Description
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Individual cells remodel natural biomaterials, such as the extracellular matrix, through signal transduction and gene expression to influence network properties including stiffness, pattern formation, cell morphology, and molecular transport. In contrast, non-living synthetic polymer networks struggle to recreate this behavior due to their lack of autonomous actuators (i.e., cells), restricted computational inputs (e.g., light, pH, enzymes), and the limited orthogonality between network chemistries. Towards combining the advantages of living and synthetic systems, engineered living materials leverage genetic and metabolic programming of cells to establish control over material-wide properties. Here, we demonstrate that extracellular electron transfer (EET), a microbial respiration process, can serve as a tunable bridge between live cell metabolism and synthetic material properties. In our system, EET flux from Shewanella oneidensis to a copper catalyst controls atom-transfer radical polymerization, which cross-links methacrylate-functionalized macromers to form a polymer network (hydrogel). We first demonstrate that design rules from fluorescence parameterization in synthetic biology can be applied to predictably control polymer network mechanics using genetic circuits that regulate EET flux. Next, capitalizing on the modularity of genetic circuitry, we program transcriptional Boolean logic gates to regulate EET gene expression and design computational polymer networks that mechanically respond to combinations of molecular inputs. We then leverage the modularity and substrate scope of EET to direct another synthetic cross-linking reaction, copper(I)-catalyzed alkyne-azide cycloaddition (CuAAC), which can also be controlled using transcriptional logic. Finally, we utilize our EET-based material interface to logically control fibroblast morphology. Our results demonstrate the utility of EET as a bridge for controlling abiotic materials and how the rational design of genetic circuits can be leveraged to emulate physiological behavior in polymer networks.
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