Characterization of Spatially Graded Biomechanical Scaffolds

Mechanobiology in Tendon, Ligament, and Skeletal Muscle Tissue Engineering

Tendon, ligament, and skeletal muscle are highly organized tissues that largely rely on a hierarchical collagenous matrix to withstand high tensile loads experienced in activities of daily life. This critical biomechanical role predisposes these tissues to injury, and current treatments fail to recapitulate the biomechanical function of native tissue. This has prompted researchers to pursue engineering functional tissue replacements, or dysfunction/disease/development models, by emulating in vivo stimuli within in vitro tissue engineering platforms-specifically mechanical stimulation, as well as active contraction in skeletal muscle. Mechanical loading is critical for matrix production and organization in the development, maturation, and maintenance of native tendon, ligament, and skeletal muscle, as well as their interfaces. Tissue engineers seek to harness these mechanobiological benefits using bioreactors to apply both static and dynamic mechanical stimulation to tissue constructs, and induce active contraction in engineered skeletal muscle. The vast majority of engineering approaches in these tissues are scaffold-based, providing interim structure and support to engineered constructs, and sufficient integrity to withstand mechanical loading. Alternatively, some recent studies have employed developmentally inspired scaffold-free techniques, relying on cellular self-assembly and matrix production to form tissue constructs. Whether utilizing a scaffold or not, incorporation of mechanobiological stimuli has been shown to improve the composition, structure, and biomechanical function of engineered tendon, ligament, and skeletal muscle. Together, these findings highlight the importance of mechanobiology and suggest how it can be leveraged to engineer these tissues and their interfaces, and to create functional multitissue constructs.

Exploring the potential of transfer learning for metamodels of heterogeneous material deformation

From the nano-scale to the macro-scale, biological tissue is spatially heterogeneous. Even when tissue behavior is well understood, the exact subject specific spatial distribution of material properties is often unknown. And, when developing computational models of biological tissue, it is usually prohibitively computationally expensive to simulate every plausible spatial distribution of material properties for each problem of interest. Therefore, one of the major challenges in developing accurate computational models of biological tissue is capturing the potential effects of this spatial heterogeneity. Recently, machine learning based metamodels have gained popularity as a computationally tractable way to overcome this problem because they can make predictions based on a limited number of direct simulation runs. These metamodels are promising, but they often still require a high number of direct simulations to achieve an acceptable performance. Here we show that transfer learning, a strategy where knowledge gained while solving one problem is transferred to solving a different but related problem, can help overcome this limitation. Critically, transfer learning can be used to leverage both low-fidelity simulation data and simulation data that is the outcome of solving a different but related mechanical problem. In this paper, we extend Mechanical MNIST, our open source benchmark dataset of heterogeneous material undergoing large deformation, to include a selection of low-fidelity simulation results that require ≈ 2 - 4 orders of magnitude less CPU time to run. Then, we show that transferring the knowledge stored in metamodels trained on these low-fidelity simulation results can vastly improve the performance of metamodels used to predict the results of high-fidelity simulations. In the most dramatic examples, metamodels trained on 100 high fidelity simulations but pre-trained on 60,000 low-fidelity simulations achieves nearly the same test error as metamodels trained on 60,000 high-fidelity simulations (1 - 1.5% mean absolute percent error). In addition, we show that transfer learning is an effective method for leveraging data from different load cases, and for leveraging low-fidelity two-dimensional simulations to predict the outcomes of high-fidelity three-dimensional simulations. Looking forward, we anticipate that transfer learning will enable us to better capture the influence of tissue spatial heterogeneity on the mechanical behavior of biological materials across multiple different domains.

Recent advances in 3D bioprinting of musculoskeletal tissues

The musculoskeletal system is essential for maintaining posture, protecting organs, facilitating locomotion, and regulating various cellular and metabolic functions. Injury to this system due to trauma or wear is common, and severe damage may require surgery to restore function and prevent further harm. Autografts are the current gold standard for the replacement of lost or damaged tissues. However, these grafts are constrained by limited supply and donor site morbidity. Allografts, xenografts, and alloplastic materials represent viable alternatives, but each of these methods also has its own problems and limitations. Technological advances in three-dimensional (3D) printing and its biomedical adaptation, 3D bioprinting, have the potential to provide viable, autologous tissue-like constructs that can be used to repair musculoskeletal defects. Though bioprinting is currently unable to develop mature, implantable tissues, it can pattern cells in 3D constructs with features facilitating maturation and vascularization. Further advances in the field may enable the manufacture of constructs that can mimic native tissues in complexity, spatial heterogeneity, and ultimately, clinical utility. This review studies the use of 3D bioprinting for engineering bone, cartilage, muscle, tendon, ligament, and their interface tissues. Additionally, the current limitations and challenges in the field are discussed and the prospects for future progress are highlighted.