Design Automation Methods and Tools for Microfluidics-Based Biochips

Microfluidics-based biochips, also known as lab-on-a-chip or bio-MEMS, are becoming increasingly popular for DNA analysis, clinical diagnostics, and the detection/manipulation of bio-molecules. These systems automate highly repetitive laboratory tasks by replacing cumbersome equipment with miniaturized and integrated systems, and they enable the handling of small amounts, e.g., nanoliters, of fluids. Thus they are able to provide ultra-sensitive detection at significantly lower costs per assay than traditional methods.

As the use of microfluidics-based biochips increases, their complexity is expected to become significant due to the need for multiple and concurrent assays on the chip, as well as more sophisticated control mechanisms for resource management. Time-to-market and fault tolerance are also expected to emerge as design considerations. As a result, current full-custom design techniques will not scale well for larger designs. There is a need to deliver the same level of CAD support to the biochip designer that the semiconductor industry now takes for granted.

Design Automation Methods and Tools for Microfluidics-Based Biochips deals with all aspects of design automation for microfluidics-based biochips. Experts have contributed chapters on various aspects of biochip design automation. Topics that are covered include device modeling; adaptation of bioassays for on-chip implementations; numerical methods and simulation tools; architectural synthesis, scheduling and binding of assay operations; physical design and module placement; fault modeling and testing; reconfiguration methods.

Preface. 1) F. Su, K. Chakrabarty and R. B. Fair, 'Microfluidics-based biochips: technology issues, implementation platforms, and design automation challenges' 2) Jun Zeng, 'Modeling and Simulation of Electrified Droplets and Its Application to Computer-Aid Design of Digital Microfluidics' 3) Jan Lienemann, Andreas Greiner, and Jan G. Korvink, 'Modelling, Simulation and Optimization of Electrowetting' 4) Xin Wang, Jacob White, Joe Kanapka, Wenjing Ye, Narayan Aluru, 'Algorithms in FastStokes and its application to micromachined device simulation' 5) Yi Wang, Qiao Lin, Tamal Mukherjee, 'Composable Behavioral Models and Schematic-Based Simulation of Electrokinetic Lab-on-a-Chips' 6) Michael D. Altman, Jaydeep P. Bardhan, Bruce Tidor, Jacob K. White, 'FFTSVD: A Fast Multiscale Boundary Element Method Solver Suitable for BioMEMS and Biomolecule Simulation' 7) Dmitry Vasilyev, Michal Rewienski, Jacob White, 'Macromodel generation for BioMEMS components using a stabilized Balanced Truncation plus Trajectory Piecewise Linear Approach' 8) A. S. Bedekar, Y. Wang, S. Krishnamoorthy, S. S. Siddhaye, and S. Sundaram, 'System-level simulation of pressure-driven and electrokinetic flow induced dispersion in lab-on-a-ch…' 9) R. Magargle, J.F. Hoburg, T. Mukherjee, 'Microfluidic Injector Models Based On Artificial Neural Networks' 10) A.B. Kahng, I.I. Mandoiu, S. Reda, X. Xu, and A.Z. Zelikovsky, 'Computer-Aided Optimization of DNA Array Design and Manufacturing' 11) Anton J. Pfeiffer, Tamal Mukherjee, and Steinar Hauan, 'Synthesis of Multiplexed Biofluidic Microchips' 12) Karl F. Böhringer, 'Modeling and Controlling Parallel Tasks in Droplet-Based Microfluidic Systems' 13) Eric J. Griffith, Srinivas Akella, Mark Goldberg, 'Performance Characterization of aReconfigurable Planar Array Digital Microfluidic System' 14) Sungroh Yoon, Luca Benini, Giovanni De Micheli, 'A Pattern Mining Method for High-throughput Lab-on-a-chip Data Analysis' Index.