The genus Restrepia is well known to orchid enthusiasts but its micromorphology has not been described, and its pollination and breeding systems have not been investigated. The aim of this investigation was, therefore, to add to existing knowledge so that the resultant data could be used to facilitate ex situ conservation initiatives. A detailed electron microscopy study (SEM) of the floral organs was performed. This confirmed the structure of the dorsal sepal and lateral petal osmophores, their secretory nature together with that of the synsepal and the labellum. It was postulated how, by manipulating different labellar surface textures, the flower might use these ‘tactile guides’ to steer the insect (fly) through the flower. The cirrhi were postulated to help by destabilising the pollinator in flight, trapping it and bringing about pollination. The papillate structure of the calli was established and their optical properties investigated. Media comparison investigations established that Western medium supported the highest germination rates and, with the addition of banana supplement, the highest rates for seedling growth and development. This represented the first protocol for axenic germination of Restrepia in the literature (Millner et al., 2008) and provided a tested methodology for investigating breeding systems and producing Restrepia plant material for both scientific and horticultural purposes. Self-pollinations were found to produce fewer embryos compared to cross-pollinations. The operation of self-incompatibility (SI) was confirmed by the study of pollen tube growth which further confirmed the time interval between pollination and fertilisation. A time line from pollination/fertilisation to flowering was established. The type of SI in operation was best explained by gametophytic incompatibility. This demonstrated that it was possible to raise Restrepia hybrids and species from seed, by performing intraspecific crosses so helping to preserve them for posterity and relieve pressure on wild populations. Narrow endemic Restrepia species face combined threats from habitat loss, habitat degradation and problems of viable seed production due to the effects of SI and inbreeding depression (ID). Recently developed online resources, such as GeoCAT, were used to perform a Red List assessment in order to identify the degree of threat individual species faced, both globally and nationally. All species were classified as facing substantial levels of threat; although this was lessened for populations in protected habitats. Conservation is needed for cultivated collections as well as these wild populations by keeping alive existing knowledge and expertise in growing these species.

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Our understanding of the complexity of the brain is limited by the data we can collect and analyze. Because of experimental limitations and a desire for greater detail, most investigations focus on just one aspect of the brain. For example, brain function can be studied at many levels of abstraction including, but not limited to, gene expression, protein interactions, anatomical regions, neuronal connectivity, synaptic plasticity, and the electrical activity of neurons. By focusing on each of these levels, neuroscience has built up a detailed picture of how the brain works, but each level is understood mostly in isolation from the others. It is likely that interaction between all these levels is just as important. Therefore, a key hypothesis is that functional units spanning multiple levels of biological organization exist in the brain. This project attempted to combine neuronal circuitry analysis with functional proteomics and anatomical regions of the brain to explore this hypothesis, and took an evolutionary view of the results obtained. During the process we had to solve a number of technical challenges as the tools to undertake this type of research did not exist. Two informatics challenges for this research were to develop ways to analyze neurobiological data, such as brain protein expression patterns, to extract useful information, and how to share and present this data in a way that is fast and easy for anyone to access. This project contributes towards a more wholistic understanding of the fruit fly brain in three ways. Firstly, a screen was conducted to record the expression of proteins in the brain of the fruit fly, Drosophila melanogaster. Protein expression patterns in the fruit fly brain were recorded from 535 protein trap lines using confocal microscopy. A total of 884 3D images were annotated and made available on an easy to use website database, BrainTrap, available at fruitfly.inf.ed.ac.uk/braintrap. The website allows 3D images of the protein expression to be viewed interactively in the web browser, and an ontology-based search tool allows users to search for protein expression patterns in specific areas of interest. Different expression patterns mapped to a common template can be viewed simultaneously in multiple colours. This data bridges the gap between anatomical and biomolecular levels of understanding. Secondly, protein trap expression patterns were used to investigate the properties of the fruit fly brain. Thousands of protein-protein interactions have been recorded by methods such as yeast two-hybrid, however many of these protein pairs do not express in the same regions of the fruit fly brain. Using 535 protein expression patterns it was possible to rule out 149 protein-protein interactions. Also, protein expression patterns registered against a common template brain were used to produce new anatomical breakdowns of the fruit fly brain. Clustering techniques were able to naturally segment brain regions based only on the protein expression data. This is just one example of how, by combining proteomics with anatomy, we were able to learn more about both levels of understanding. Results are analysed further in combination with networks such as genetic homology networks, and connectivity networks. We show how the wealth of biological and neuroscience data now available in public databases can be combined with the Brain- Trap data to reveal similarities between areas of the fruit fly and mammalian brain. The BrainTrap data also informs us on the process of evolution and we show that genes found in fruit fly, yeast and mouse are more likely to be generally expressed throughout the brain, whereas genes found only in fruit fly and mouse, but not yeast, are more likely to have a specific expression pattern in the fruit fly brain. Thus, by combining data from multiple sources we can gain further insight into the complexity of the brain. Neural connectivity data is also analyzed and a new technique for enhanced motifs is developed for the combined analysis of connectivity data with other information such as neuron type data and potentially protein expression data. Thirdly, I investigated techniques for imaging the protein trap lines at higher resolution using electron microscopy (EM) and developed new informatics techniques for the automated analysis of neural connectivity data collected from serial section transmission electron microscopy (ssTEM). Measurement of the connectivity between neurons requires high resolution imaging techniques, such as electron microscopy, and images produced by this method are currently annotated manually to produce very detailed maps of cell morphology and connectivity. This is an extremely time consuming process and the volume of tissue and number of neurons that can be reconstructed is severely limited by the annotation step. I developed a set of computer vision algorithms to improve the alignment between consecutive images, and to perform partial annotation automatically by detecting membrane, synapses and mitochondria present in the images. Accuracy of the automatic annotation was evaluated on a small dataset and 96% of membrane could be identified at the cost of 13% false positives. This research demonstrates that informatics technology can help us to automatically analyze biological images and bring together genetic, anatomical, and connectivity data in a meaningful way. This combination of multiple data sources reveals more detail about each individual level of understanding, and gives us a more wholistic view of the fruit fly brain.