Novel computational methods to understand neuronal networks and facilitate nucleic acid testing

This thesis is based on two distinct projects. One is focused on developing computational tools to facilitate assay design for nucleic acid testing methods. In the isothermal EXPonential Amplification Reaction (EXPAR), template sequences with similar thermodynamic characteristics perform very differently. To understand what causes this variability, we characterized the performance of 384 template sequences, and used this data to develop two computational methods to predict EXPAR template performance based on sequence: a position weight matrix approach with support vector machine classifier, and RELIEF attribute evaluation with Naive Bayes classification. The methods identified well and poorly performing EXPAR templates with 67-70% sensitivity and 77-80% specificity. Furthermore, our data suggest that variability in template performance is linked to specific sequence motifs. Cytidine, a pyrimidine base, is over-represented in certain positions of well-performing templates. Guanosine and adenosine, both purine bases, are over-represented in similar regions of poorly performing templates, frequently GA or AG dimers. Since polymerases have a higher affinity for purine oligonucleotides, polymerase binding to GA-rich regions of a single-stranded DNA template may promote non-specific amplification in EXPAR and other nucleic acid amplification reactions. We combined these methods into a computational tool that can accelerate new assay design by ruling out likely poor performers.;Another two computational tools are also developed to facilitate EXPAR and PROXimity Amplification Reaction (PROXAR) assay design. Another project is focused on network analysis. How does the brain, a complex network of interconnected neurons, give rise to biological function? To answer this question, we decomposed the C. elegans brain network into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. These sub-networks were introduced as "colored motifs". By coloring neurons in the network by their cell type, and analyzing the distribution and information content of these color motifs, we identified some common building blocks of the network and gained a better understanding of how the worm uses its neuronal network for signal transduction and how the neuronal network stores its information in the network structure.