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: 1914796
• 负责人: Krishnendu Chakrabarty
• 金额: $ 44.99万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1914796&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”)微流控生物芯片(DMFBs)正在给高通量DNA测序和临床点诊断带来革命性的变化。使用DMFBs，生物检测协议被缩减到液滴大小，并通过基于软件的控制图案化电极阵列上的纳升液滴来执行。然而，向工业的技术过渡一直是具有挑战性的，因为今天的DMFBs受到几个关键限制：(1)液滴大小的限制；(2)用于实时检测和监测的传感器集成困难；以及(3)可靠性/产量问题。为了克服这些限制，微电极点阵(MEDA)生物芯片最近被开发出来，它在每个微电极上结合实时电容传感来检测液滴的性质和位置。这种‘传感地图’开启了网络物理MEDA生物芯片的令人兴奋的机会，这些芯片可以动态响应生物分析结果，执行实时错误恢复，并执行生物化学中的“如果-然后-否则”协议，这是支持具有紧密集成的芯片上实验室传感技术的下一代网络物理系统(CP)所必需的。尽管有如此巨大的前景，但在MEDA用于现实生物化学的开发中，一个重要的障碍来自于需要手动控制生物芯片上的生化协议。因此，这项研究的动机是需要能够在可编程的和网络物理的MEDA生物芯片上执行生物分子分析。为了充分利用MEDA的动态适应能力，需要一种能够灵活地响应实时传感器反馈的综合和运行时优化框架。因此，拟议的研究构成了实现基于MEDA的CPS的全面努力，从而产生新的应用，例如，使癌症治疗或智能城市污染监测的大气气溶胶测量能够取得突破。这旨在为MEDA开发一个综合的系统解决方案，包括硬件和软件方面的进步。具体研究成果包括：(1)建模和稳健控制器设计，包括基于离线模型的协议综合和基于在线学习的协议/模型自适应；(2)综合考虑MEDA特有的液滴操作的自适应和弹性综合技术；(3)多反应物合成的优化方法，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)基于实时传感、液滴跟踪和自适应MEDA特有的射流操作的容错；以及(5)MEDA生物芯片的设计、制造和试验台的设置，以及在个性化癌症治疗中细胞分析的软件控制下的实时适配演示。这些突破将把MEDA从一个用于演示液滴操作的探索性平台推进到一个成熟的平台，微生物学家和生物化学家可以用来实施现实的协议。该项目还包括广泛的教育和推广部分，包括课程开发、扩大本科生和研究生的实践研究机会以及国际合作。例如，MEDA-CPS将作为一个重要的例子，在新的本科和研究生课程中展示实时适应，这些课程涉及嵌入式控制和网络物理系统的建模、设计和分析。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。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Enhancing the Reliability of MEDA Biochips Using IJTAG and Wear Leveling

使用 IJTAG 和磨损均衡增强 MEDA 生物芯片的可靠性

• DOI: 10.1109/tcad.2020.3032629
• 发表时间: 2020
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Zhong, Zhanwei;Liang, Tung-Che;Chakrabarty, Krishnendu
• 通讯作者: Chakrabarty, Krishnendu

IJTAG-based Fault Recovery and Robust Microelectrode-Cell Design for MEDA Biochips

适用于 MEDA 生物芯片的基于 IJTAG 的故障恢复和鲁棒微电极单元设计

• DOI: 10.1109/tcad.2020.2986741
• 发表时间: 2020
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Zhong, Zhanwei;Chakrabarty, Krishnendu
• 通讯作者: Chakrabarty, Krishnendu

Multi-Target Sample Preparation Using MEDA Biochips

使用 MEDA 生物芯片制备多目标样品

• DOI: 10.1109/tcad.2019.2942002
• 发表时间: 2019
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Liang, Tung-Che;Chan, Yun-Sheng;Ho, Tsung-Yi;Chakrabarty, Krishnendu;Lee, Chen-Yi
• 通讯作者: Lee, Chen-Yi

Adaptive Droplet Routing for MEDA Biochips via Deep Reinforcement Learning

通过深度强化学习实现 MEDA 生物芯片的自适应液滴路由

• DOI: 10.23919/date54114.2022.9774737
• 发表时间: 2022
• 期刊: Automation and Test in Europe (DATE
• 作者: Elfar, Mahmoud;Liang, Tung-Che;Chakrabarty, Krishnendu;Pajic, Miroslav
• 通讯作者: Pajic, Miroslav

Parallel Droplet Control in MEDA Biochips using Multi-Agent Reinforcement Learning

• 发表时间: 2021
• 作者: Tung-Che Liang;Jin Zhou;Yun-Sheng Chan;Tsung-Yi Ho;K. Chakrabarty;Cy Lee