Main description:

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.

Contents:

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