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Permanent link (DOI): https://doi.org/10.7939/R3W88Q

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Robust Learning Algorithms for Bioengineering Systems Open Access

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Other title
Subject/Keyword
Bioreactor System
Gene Network
Modeling
Fault Detection
Statistical Methods
Monitoring
Transplant Rejection
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Nadadoor Srinivasan, Venkat R.
Supervisor and department
Ben-Zvi, Amos (Chemical and Materials Engineering)
Shah, Sirish (Chemical and Materials Engineering)
Examining committee member and department
Bonvin, Dominique (Automation Control Laboratory, EPFL)
McCaffrey, William (Chemical and Materials Engineering)
Shah, Sirish (Chemical and Materials Engineering)
Chen, Tongwen (Electrical and Computer Engineering)
Ben-Zvi, Amos (Chemical and Materials Engineering)
Department
Department of Chemical and Materials Engineering
Specialization

Date accepted
2011-11-29T21:39:31Z
Graduation date
2012-06
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
Biological engineering is a domain of study that involves applying known engineering principles to biological systems. Qualitative studies in the field of biology have undergone tremendous advancements in the last two decades but quantitation is still in its early stages due to various complexities involved in its design, control, and operation. The current state of research in the field of bioengineering involves mostly elementary quantitation of biological systems without a strong grasp into the fundamentals of engineering. Advanced learning algorithms can help overcome some of the problems generally associated with biological systems including model complexity, noisy measurements, and data scarcity. In the current study, bioengineering problems are viewed from process system engineering perspective with a focus on three aspects: modeling, monitoring, and fault detection. The three representative bioengineering problems chosen to cover the three aforementioned aspects are: Modeling a gene network: Accurate inference of gene network can provide information that can lead to new ideas for treating complex diseases. A novel algorithm for building gene networks from microarray datasets using a first principles differential equations model is proposed. The proposed algorithm was able to obtain a good estimate of the gene connectivity matrix for an experimental dataset on a nine gene network in Eschericia coli. Monitoring a microalgal bioreactor system: Monitoring of process conditions in algal cultures helps in maximizing oil productivity. A support vector regression based algorithm is proposed for monitoring the culture conditions of an algal bioreactor system. The multivariate sensor built using an experimental dataset gave good predictions for the concentrations of biomass, glucose and percentage oil content. Detection of transplant rejection: Early detection of graft rejection is mandatory to effectively treat and prevent cardiac dysfunction. An algorithm based on hypothesis testing is proposed for detecting biomarkers useful for detection of rejection. The chosen biomarkers are validated on publicly available microarray datasets. For these datasets, the biomarkers obtained based on the proposed method were able to achieve a good separation between the successful and failed transplant classes. The methodologies and strategies proposed in this thesis have helped in the modeling, monitoring, and fault detection of bioengineering systems.
Language
English
DOI
doi:10.7939/R3W88Q
Rights
License granted by Venkat Nadadoor (venkat@ualberta.ca) on 2011-11-28T23:41:28Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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