Foreword |
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xi | |
Preface |
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xiii | |
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1 | (30) |
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1.1 Overview of Digital Microfluidics |
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4 | (6) |
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1.2 Overview of Continuous-Flow Microfluidics |
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10 | (2) |
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1.3 Design Automation and Optimization of Microfluidic Biochips |
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12 | (3) |
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1.4 Cyber-Physical Adaptation for Quantitative Analysis |
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15 | (2) |
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1.5 Security Assessment of Biomolecular Quantitative Analysis |
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17 | (1) |
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1.6 Proposed Research Methodology |
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18 | (8) |
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26 | (5) |
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I Real-Time Execution of Multi-Sample Biomolecular Analysis |
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31 | (78) |
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2 Synthesis for Multiple Sample Pathways: Gene-Expression Analysis |
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33 | (26) |
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2.1 Benchtop Protocol for Gene-Expression Analysis |
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34 | (5) |
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2.2 Digital Microfluidics for Gene-Expression Analysis |
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39 | (4) |
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2.3 Spatial Reconfiguration |
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43 | (3) |
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2.4 Shared-Resource Allocation |
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46 | (3) |
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2.5 Firmware for Quantitative Analysis |
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49 | (2) |
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51 | (6) |
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57 | (2) |
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3 Synthesis of Protocols with Temporal Constraints: Epigenetic Analysis |
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59 | (22) |
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3.1 Miniaturization of Epigenetic-Regulation Analysis |
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60 | (4) |
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64 | (4) |
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3.3 Task Assignment and Scheduling |
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68 | (5) |
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3.4 Simulation Results and Experimental Demonstration |
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73 | (6) |
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79 | (2) |
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4 A Microfluidics-Driven Cloud Service: Genomic Association Studies |
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81 | (28) |
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82 | (2) |
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4.2 Biological Pathway of Gone Expression and Omic Data |
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84 | (2) |
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4.3 Case Study: Integrative Multi-Omic Investigation of Breast Cancer |
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86 | (4) |
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4.4 The Proposed Framework: BioCyBig |
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90 | (4) |
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4.5 BioCyBig Application Stack |
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94 | (6) |
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4.6 Design of Microfluidics for Genomic Association Studies |
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100 | (5) |
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4.7 Distributed-System Interfacing and Integration |
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105 | (2) |
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107 | (2) |
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II High-Throughput Single-Cell Analysis |
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109 | (68) |
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5 Synthesis of Protocols with Indexed Samples: Single-Cell Analysis |
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111 | (32) |
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5.1 Hybrid Platform and Single-Cell Analysis |
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113 | (7) |
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5.2 Mapping to Algorithmic' Models |
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120 | (3) |
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5.3 Co-Synthesis Methodology |
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123 | (3) |
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5.4 Valve-Based Synthesizer |
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126 | (4) |
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5.5 Protocol Modeling Using Markov Chains |
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130 | (5) |
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135 | (6) |
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141 | (2) |
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6 Timing-Driven Synthesis with Pin Constraints: Single-Cell Screening |
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143 | (34) |
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145 | (5) |
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6.2 Multiplexed Control and Delay |
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150 | (10) |
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6.3 Sortex: Synthesis Solution |
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160 | (8) |
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168 | (8) |
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176 | (1) |
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III Parameter-Space Exploration and Error Recovery |
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177 | (46) |
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7 Synthesis for Parameter-Space Exploration: Synthetic Biocircuits |
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179 | (30) |
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180 | (3) |
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7.2 PSE Based on MEDA Biochips |
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183 | (2) |
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7.3 Sampling of Concentration Factor Space |
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185 | (3) |
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7.4 Synthesis Methodology |
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188 | (3) |
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191 | (2) |
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7.6 Physical-Level Synthesis |
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193 | (9) |
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202 | (5) |
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207 | (2) |
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8 Fault-Tolerant Realization of Biomolecular Assays |
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209 | (14) |
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8.1 Physical Defects and Prior Error-Recovery Solutions |
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209 | (2) |
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8.2 Adaptation of the C5 Architecture to Error Recovery |
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211 | (2) |
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213 | (3) |
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8.4 Dictionary-Based Error Recovery |
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216 | (3) |
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8.5 Experiment Results and Demonstration |
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219 | (2) |
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221 | (2) |
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IV Security Vulnerabilities and Countermeasures |
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223 | (60) |
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9 Security Vulnerabilities of Quantitative-Analysis Frameworks |
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225 | (24) |
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9.1 Threats Assessment of DMFBs |
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226 | (4) |
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9.2 Manipulation Attacks on Glucose-Test Results |
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230 | (8) |
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9.3 Attacks in the Presence of Cyber-Physical Integration |
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238 | (2) |
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9.4 DNA-Forgery Attacks on DNA Preparation |
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240 | (8) |
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248 | (1) |
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10 Security Countermeasures of Quantitative-Analysis Frameworks |
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249 | (26) |
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10.1 Microfluidic Encryption |
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250 | (6) |
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10.2 Aging Reinforces DMFB Security |
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256 | (1) |
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10.3 Encryption Security Analysis and Simulation Results |
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257 | (5) |
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10.4 DNA Barcoding as a Biochemical-Level Defense Mechanism |
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262 | (3) |
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10.5 Benchtop Demonstration of DNA Barcoding |
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265 | (8) |
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273 | (2) |
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11 Conclusion and Future Outlook |
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275 | (8) |
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275 | (3) |
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11.2 Future Research Directions |
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278 | (5) |
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Appendix A Proof of Theorem 5.1: A Fully Connected Routing Crossbar |
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283 | (4) |
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Appendix B Modeling a Fully Connected Routing Crossbar |
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287 | (4) |
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Appendix C Proof of Lemma 6.1: Derivation of Control Delay Vector * |
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291 | (6) |
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Appendix D Proof of Theorem 6.1: Derivation of Control Latency on |
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297 | (6) |
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Appendix E Proof of Lemma 7.1: Properties of Aliquot-Generation Trees |
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303 | (4) |
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E.1 Overlapping-Subproblems Property |
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304 | (1) |
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E.2 Optimal-Substructure Property |
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305 | (2) |
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Appendix F Proof of Theorem 7.1: Recursion in Aliquot-Generation Trees |
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307 | (6) |
Bibliography |
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313 | (28) |
Index |
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341 | |