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E-raamat: Optimization of Trustworthy Biomolecular Quantitative Analysis Using Cyber-Physical Microfluidic Platforms [Taylor & Francis e-raamat]

(Technical University of Munich, and the University of Breme, Germany.), (Duke University, USA.)
  • Formaat: 349 pages, 17 Tables, black and white; 163 Illustrations, black and white
  • Ilmumisaeg: 10-Jul-2020
  • Kirjastus: CRC Press
  • ISBN-13: 9781003053187
  • Taylor & Francis e-raamat
  • Hind: 170,80 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 244,00 €
  • Säästad 30%
  • Formaat: 349 pages, 17 Tables, black and white; 163 Illustrations, black and white
  • Ilmumisaeg: 10-Jul-2020
  • Kirjastus: CRC Press
  • ISBN-13: 9781003053187

A microfluidic biochip is an engineered fluidic device that controls the flow of analytes, thereby enabling a variety of useful applications. According to recent studies, the fields that are best set to benefit from the microfluidics technology, also known as lab-on-chip technology, include forensic identification, clinical chemistry, point-of-care (PoC) diagnostics, and drug discovery. The growth in such fields has significantly amplified the impact of microfluidics technology, whose market value is forecast to grow from $4 billion in 2017 to $13.2 billion by 2023. The rapid evolution of lab-on-chip technologies opens up opportunities for new biological or chemical science areas that can be directly facilitated by sensor-based microfluidics control. For example, the digital microfluidics-based ePlex system from GenMarkDx enables automated disease diagnosis and can bring syndromic testing near patients everywhere.

However, as the applications of molecular biology grow, the adoption of microfluidics in many applications has not grown at the same pace, despite the concerted effort of microfluidic systems engineers. Recent studies suggest that state-of-the-art design techniques for microfluidics have two major drawbacks that need to be addressed appropriately: (1) current lab-on-chip systems were only optimized as auxiliary components and are only suitable for sample-limited analyses; therefore, their capabilities may not cope with the requirements of contemporary molecular biology applications; (2) the integrity of these automated lab-on-chip systems and their biochemical operations are still an open question since no protection schemes were developed against adversarial contamination or result-manipulation attacks. Optimization of Trustworthy Biomolecular Quantitative Analysis Using Cyber-Physical Microfluidic Platforms provides solutions to these challenges by introducing a new design flow based on the realistic modeling of contemporary molecular biology protocols. It also presents a microfluidic security flow that provides a high-level of confidence in the integrity of such protocols. In summary, this book creates a new research field as it bridges the technical skills gap between microfluidic systems and molecular biology protocols but it is viewed from the perspective of an electronic/systems engineer.

Foreword xi
Preface xiii
1 Introduction
1(30)
1.1 Overview of Digital Microfluidics
4(6)
1.2 Overview of Continuous-Flow Microfluidics
10(2)
1.3 Design Automation and Optimization of Microfluidic Biochips
12(3)
1.4 Cyber-Physical Adaptation for Quantitative Analysis
15(2)
1.5 Security Assessment of Biomolecular Quantitative Analysis
17(1)
1.6 Proposed Research Methodology
18(8)
1.7 Book Outline
26(5)
I Real-Time Execution of Multi-Sample Biomolecular Analysis
31(78)
2 Synthesis for Multiple Sample Pathways: Gene-Expression Analysis
33(26)
2.1 Benchtop Protocol for Gene-Expression Analysis
34(5)
2.2 Digital Microfluidics for Gene-Expression Analysis
39(4)
2.3 Spatial Reconfiguration
43(3)
2.4 Shared-Resource Allocation
46(3)
2.5 Firmware for Quantitative Analysis
49(2)
2.6 Simulation Results
51(6)
2.7
Chapter Summary
57(2)
3 Synthesis of Protocols with Temporal Constraints: Epigenetic Analysis
59(22)
3.1 Miniaturization of Epigenetic-Regulation Analysis
60(4)
3.2 System Model
64(4)
3.3 Task Assignment and Scheduling
68(5)
3.4 Simulation Results and Experimental Demonstration
73(6)
3.5
Chapter Summary
79(2)
4 A Microfluidics-Driven Cloud Service: Genomic Association Studies
81(28)
4.1 Background
82(2)
4.2 Biological Pathway of Gone Expression and Omic Data
84(2)
4.3 Case Study: Integrative Multi-Omic Investigation of Breast Cancer
86(4)
4.4 The Proposed Framework: BioCyBig
90(4)
4.5 BioCyBig Application Stack
94(6)
4.6 Design of Microfluidics for Genomic Association Studies
100(5)
4.7 Distributed-System Interfacing and Integration
105(2)
4.8
Chapter Summary
107(2)
II High-Throughput Single-Cell Analysis
109(68)
5 Synthesis of Protocols with Indexed Samples: Single-Cell Analysis
111(32)
5.1 Hybrid Platform and Single-Cell Analysis
113(7)
5.2 Mapping to Algorithmic' Models
120(3)
5.3 Co-Synthesis Methodology
123(3)
5.4 Valve-Based Synthesizer
126(4)
5.5 Protocol Modeling Using Markov Chains
130(5)
5.6 Simulation Results
135(6)
5.7
Chapter Summary
141(2)
6 Timing-Driven Synthesis with Pin Constraints: Single-Cell Screening
143(34)
6.1 Preliminaries
145(5)
6.2 Multiplexed Control and Delay
150(10)
6.3 Sortex: Synthesis Solution
160(8)
6.4 Experimental Results
168(8)
6.5
Chapter Summary
176(1)
III Parameter-Space Exploration and Error Recovery
177(46)
7 Synthesis for Parameter-Space Exploration: Synthetic Biocircuits
179(30)
7.1 Background
180(3)
7.2 PSE Based on MEDA Biochips
183(2)
7.3 Sampling of Concentration Factor Space
185(3)
7.4 Synthesis Methodology
188(3)
7.5 High-Level Synthesis
191(2)
7.6 Physical-Level Synthesis
193(9)
7.7 Simulation Results
202(5)
7.8
Chapter Summary
207(2)
8 Fault-Tolerant Realization of Biomolecular Assays
209(14)
8.1 Physical Defects and Prior Error-Recovery Solutions
209(2)
8.2 Adaptation of the C5 Architecture to Error Recovery
211(2)
8.3 System Design
213(3)
8.4 Dictionary-Based Error Recovery
216(3)
8.5 Experiment Results and Demonstration
219(2)
8.6
Chapter Summary
221(2)
IV Security Vulnerabilities and Countermeasures
223(60)
9 Security Vulnerabilities of Quantitative-Analysis Frameworks
225(24)
9.1 Threats Assessment of DMFBs
226(4)
9.2 Manipulation Attacks on Glucose-Test Results
230(8)
9.3 Attacks in the Presence of Cyber-Physical Integration
238(2)
9.4 DNA-Forgery Attacks on DNA Preparation
240(8)
9.5
Chapter Summary
248(1)
10 Security Countermeasures of Quantitative-Analysis Frameworks
249(26)
10.1 Microfluidic Encryption
250(6)
10.2 Aging Reinforces DMFB Security
256(1)
10.3 Encryption Security Analysis and Simulation Results
257(5)
10.4 DNA Barcoding as a Biochemical-Level Defense Mechanism
262(3)
10.5 Benchtop Demonstration of DNA Barcoding
265(8)
10.6
Chapter Summary
273(2)
11 Conclusion and Future Outlook
275(8)
11.1 Book Summary
275(3)
11.2 Future Research Directions
278(5)
Appendix A Proof of Theorem 5.1: A Fully Connected Routing Crossbar
283(4)
Appendix B Modeling a Fully Connected Routing Crossbar
287(4)
Appendix C Proof of Lemma 6.1: Derivation of Control Delay Vector *
291(6)
Appendix D Proof of Theorem 6.1: Derivation of Control Latency on
297(6)
Appendix E Proof of Lemma 7.1: Properties of Aliquot-Generation Trees
303(4)
E.1 Overlapping-Subproblems Property
304(1)
E.2 Optimal-Substructure Property
305(2)
Appendix F Proof of Theorem 7.1: Recursion in Aliquot-Generation Trees
307(6)
Bibliography 313(28)
Index 341
Mohamed Ibrahim was a Visiting Scholar with the Technical University of Munich, Germany, and the University of Bremen, Germany. He spent a total of three years as a Research and Development Engineer in the semiconductor industry where he worked on design-for-test and post-silicon validation methodologies for several system-on-chip (SoC) designs. His current research interests include SoC design and embedded systems, electronic design automation of LOC systems, Internet-of-Bio-Things, security and trust of bio-systems, and machine-learning applications of bio-systems. Dr. Ibrahim was a recipient of the Best Paper award at the 2017 IEEE/ACM Design, Automation, and Test in Europe Conference, the 2017 Postdoc Mobility award from the Technical University of Munich, Germany, two ACM conference travel awards from ACM-SIGBED in 2016 and ACM-SIGDA in 2017, and Duke Graduate School Fellowship in 2013.

Krishnendu Chakrabarty is the William H. Younger Distinguished Professor and Department Chair of Electrical and Computer Engineering, and Professor of Computer Science, at Duke University. He is a recipient of the National Science Foundation CAREER award, the Office of Naval Research Young Investigator award, the Humboldt Research Award from the Alexander von Humboldt Foundation, Germany, the IEEE Transactions on CAD Donald O. Pederson Best Paper Award (2015), the ACM Transactions on Design Automation of Electronic Systems Best Paper Award (2017), and over a dozen best paper awards at major conferences. He is also a recipient of the IEEE Computer Society Technical Achievement Award (2015), the IEEE Circuits and Systems Society Charles A. Desoer Technical Achievement Award (2017), the Semiconductor Research Corporation Technical Excellence Award (2018), and the Distinguished Alumnus Award from the Indian Institute of Technology, Kharagpur (2014). Prof. Chakrabartys current research projects include: testing and design-for-testability of integrated circuits and systems; digital microfluidics, biochips, and cyberphysical systems; data analytics for fault diagnosis, failure prediction, anomaly detection, and hardware security; neuromorphic computing systems.