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Abstract Synthetic biological systems are used for a myriad of applications, including tissue engineered constructs for in vivo use and microengineered devices for in vitro testing. Recent advances in engineering complex biological systems have been fueled by opportunities arising from the combination of bioinspired materials with biological and computational tools. Driven by the availability of large datasets in the “omics” era of biology, the design of the next generation of tissue equivalents will have to integrate information from single‐cell behavior to whole organ architecture. Herein, recent trends in combining multiscale processes to enable the design of the next generation of biomaterials are discussed. Any successful microprocessing pipeline must be able to integrate hierarchical sets of information to capture key aspects of functional tissue equivalents. Micro‐ and biofabrication techniques that facilitate hierarchical control as well as emerging polymer candidates used in these technologies are also reviewed. 

The extracellular matrix (ECM) influences biological processes associated with tissue development and disease progression. However, robust cell‐free techniques to control fiber alignment of naturally derived ECM proteins, such as fibronectin (Fn), remain elusive. It is demonstrated that controlled hydrodynamics of Fn solutions at the air/fluid interface of porous tessellated polymer scaffolds (TPSs) generates suspended 3D fibrillar networks with alignment across multiple length scales (<1, 1–20 μm, extended to >1 mm). The direction of the fluid flow and the architecture of the polymeric supports influence protein solution flow profiles and, subsequently, the alignment of insoluble Fn fibrils. Aligned networks of fibrillar Fn characteristically alter fibroblast phenotype, indicated by increased directional orientation, enhanced nuclear and cytoskeletal polarity, and highly anisotropic and persistent cell motility when compared with nonaligned 3D networks and 2D substrates. Engineered extracellular matrices (EECMs) establish a critically needed tool for both fundamental and applied cell biology studies, with potential applications in diverse areas such as cancer biology and regenerative medicine.