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E-grāmata: Error-Tolerant Biochemical Sample Preparation with Microfluidic Lab-on-Chip [Taylor & Francis e-book]

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  • Formāts: 206 pages, 36 Tables, black and white; 103 Line drawings, black and white; 103 Illustrations, black and white
  • Sērija : Emerging Materials and Technologies
  • Izdošanas datums: 27-Jul-2022
  • Izdevniecība: CRC Press
  • ISBN-13: 9781003219651
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Error-Tolerant Biochemical Sample Preparation with Microfluidic Lab-on-ChipPalielināt
  • Formāts: 206 pages, 36 Tables, black and white; 103 Line drawings, black and white; 103 Illustrations, black and white
  • Sērija : Emerging Materials and Technologies
  • Izdošanas datums: 27-Jul-2022
  • Izdevniecība: CRC Press
  • ISBN-13: 9781003219651
Citas grāmatas par šo tēmu:
Taylor & Francis e-booksMore info about Taylor & Francis e-books
Rokasgrāmatas mājas lapa: https://www.taylorfrancis.com/books/9781003219651
Microfluidic biochips have gained prominence due to their versatile applications to biochemistry and health-care domains such as point-of-care clinical diagnosis of tropical and cardiovascular diseases, cancer, diabetes, toxicity analysis, and for the mitigation of the global HIV crisis, among others. Microfluidic Lab-on-Chips (LoCs) offer a convenient platform for emulating various fluidic operations in an automated fashion. However, because of the inherent uncertainty of fluidic operations, the outcome of biochemical experiments performed on-chip can be erroneous even if the chip is tested a priori and deemed to be defect-free. This book focuses on the issues encountered in reliable sample preparation with digital microfluidic biochips (DMFBs), particularly in an error-prone environment. It presents state-of-the-art error management techniques and underlying algorithmic challenges along with their comparative discussions.











Describes a comprehensive framework for designing a robust and error-tolerant biomedical system which will help in migrating from cumbersome medical laboratory tasks to small-sized LOC-based systems





Presents a comparative study on current error-tolerant strategies for robust sample preparation using DMFBs and reports on efficient algorithms for error-tolerant sample dilution using these devices





Illustrates how algorithmic engineering, cyber-physical tools, and software techniques are helpful in implementing fault tolerance





Covers the challenges associated with design automation for biochemical sample preparation





Teaches how to implement biochemical protocols using software-controlled microfluidic biochips

Interdisciplinary in its coverage, this reference is written for practitioners and researchers in biochemical, biomedical, electrical, computer, and mechanical engineering, especially those involved in LOC or bio-MEMS design.
Foreword xi
Acknowledgments xiii
Biographies xv
SECTION I Introduction and Background
Chapter 1 Introduction
3(12)
1.1 Basics of Microfluidic Lab-on-Chips
4(5)
1.1.1 Digital Microfluidic Lab-on-chips
5(1)
1.1.2 Biochips based on Micro-Electrode Dot-Array (MEDA) architecture
6(3)
1.2 Basics of sample preparation and volumetric split-errors
9(3)
1.3 Scope of the book
12(1)
1.4 Organization of the book
13(2)
Chapter 2 Background
15(12)
2.1 Preliminaries
15(3)
2.1.1 Mixing models
15(1)
2.1.2 Concentration and Dilution factors
16(1)
2.1.3 Automated dilution of a Sample Fluid
16(1)
2.1.3.1 Linear and serial dilution
17(1)
2.1.3.2 Exponential and interpolated dilution
18(1)
2.2 Prior-work on sample preparation
18(3)
2.3 Effect of volumetric split-errors on target concentration
21(1)
2.4 Error-correction during multi-target sample preparation
22(1)
2.5 Conclusion
23(4)
SECTION II Literature Review
Chapter 3 Error Recovery Methods for Biochips
27(18)
3.1 Design objectives for error-recovery
27(2)
3.2 Error Recovery with regular DMFBs
29(6)
3.2.1 Integrated control-path design and error recovery
29(1)
3.2.2 Synthesis of Protocols on DMFBs with operational variability
30(1)
3.2.3 Error recovery in cyber-physical DMFBs
31(1)
3.2.4 Dictionary-based Real-time Error Recovery
32(1)
3.2.5 Dynamic error recovery during sample preparation
33(1)
3.2.6 Redundancy-based error recovery in DMFBs
34(1)
3.3 Error-recovery with MEDA Biochips
35(7)
3.3.1 Droplet Size-Aware and Error-Correcting Sample Preparation
35(2)
3.3.2 Adaptive Error Recovery in MEDA biochips
37(1)
3.3.3 Roll-Forward Error Recovery in MEDA Biochips
38(4)
3.4 Conclusion
42(3)
SECTION III Design Automation Methods
Chapter 4 Error-Correcting Sample Preparation with Cyber-physical DMFBs
45(28)
4.1 Automated sample preparation
46(2)
4.1.1 Related Prior Work
46(1)
4.1.2 Roll-forward Scheme for Error Recovery
46(2)
4.2 Motivation and problem formulation
48(11)
4.2.1 Error modeling: effect of errors on target-CFs
49(2)
4.2.2 Impact of multiple errors on target-CF: error collapsing
51(1)
4.2.3 Critical and non-critical errors
52(4)
4.2.4 Cancellation of concentration error at the target
56(2)
4.2.5 Problem Formulation
58(1)
4.3 Error-correcting dilution algorithm
59(5)
4.3.1 Reaction path: critical operation
59(1)
4.3.2 Roll-forward error recovery
60(4)
4.4 Designing an LoC for implementing ECSP
64(1)
4.4.1 Description of the layout
64(1)
4.4.2 Simulation of error-correcting dilution
64(1)
4.5 Experimental Results
65(5)
4.6 Conclusions
70(3)
Chapter 5 Effect of Volumetric Split-Errors on Target-Concentration
73(18)
5.1 Error-recovery approaches: prior art
73(1)
5.2 Cyber-physical technique for error-recovery
74(3)
5.2.1 Compilation for error-recovery
74(1)
5.2.2 Working principle of cyber-physical-based DMFBs
75(2)
5.3 Effect of split-errors on target-CFs
77(3)
5.3.1 Single volumetric split-error
77(1)
5.3.2 Multiple volumetric split-errors
78(2)
5.4 Worst-case error in target-CF
80(4)
5.5 Maximum CF-error: A justification
84(5)
5.6 Conclusion
89(2)
Chapter 6 Error-Oblivious Sample Preparation with DMFBs
91(30)
6.1 Sample preparation using DMFBs
92(4)
6.1.1 Sample preparation
93(1)
6.1.2 Errors in DMFB
93(1)
6.1.2.1 Dispensing error
93(1)
6.1.2.2 Volumetric split error
93(1)
6.1.2.3 Critical/non-critical set of errors
94(1)
6.1.3 Summary of Prior Art
95(1)
6.2 EOSP: Main Idea
96(3)
6.2.1 Error-vector (E)
97(2)
6.3 Effect of errors
99(4)
6.3.1 Critical and Non-critical set of errors
100(1)
6.3.2 Effect of Multiple Errors on target-CFs
101(2)
6.4 Baseline approach to error-obliviousness
103(2)
6.5 Resulting methodology
105(7)
6.6 Experimental results
112(6)
6.7 Conclusions
118(3)
Chapter 7 Robust Multi-Target Sample Preparation On-Demand with DMFBs
121(26)
7.1 Motivation
123(1)
7.2 Basics of sample preparation
124(1)
7.3 Literature review
125(1)
7.4 On-demand multi-target dilution-problem (MTD)
126(4)
7.4.1 Problem definition
128(1)
7.4.2 Main results
129(1)
7.5 Rapid production of target-CFs on-the-fly
130(6)
7.5.1 Integer Linear Programming (ILP) formulation
131(1)
7.5.2 Approximation scheme
132(4)
7.6 Generating partial set of concentration factors
136(2)
7.7 Reduction of on-chip reservoirs
138(1)
7.8 Streaming of different source concentrations
139(2)
7.9 Experimental results
141(3)
7.9.1 Performance evaluation of ILP and the approximation scheme
141(1)
7.9.2 Performance evaluation of BCS scheme
142(1)
7.9.3 Performance evaluation of MTSE
142(1)
7.9.4 Performance of the integrated dilution scheme with MTSE
142(2)
7.10 Error-obliviousness
144(1)
7.11 Conclusions
145(2)
Chapter 8 Robust Multi-Target Sample Preparation with MEDA Biochips
147(30)
8.1 Preliminaries and Background
149(5)
8.1.1 Digital Microfluidics with MEDA
149(2)
8.1.2 Split-error
151(1)
8.1.3 Dispensing-error
152(1)
8.1.4 Minimization of waste droplets
153(1)
8.2 MTM: Main Idea
154(5)
8.3 Effect of dispensing errors on target-CF
159(4)
8.4 Problem Formulation
163(1)
8.5 Resulting Methodology
163(2)
8.5.1 Split-less ECF dilution forest
163(1)
8.5.2 Split-less dilution-tree for the target-CF
164(1)
8.6 Error-tolerance
165(1)
8.7 Error-free multiple target sample preparation
166(1)
8.8 Experimental results
167(5)
8.8.1 Single-Target Sample Preparation
168(1)
8.8.2 Multi-Target Sample Preparation
169(3)
8.9 Conclusions
172(5)
SECTION IV Summary
Chapter 9 Summary and Future Directions
177(4)
SECTION V Appendix
Appendix A Error-Correcting Sample Preparation with Cyber-physical DMFBs
181(22)
A.1 Cyber-physical system
181(1)
A.1.1 Sensing system
182(1)
A.1.1.1 Optical Sensing
182(1)
A.1.1.2 Charge-Coupled Device (CCD)-based Sensing
182(1)
A.1.1.3 Capacitive Sensing
183(1)
A.1.2 Integration of biochip and control software
184(2)
A.2 Error recovery in rollback and roll-forward approaches
186(1)
A.3 Snapshots of concentration errors
187(1)
A.4 Snapshots of the biochip layout and simulation
187(2)
References
189(14)
Index 203
Sudip Poddar received the B.Tech. degree in computer science and engineering from the Maulana Abul Kalam Azad University of Technology (formerly known as West Bengal University of Technology), West Bengal, India, in 2008. He received the M.Tech degree in computer science and engineering from the University of Kalyani, India, in 2012. He obtained his PhD degree in Engineering (Computer Science) from Indian Institute of Engineering Science and Technology, Shibpur, Kolkata, India in 2019. He is currently working as Postdoctoral Fellow at Johannes Kepler University (JKU) Linz, Austria. Prior to joining JKU, he worked as Postdoctoral Fellow at National Taiwan University of Science and Technology (NTUST), Taipei, Taiwan for six months (July 2019-December 2019). He has received Young Career Projects Award (2019) from Linz Institute for Technology (LIT), Govt. of Austria. He is the recipient of Research Associateship (RA) from CSIR (Council of Scientific and Industrial Research), MHRD, Govt. of India (2017-2020). His research interests include computer-aided design for microfluidic lab-on-chip and soft computing.

Bhargab B. Bhattacharya is Distinguished Visiting Professor of Computer Science & Engineering at Indian Institute of Technology (IIT) Kharagpur. Prior to that, he had been on the faculty of Indian Statistical Institute, Kolkata, for over 35 years. He received the B.Sc. degree in Physics from the Presidency College, Kolkata, B.Tech. and M.Tech. degrees in Radiophysics and Electronics, and the PhD degree in Computer Science, all from the University of Calcutta. His research area includes digital logic testing, and electronic design automation for integrated circuits and microfluidic biochips. He has published more than 400 papers, and he holds ten US patents. Dr. Bhattacharya is a Fellow of the Indian National Academy of Engineering, a Fellow of the National Academy of Sciences (India), and a Fellow of the IEEE.
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