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Sensing and Automation for Protein Studies in Life Science

Yuan, Liang
http://rave.ohiolink.edu/etdc/view?acc_num=osu1311803164

Abstract Details

2011, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
In the past decade, sensing and automation have become more and more important in life science studies since they improve the productivity, safety, and efficiency of the experiments. These two areas are growing fast in different applications including protein crystallization, intercellular dynamic motion analysis, drug delivery, cell and tissue manipulation, etc. This dissertation makes two contributions in the field. The first contribution is for automating the protein crystallization process for the purpose of high-throughput, which involves the mixing of highly viscous monoolein with protein solution in the micro-liter level. In the past the mixing of highly viscous bio-samples was done manually, which was tedious and inefficient. The challenge is that bio-samples stick to everything they touch. To solve this problem, two novel mixing approaches are developed. One is to use a micro-capsule to mix viscous bio-samples under centrifugation, the other is to use a small block with multiple channels to mix viscous bio-samples under orbital shaking. The second contribution of this dissertation is in the automatic analysis of the intercellular object movement, which involves automatic tracking of neurofilaments. This dissertation introduces a novel approach which tracks neurofilament movement using the particle filtering algorithm with kinematic constraints to limit position and orientation of neurofilaments. In the first approach of automatic mixing, a microcapsule along with a micro-channel is designed. Under centrifugation, bio-samples travel back and forth through the micro-channel for mixing. In the second approach, an innovative block, which divides the micro-wells into two compartments, is designed. Mixing is achieved by orbital shaking which forces bio-samples to flow through micro-channels. In both approaches, visual sensors are used to observe the mixing process, which assists the theoretical analysis for understanding the physical insight of the two mixing process. X-ray diffraction results further verify that two approaches are effective and efficient. In the contribution of intercellular dynamical motion analysis, the particle filter algorithm is implemented according to the fact that neurofilament movement is confined within the boundaries of the axon. Piecewise cubic spline interpolation to model the path of the axon is used to limit both the orientation and location of the neurofilament in the particle tracking algorithm. Based on these two constraints, a prior dynamic state model that generates significantly fewer particles than generic particle filtering is developed. Experiments on real time-lapse image sequences of neurofilament movement demonstrate the efficacy and efficiency of this approach.
Yuan F. Zheng, Dr. (Advisor)
Charles Klein, Dr. (Committee Member)
Hooshang Hemami, Dr. (Committee Member)
141 p.
Electrical Engineering

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Citations

  • Yuan, L. (2011). Sensing and Automation for Protein Studies in Life Science [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1311803164

    APA Style (7th edition)

  • Yuan, Liang. Sensing and Automation for Protein Studies in Life Science. 2011. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1311803164.

    MLA Style (8th edition)

  • Yuan, Liang. "Sensing and Automation for Protein Studies in Life Science." Doctoral dissertation, Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1311803164

    Chicago Manual of Style (17th edition)

osu1311803164
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© 2011, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.