CDI-Type I:Engineering Massively Parallelized Fluidic Processors: From Data to Predictive Models to Functional Designs

CDI-I 型：工程大规模并行流体处理器：从数据到预测模型到功能设计

• 批准号: 1124814
• 负责人: Raghunathan Rengasamy
• 金额: $ 50万
• 依托单位: Texas Tech University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1124814&HistoricalAwards=false
• 关键词: CDI; Type; Engineering; Massively; Parallelized

Currently, microfluidic devices can produce millions of nanoliter-scale droplets allowing high throughput biological analysis. Given the multi-step nature of biological analysis, these droplets often need to be shuttled through a network of fluidic channels so that they can be merged with other reagent-loaded droplets, then sorted and eventually analyzed. Realizing this vision of an ultrafast fluidic bioprocessor in the laboratory is quite a daunting task because non-linearity in the motion of droplets through interconnected networks precludes full control over the position and timing of each and every droplet. The principal hypothesis of this work is that computational thinking approaches can lead to a paradigm shift in precision engineering of error-free fluidic processors by narrowing down the design space and yielding optimized solutions of network architecture. Preliminary data supports this hypothesis, motivating us to implement computational strategies to address this design challenge. First, predictive models of the basic fluidic components of a processor will be built. Second, predictive control strategies will be used to address the relative significance of passive approaches vis-à-vis active methods to regulate droplet trajectories in fluidic networks. Finally, specialized genetic algorithms (GAs) will be developed to optimize network architecture for desired processor functionality. Additional cyber aspects of the proposal include generation and analysis of tremendous amount of digital microscopy data capturing the non-linear dynamics of droplets in networks and efficient knowledge generation from this abundant data using the best available computational infrastructure. Thus, the proposed work cuts across several disciplines including control theory, systems engineering, computational science, non-linear dynamics, fluid mechanics, microfabrication and image processing. The results from this study will not only advance scientific and engineering frontiers in a variety of disciplines but will also lead to transformative impact in applications related to biological analysis, material synthesis, biosensing and disease diagnostics. The computational tools being developed in this work can be adapted to analyze complex networks in natural systems including microcirculation and transportation systems. Educational component of the project includes drawing graduate and undergraduate students to the visually striking microfluidics research and providing state-of-the-art training in interdisciplinary areas - yielding a workforce that is uniquely trained. In addition to disseminating the results through conference presentations and publications, the digital movies generated during the project will be stored on dedicated network servers for access to other users to eventually build a digital library of fluidic processor architectures.

当前，微流体设备可以生产数百万个纳米尺度的液滴，从而允许高通量生物学分析。鉴于生物学分析的多步骤，这些液滴通常需要通过流体通道网络将其穿梭，以便可以将它们与其他负载的液滴合并，然后进行分类并最终进行分析。实现实验室中超快流体生物处理器的这种愿景是一项艰巨的任务，因为通过互连网络的液滴运动的非线性性无法完全控制每个液滴的位置和时机。这项工作的主要假设是，计算思维方法可以通过缩小设计空间并产生网络体系结构的优化解决方案，从而导致无错误处理器的精确工程的范式转移。初步数据支持这一假设，促使我们实施计算策略来应对这一设计挑战。首先，将构建处理器基本流体组件的预测模型。其次，将使用预测控制策略来解决被动方法相对于活性方法的相对重要性，以调节流体网络中的液滴轨迹。最后，将开发专门的遗传算法（GAS）来优化所需的处理器功能的网络体系结构。该提案的其他网络方面包括对网络中液滴的非线性动态的大量数字显微镜数据的生成和分析，并使用最佳的可用计算基础结构从这些丰富的数据中产生了有效的知识。这就是拟议的工作削减了几个学科，包括控制理论，系统工程，计算科学，非线性动力学，流体力学，微结合和图像处理。这项研究的结果不仅将推进各种学科的科学和工程前沿，而且还将导致对与生物分析，材料合成，生物传感和疾病诊断相关的应用的变革影响。可以对这项工作中开发的计算工具进行调整，以分析包括微循环和运输系统在内的自然系统中的复杂网络。该项目的教育组成部分包括吸引学生和本科生的视觉上引人注目的微流体研究，并在跨学科领域提供最先进的培训，从而产生了一个受独特培训的劳动力。除了通过会议演示和出版物传播结果外，项目期间生成的数字电影还将存储在专用的网络服务器上，以访问其他用户，以最终构建流体处理器体系结构的数字库。