrogate and reduced order models, especially principal component analysis and Gaussian process regression. We first show the characterization of stress profiles under uncertainty for the three most common flap designs. For these examples we deal with idealized geometries. The probabilistic surrogates enable not only tasks such as fast prediction and uncertainty quantification, but also optimization. Based on a global sensitivity analysis we show that the direction of anisotropy of skin with respect to the flap geometry is the most important parameter controlled by the surgeon, and we show hot to optimize the flap in this idealized setting. We conclude with the application of the probabilistic surrogates to perform uncertainty analysis in patient-specific geometries. In summary, this dissertation focuses on some of the fundamental challenges that needed to be addressed to make virtual surgery models ready for clinical use. We anticipate that our results will continue to shape the way computational models continue to be incorporated in reconstructive surgery plans.</div>

Low back pain (LBP) remains the most prevalent and costly work-related disability worldwide and is directly associated with "physical" risk factors prevalent in manual material handling (MMH) tasks. Back-support exoskeletons (BSEs) are a promising ergonomic intervention to mitigate LBP risk, by reducing muscular exertion and spine loading. The purpose of this work was to help better understand both the "intended" and "unintended" consequences of BSE use on physical risk factors for LBP, as an essential prerequisite for the safe and effective implementation of this technology in actual workplaces. The first study assessed the effects of using two BSEs on objective and subjective responses during repetitive lifting involving symmetric and asymmetric postures. Wearing both BSEs significantly reduced peak levels of trunk extensor muscle activity and reduced energy expenditure. Such reductions, though, were more pronounced in the symmetric conditions and differed between the two BSEs tested. The second study quantified the assistive torque profiles of two passive BSEs using a computerized dynamometer, with both human subjects and a mannequin. Clear differences in torque magnitudes were evident between the BSEs, though both generated more assistive torques during flexion than extension. The third study estimated the effects of BSE use on lumbosacral compressive and shear forces during repetitive lifting using an optimization-based model. Using both BSEs reduced peak compression and anteroposterior shear forces, but these effects differed between tasks and BSE designs. Reductions in composite measures of trunk muscle activity did not correspond consistently with changes in spine forces when using a BSE. The fourth study quantified the effects of two passive BSEs on trunk stability and movement coordination during repetitive lifting. Some adverse effects on stability were evident for pelvis and thorax movements and coupling of these body segments, suggesting that caution is needed in selecting a BSE for a given MMH task. Overall, we found that the efficacy of BSEs is design- and task-specific. Important safety features of the exoskeletons were also identified, providing insights on their performance boundaries. Overall, the BSEs tested were more effective and safer in tasks closer to the mid-sagittal plane and with moderate degrees of trunk flexion. / Doctor of Philosophy / Low back pain (LBP) remains the most prevalent and costly work-related disability worldwide, and the risk of LBP is related to "physical" risk factors common in manual material handling (MMH) tasks. Back-support exoskeletons (BSEs) are a new ergonomic intervention that may reduce the risk of occupational LBP, by reducing muscular efforts and loads on the spine. For the safe use of BSEs, though, it is critical to better understand both the "intended" and "unintended" consequences of this emerging technology. In this dissertation, such consequences of BSE use were evaluated in the context of repetitive lifting tasks. The first study assessed the efficacy of two BSEs in terms of physical demands during repetitive lifting tasks involving a range of torso bending and twisting. Wearing both BSEs reduced the physical demands on back muscles and decreased energy consumption. Larger reductions, though, were observed in forward bending and such reductions differed between the two BSEs tested. The second study measured the amount of support provided by two BSEs using a new measurement method, which was examined for both human subjects and a mannequin. Clear differences in the BSE support were evident between the BSEs, and both devices generated more support during torso forward bending than returning upright. The third study estimated the effects of BSE use on low back loadings during repetitive lifting using a computational model. Using both BSEs reduced loads on the low back region, though such reductions were task-specific and depended on the BSE design. The fourth study quantified the effects of the BSE use on torso stability and movement patterns during repetitive lifting. Some adverse effects on stability were evident for lower and upper torso, suggesting that caution is needed in selecting a BSE for a given MMH task. Findings from this work show the potential benefits of BSEs for use in MMH tasks, yet such benefits can depend on the BSE design and the MMH task they are used for. Further, BSE use can lead to adverse effects, especially with tasks involving extreme working postures.