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Cardiomyocytes (CMs) and fibroblast cells are two essential elements for cardiac tissue structure and function. The interactions between them can alter cardiac electrophysiology and thus contribute to cardiac diseases, such as arrhythmogenesis. One possible explanation is that fibroblasts can directly affect cardiac electrophysiology through electrical coupling with CMs. Therefore, detecting the electrical activities in the CM-fibroblast network is vital for understanding the coupling dynamics among them. Current commercialized platforms for studying cardiac electrophysiology utilize planar microelectrode arrays (MEAs) to record the extracellular field potential (FP) in real-time, but the prearranged electrode configuration highly limits the measurement capabilities at specific locations. Here, we report a custom-designed MEA device with a novel micropatterning method to construct a controlled network of neonatal rat CMs (rCMs) and fibroblast connections for monitoring the electrical activity of rCM-fibroblast co-cultures in a spatially controlled fashion. For the micropatterning of the co-culture, surface topographical features and mobile blockers were used to control the initial attachment locations of a mixture of rCMs and fibroblasts, to form separate beating rCM-fibroblast clusters while leaving empty space for fibroblast growth to connect these clusters. Once the blockers are removed, the proliferating fibroblasts connect and couple the separate beating clusters. Using this method, electrical activity of both rCMs and human-induced-pluripotent-stem-cell-derived cardiomyocytes (iCMs) was examined. The coupling dynamics were studied through the extracellular FP and impedance profile recorded from the MEA device, indicating that the fibroblast bridge provided an RC-type coupling of physically separate rCM-containing clusters and enabled synchronization of these clusters. 

Current rate of data generation and the need for real‐time data analytics can benefit from new computational approaches where computation proceeds in a massively parallel way while being scalable and energy efficient. Biological systems arising from interaction of living cells can provide such pathways for sustainable computing. Current designs for biocomputing leveraging the information processing units of the cells, such as DNA, gene, or protein circuitries, are inherently slow (hours to days speed) and, therefore, are primarily being considered for archival storage of information. On the contrary, electrically active cells that can synchronize in milliseconds and can be connected as networks to perform massively parallel tasks can transform biocomputing and lead to novel ways of high throughput information processing. Herein, coupled oscillator networks made of living cardiac muscle cells, or bio‐oscillators, is explored as collective computing components for solving computationally hard problems. An empirically validated circuit compatible macromodel is developed for the bio‐oscillators and the fibroblast cells acting as coupling elements, to faithfully reproduce the synchronization dynamics of the network and it is shown that such bio‐oscillator network can be scaled up to hundreds of nodes and be used to solve computationally hard problems faster than traditional heuristics‐based Boolean algorithms.