Digital Microfluidics for Systems Biology

This book is a modified version of the author s
thesis, "EWOD-based Digital Microfluidics as a Novel
Platform for Systems Biology . The author, having
mastered both medicine and engineering, believes the
real challenge in science and engineering today
is integration . For true integration, it is
important for an individual to have basic knowledge
about many different fields. However, having the
knowledge is not enough, since integration is not
simply adding things. Creative thinking is equally
critical, for integration is a way of creating novel
features or paradigms, which are often
incomprehensible before the integration. One of the
goals of this book is to demonstrate such
integration of various fields (digital
microfluidics, systems biology, high-throughput
imaging, machine vision, state machine, etc).
Another goal is to introduce state machine-based
gene transcription network modeling, which is not
included in the thesis. It is a novel way of
modeling complex gene regulation networks, and the
author believes it has potential of revolutionizing
genomics and shows the direction in which future
systems biology should move forward.

Autorentext
The author earned M.D. from Seoul National University College of Medicine and M.S. in electrical engineering from the University of Texas at Dallas (UTD). He is currently pursuing Ph.D. in electrical engineering at UTD. He has passion for creating new medicine by integrating medicine with engineering and computer science.

Klappentext
This book is a modified version of the author's thesis, "EWOD-based Digital Microfluidics as a Novel Platform for Systems Biology". The author, having mastered both medicine and engineering, believes the real challenge in science and engineering today is 'integration'. For true integration, it is important for an individual to have basic knowledge about many different fields. However, having the knowledge is not enough, since integration is not simply adding things. Creative thinking is equally critical, for integration is a way of creating novel features or paradigms, which are often incomprehensible before the integration. One of the goals of this book is to demonstrate such integration of various fields (digital microfluidics, systems biology, high-throughput imaging, machine vision, state machine, etc). Another goal is to introduce state machine-based gene transcription network modeling, which is not included in the thesis. It is a novel way of modeling complex gene regulation networks, and the author believes it has potential of revolutionizing genomics and shows the direction in which future systems biology should move forward.