Current Opinion in Biomedical Engineering最新文献

Tissue patterning, the process of localizing different cell types to the right place, is critical for tissue function and thus a central goal for tissue engineering. Developing embryos employ diverse cell interaction-based mechanisms to robustly pattern tissues, such as specifying different regions of the central nervous system and aligning all the hair cells in the inner ear. These events range in lengthscale and must all be specified with cell-level precision, imposing challenges for recreating such patterns in vitro using conventional engineering approaches. Synthetic developmental biology as an emerging field provides a complementary approach for patterning tissues, by harnessing the molecular mechanisms used by natural tissues to program self-organizing behavior of the cells. Here we review advances in adapting these modules to program cells in culture. These modules could potentially be used for biomedical tissue engineering, as a complement to existing methods for generating morphologically complex multi-cell-type tissues in vitro.

Computational models of electrical stimulation, block and recording of autonomic nerves enable analysis of mechanisms of action underlying neural responses and design of optimized stimulation parameters. We reviewed advances in computational modeling of autonomic nerve stimulation, block, and recording over the past five years, with a focus on vagus nerve stimulation, including both implanted and less invasive approaches. Few models achieved quantitative validation, but integrated computational pipelines increase the reproducibility, reusability, and accessibility of computational modeling. Model-based optimization enabled design of electrode geometries and stimulation parameters for selective activation (across fiber locations or types). Growing efforts link models of neural activity to downstream physiological responses to represent more directly the therapeutic effects and side effects of stimulation. Thus, computational modeling is an increasingly important tool for analysis and design of bioelectronic therapies.

Protein circuit design is still in its infancy in terms of programmability. DNA nanotechnology, however, excels at this property and its community has created a myriad of circuits and assemblies following modular hierarchical design rules. In this mini-review, we reason that the rationales behind DNA nanotechnology can nurture protein circuit design, and the unique versatility orchestrated by groups of proteins can be further exploited to program cells. Community efforts to develop databases and design algorithms for standardizing and customizing protein modules could bring the programmability of protein circuits to a level comparable to DNA nanotechnology, ultimately empowering modular hierarchical protein circuit design.

Mammalian synthetic biology aims to engineer cellular behaviors for therapeutic applications, such as enhancing immune cell efficacy against cancers or improving cell transplantation outcomes. Programming complex biological functions necessitates an understanding of molecular mechanisms governing cellular responses to stimuli. Traditionally, synthetic biology has focused on transcriptional circuits, but recent advances have led to the development of synthetic protein circuits, leveraging programmable binding, proteolysis, or phosphorylation to modulate protein interactions and cellular functions. These circuits offer advantages including robust performance, rapid functionality, and compact design, making them suitable for cellular engineering or gene therapies. This review outlines the post-translational toolkit, emphasizing synthetic protein components utilizing proteolysis or phosphorylation to program mammalian cell behaviors. Finally, we focus on key differences between rewiring native signaling pathways and creating orthogonal behaviors, alongside a proposed framework for translating synthetic protein circuits from tool development to pre-clinical applications in biomedicine.

Engineering synthetic regulatory circuits with precise input–output behavior—a central goal in synthetic biology—remains encumbered by the inherent molecular complexity of cells. Non-linear, high-dimensional interactions between genetic parts and host cell machinery make it difficult to design circuits using first-principles biophysical models. We argue that adopting data-driven approaches that integrate modern machine learning (ML) tools and high-throughput experimental approaches into the synthetic biology design/build/test/learn process could dramatically accelerate the pace and scope of circuit design, yielding workflows that rapidly and systematically discern design principles and achieve quantitatively precise behavior. Current applications of ML to circuit design are occurring at three distinct scales: 1) learning relationships between part sequence and function; 2) determining how part composition determines circuit behavior; 3) understanding how function varies with genomic/host-cell context. This work points toward a future where ML-driven genetic design is used to program robust solutions to complex problems across diverse biotechnology domains.

The spatial distribution of the signaling molecules that mediate cell–cell communication and pattern formation is an important consideration for natural and engineered multicellular systems.

Signaling molecule concentration profiles directly impact cell response profiles, and various experimental techniques can be utilized to modulate these spatial distributions. Current strategies focused on physically or chemically modifying the extracellular space to affect signal distribution include performing experiments in microfluidic devices with dynamic user-controlled inputs and flow rates or adjusting the mesh sizes and protein binding affinities of extracellular matrix-mimicking hydrogels. Recent advances in synthetic biology have paved the way for new approaches that involve directly engineering the signaling molecules, their interactors, and their downstream effectors for fully orthogonal communication platforms.