Error-Tolerant Biochemical Sample Preparation with Microfluidic Lab-on-Chip

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.

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.

Part I: Introduction and Background. 1. Introduction. 2. Background. Part II: Literature review. 3. A Review on Error Recovery Methods with Microfluidic Biochips. Part III: Design Automation Methods. 4. Error-Correcting Sample Preparation with Cyberphysical Digital Microfluidic Lab-on-Chip. 5. Effect of Volumetric Split-Errors. 6. Error-Oblivious Sample Preparation. 7. Multi-target Sample Preparation On-demand. Part IV: Conclusions. 8. Conclusions and Future Directions. Part V: Appendix. A: Error-Correcting Sample Preparation with Cyberphysical Digital Microfluidic Lab-on-Chip