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System-level modeling and simulation of the cell culture microfluidic biochip ProCell

机译:系统级建模和模拟细胞培养微流体生物芯片proCell

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摘要

Microfluidic biochips offer a promising alternative to a conventional biochemical laboratory. There are two technologies for the microfluidic biochips: droplet-based and flow-based. In this paper we are interested in flow-based microfluidic biochips, where the liquid flows continuously through pre-defined micro-channels using valves and pumps. We present an approach to the system-level modeling and simulation of a cell culture microfluidic biochip called ProCell, Programmable Cell Culture Chip. ProCell contains a cell culture chamber, which is envisioned to run 256 simultaneous experiments (viewed as a 16 × 16 matrix). We use an inverted fluorescence microscope to observe the experiments in real-time, allowing kinetic data analysis. We are able to automatically adjust the current experimental setup thus allowing, for the first time, conditional experiments. We propose a biochip architecture model and a comprehensive fault model that captures permanent faults occurring during chip operation. Using the proposed modeling and simulation framework, we perform an architectural level evaluation of two cell culture chamber implementations. A qualitative success metric is also proposed to evaluate chip performance in the presence of partial failures. Our results show that significant improvements in efficiency can be obtained using redundancy, providing improved chances to complete an experiment even in the presence of faults. This decreases the experiment repetition rate while increasing system productivity, saving time and reducing costs.
机译:微流体生物芯片为常规生化实验室提供了有希望的替代方法。微流体生物芯片有两种技术:基于液滴的技术和基于流动的技术。在本文中,我们对基于流动的微流生物芯片感兴趣,其中液体使用阀门和泵连续流过预定的微通道。我们提出了一种称为ProCell,可编程细胞培养芯片的细胞培养微流体生物芯片的系统级建模和仿真方法。 ProCell包含一个细胞培养室,可以进行256个同时实验(以16×16矩阵显示)。我们使用倒置荧光显微镜实时观察实验,从而进行动力学数据分析。我们能够自动调整当前的实验设置,从而首次允许进行条件实验。我们提出了一个生物芯片架构模型和一个全面的故障模型,可以捕获芯片运行期间发生的永久性故障。使用提出的建模和仿真框架,我们对两个细胞培养室实施方案进行了架构级别的评估。还提出了定性成功度量标准,以评估存在部分故障时的芯片性能。我们的结果表明,使用冗余可以显着提高效率,即使存在故障也可以提高完成实验的机会。这样可以降低实验重复率,同时提高系统生产率,节省时间并降低成本。

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