Adaptive Protocol Synthesis and Error Recovery in Micro-Electrode-Dot-Array (MEDA) Microfluidic Biochips

微电极点阵列 (MEDA) 微流控生物芯片中的自适应协议合成和错误恢复

• 批准号: 2313498
• 负责人: Krishnendu Chakrabarty
• 金额: $ 44.99万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313498&HistoricalAwards=false
• 关键词: Adaptive; Protocol; Synthesis; Error; Recovery

Droplet-based ("digital") microfluidic biochips (DMFBs) are revolutionizing high-throughput DNA sequencing and point-of-care clinical diagnosis. Using DMFBs, bioassay protocols are scaled down to droplet size and executed through software-based control of nanoliter droplets on a patterned electrode array. However, technology transition to industry has been challenging as today's DMFBs suffer from several key limitations: (1) constraints on droplet size; (2) difficulty of sensor integration for real-time detection and monitoring; and (3) reliability/yield concerns. To overcome these limitations, micro-electrode-dot-array (MEDA) biochips have been recently developed, incorporating real-time capacitive sensing on every microelectrode to detect the property and location of a droplet. Such 'sensing maps' open up the exciting opportunity of cyber-physical MEDA biochips that can dynamically respond to bioassay outcomes, perform real-time error recovery, and execute "if-then-else" protocols from biochemistry necessary to support the next generation of cyber-physical systems (CPS) with tightly integrated lab-on-chip sensing technology. Despite such tremendous promise, a significant barrier in the exploitation of MEDA for realistic biochemistry arises from the need to manually control biochemical protocols on the biochip. This research is thus motivated by the need to enable the execution of biomolecular assays on programmable and cyber-physical MEDA biochips. To take full advantage of the dynamic adaptation capabilities of MEDA, there is a need for a synthesis and run-time optimization framework that can be agile in its ability to respond to real-time sensor feedback. The proposed research therefore constitutes a comprehensive effort towards the realization of MEDA-based CPS, resulting in new applications that would, for instance, enable breakthroughs in cancer treatment or atmospheric aerosol measurements for pollution monitoring in smart cities.This is aimed at developing an integrated system solution for MEDA that includes advances in both hardware and software. Specific research products include the following innovations: (1) Modeling and robust controller design, which will involve offline model-based protocol synthesis and online learning-based protocol/model adaptation; (2) Adaptive and elastic synthesis techniques that comprehensively incorporate all the MEDA-specific droplet operations; (3) Optimization methods for multiple-reactant synthesis, which will involve on-chip sample preparation and optimization of the fluidic steps associated with dilution, mixing, and the generation of concentration gradients; (4) Fault tolerance through error recovery based on real-time sensing, droplet tracking, and adaptive MEDA-specific fluidic operations; and (5) MEDA biochip design, fabrication, and testbed setup, and the demonstration of real-time adaptation under software control for cell analysis in personalized cancer treatment. These breakthroughs will advance MEDA from an exploratory platform used to demonstrate droplet manipulation, to a mature platform that microbiologists and biochemists can use for implementing realistic protocols. The project also has an extensive education and outreach component, including curriculum development, expansion of hands-on research opportunities for undergraduate and graduate students, and international collaboration. For instance, MEDA-CPS will be used as an important example to showcase real-time adaptation in new undergraduate and graduate courses on modeling, design, and analysis of embedded control and cyber-physical systems. Tutorials at top conferences and benchmark dissemination activities will benefit the broader research community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

基于液滴的（“数字”）微流体生物芯片（DMFB）正在革新高通量DNA测序和护理点临床诊断。使用DMFB，将生物测定协议缩放到液滴大小，并通过基于软件的纳米液液滴的控制在图案电极阵列上执行。但是，由于当今的DMFB遭受了几个关键局限性，因此向行业的技术过渡一直具有挑战性：（1）滴对液滴尺寸的限制； （2）传感器集成难以实时检测和监测； （3）可靠性/收益率关注。为了克服这些局限性，最近已经开发了微电极 - 点阵列（MEDA）生物芯片，并在每个微电极上都结合了实时电容感，以检测液滴的属性和位置。这种“传感地图”为可以动态响应生物测定结果，实时错误恢复并执行“ IF-then-Else”协议，从而为下一代网络物理系统（CPS）提供紧密集成的实验室式的实验室传感技术所需的“ IF-THEN-ELSE”协议，从而为网络物理Meda生物芯片提供了令人兴奋的机会。尽管有如此巨大的希望，但对MEDA对现实生物化学的剥削的重大障碍源于需要手动控制生物芯片上的生化方案。因此，这项研究是出于需要在可编程和网络物理MEDA生物芯片上执行生物分子测定法。为了充分利用MEDA的动态适应能力，需要一个合成和运行时优化框架，它可以敏捷地响应实时传感器反馈。因此，拟议的研究构成了实现基于MEDA的CP的全面努力，从而实现了新的应用，例如，将在癌症治疗或大气气溶胶测量中取得突破，以在智能城市进行污染监测。旨在为MEDA开发综合系统解决方案，包括硬件和软件中的进步。特定的研究产品包括以下创新：（1）建模和鲁棒控制器设计，该设计将涉及基于离线模型的协议合成和基于在线学习的协议/模型适应； （2）全面结合所有MEDA特异性液滴操作的自适应和弹性合成技术； （3）对多重合成的优化方法，该方法将涉及片上样品制备和与稀释，混合和浓度梯度产生相关的流体步骤的优化； （4）基于实时感测，液滴跟踪和自适应MEDA特异性流体操作，通过误差恢复的容错性； （5）MEDA Biochip设计，制造和测试床设置，以及在个性化癌症治疗中用于细胞分析的软件控制下实时适应的演示。这些突破将使MEDA从用于证明液滴操纵的探索平台上，并将微生物学家和生物化学家可以用于实施现实协议的成熟平台。该项目还具有广泛的教育和外展部分，包括课程开发，扩大本科生和研究生的动手研究机会以及国际合作。例如，MEDA-CP将被用作重要的例子，以展示有关嵌入式控制和网络物理系统的建模，设计和分析的新本科和研究生课程的实时适应。顶级会议和基准传播活动的教程将使更广泛的研究社区受益。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估标准通过评估来支持的。

项目成果

Formal Synthesis of Adaptive Droplet Routing for MEDA Biochips

MEDA 生物芯片自适应液滴路由的正式合成

• DOI: 10.1109/tcad.2021.3110190
• 发表时间: 2022
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Elfar, Mahmoud;Liang, Tung-Che;Chakrabarty, Krishnendu;Pajic, Miroslav
• 通讯作者: Pajic, Miroslav

Deep Reinforcement Learning-Based Approach for Efficient and Reliable Droplet Routing on MEDA Biochips

基于深度强化学习的方法，在 MEDA 生物芯片上实现高效可靠的液滴路由

• DOI: 10.1109/tcad.2022.3194808
• 发表时间: 2023
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Elfar, Mahmoud;Chang, Yi-Chen;Ku, Harrison Hao-Yu;Liang, Tung-Che;Chakrabarty, Krishnendu;Pajic, Miroslav
• 通讯作者: Pajic, Miroslav