EE 5349 – 001, Fundamentals of Bio-Nanotechnology
Course Number & Title: EE 5349
– 001, Fundamentals of Bio-Nanotechnology
Instructor: Prof. Samir Iqbal
Days and time: Mon and Wed, 2:30-3:50 pm
Room: PH 110
See WebCT
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In the last decade or so, nanotechnology has brought together scientists and engineers from diverse fields to collaborate and share their expertise and tools. One area where nanotechnology can have profound and far reaching impacts is the capability of sensing biological molecules and processes. The current drive to coalesce nano-scale devices and bio-systems takes its roots in the special abilities of nanofabrication at the scales of bacteria, viruses and DNA. The functionalizations of these devices have resulted in unprecedented sensitivity and selectivity. Such efforts have resulted in a large arsenal of rapid and robust nanoscale biosensors and systems. Owing to the promise of major breakthroughs, the Nanobiosensors are poised to become a > $50 B industry by 2015.
 
The goal of this course is to provide an insight into the fundamentals of nanotechnology in biological and biomedical research. This course focuses on solid-state materials, devices and systems, related to biological applications especially detection and sensing. Basic biological molecules and the importance of their detection are discussed. The state of the art in BioMEMS and bio-nanotechnology is covered with particular emphasis on top-down MEMS fabrication and bottom-up biochemistry. The need for the solid-state devices for biological sensing is addressed, e.g. novel DNA sequencing technologies that can greatly influence personalized medicine, forensics and cure. An introduction to the detection-enabling and performance-enhancing surface functionalization techniques is presented. The methods and issues of biological functionalization and the characterization techniques are introduced and discussed in detail. The course will culminate by correlating engineering and biological disciplines, placing basic biology in context of engineering models.
The course requires several homework assignments, one individual paper-review presentation, a group research proposal and two exams. The course material comprises a collection of research publications, tutorials and lecture notes. A list of reference books and related online material is provided.
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