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The development of easily fabricated, highly stable DNA-based microarray and continuous flow concentrating devices is vital for several biomedical and environmental applications. Nucleic acid biosensors can be used for genetic analysis, disease diagnosis, drug discovery, food and water quality control and more, however methods of fabrication are tedious, and the longevity of sensors is compromised by the fragility of the sensing component. In this report, the fabrication and characterization of two biosensing modalities – microarrays and microgels – composed of Rolling Circle Amplification (RCA) product in poly(oligoethylene glycol methacrylate) (POEGMA) hydrogels are investigated. RCA product microarrays were developed by the sequential printing of aldehyde and hydrazide functionalized POEGMA precursors on nitrocellulose paper, exploiting rapid gelling via hydrazone crosslinking to generate thin film hydrogel sensing arrays. POEGMA/RCA product microgels for affinity column applications were synthesized using an inverse emulsion polymerization technique. Inkjet printing evenly deposited RCA product in all wells, with POEGMA effectively stabilizing DNA on the cellulose substrate. Hybridization of complementary probe to the encapsulated RCA product was optimized, yielding a signal to noise ratio of ~4 for a large range of probe concentrations. Microgels were successfully synthesized in the size range of 10-60 μm diameter, and a linear model that can accurately predict size based on initiator and emulsifier concentration was developed. The encapsulation efficiency of RCA product in different sized microgels was explored, with larger microgels entrapping more product and the highest encapsulation efficiency calculated at 56%. These results demonstrate that POEGMA hydrogels can be utilized to encapsulate and stabilize RCA product in two distinct structures, providing a basis for the development of easily fabricated biosensors for more specific applications. / Thesis / Master of Applied Science (MASc)

No / Microarray data analysis and classification has demonstrated convincingly that it provides an effective methodology for the effective diagnosis of diseases and cancers. Although much research has been performed on applying machine learning techniques for microarray data classification during the past years, it has been shown that conventional machine learning techniques have intrinsic drawbacks in achieving accurate and robust classifications. This paper presents a novel ensemble machine learning approach for the development of robust microarray data classification. Different from the conventional ensemble learning techniques, the approach presented begins with generating a pool of candidate base classifiers based on the gene sub-sampling and then the selection of a sub-set of appropriate base classifiers to construct the classification committee based on classifier clustering. Experimental results have demonstrated that the classifiers constructed by the proposed method outperforms not only the classifiers generated by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods (bagging and boosting).

Microarray gene expression data for thousands of genes in many organisms is quickly becoming available. The information this data can provide the experimental biologist is powerful. This data may provide information clarifying the regulatory linkages between genes within a single metabolic pathway, or alternative pathway routes under different environmental conditions, or provide information leading to the identification of genes for selection in animal and plant genetic improvement programs or targets for drug therapy. Many analysis methods to unlock this information have been both proposed and utilized, but not evaluated under known conditions (e.g. simulations). Within this dissertation, an analysis method is proposed and evaluated for identifying independent and linked metabolic pathways and compared to a popular analysis method. Also, this same analysis method is investigated for its ability to identify regulatory linkages within a single metabolic pathway. Lastly, a variant of this same method is used to analyze time series microarray data. In Chapter 2, Factor Analysis is shown to identify and group genes according to membership within independent metabolic pathways for steady state microarray gene expression data. There were cases, however, where the allocation of all genes to a pathway was not complete. A competing analysis method, Hierarchical Clustering, was shown to perform poorly when negatively correlated genes are assumed unrelated, but performance improved when the sign of the correlation coefficient was ignored. In Chapter 3, Factor Analysis is shown to identify regulatory relationships between genes within a single metabolic pathway. These relationships can be explained using metabolic control analysis, along with external knowledge of the pathway structure and activation and inhibition of transcription regulation. In this chapter, it is also shown why factor analysis can group genes by metabolic pathway using metabolic control analysis. In Chapter 4, a Bayesian exploratory factor analysis is developed and used to analyze microarray gene expression data. This Bayesian model differs from a previous implementation in that it is purely exploratory and can be used with vague or uninformative priors. Additionally, 95% highest posterior density regions can be calculated for each factor loading to aid in interpretation of factor loadings. A correlated Bayesian exploratory factor analysis model is also developed in this chapter for application to time series microarray gene expression data. While this method is appropriate for the analysis of correlated observation vectors, it fails to group genes by metabolic pathway for simulated time series data. / Ph. D.