High-throughput experimental methods, such as DNA and protein microarrays, have become ubiquitous and indispensable tools in biology and biomedicine, and the number of high-throughput technologies is constantly increasing. They provide the power to measure thousands of properties of a biological system in a single experiment and have the potential to revolutionize our understanding of biology and medicine. However, the high expectations on high-throughput methods are challenged by the problem to statistically model the wealth of data in order to translate it into concrete biological knowledge, new drugs, and clinical practices. In particular, the huge number of properties measured in high-throughput experiments makes statistical model selection and assessment exigent. To use high-throughput data in critical applications, it must be warranted that the models we construct reflect the underlying biology and are not just hypotheses suggested by the data. We must furthermore have a clear picture of the risk of making incorrect decisions based on the models. The rapid improvements of computers and information technology have opened up new ways of how the problem of model selection and assessment can be approached. Specifically, eScience, i.e. computationally intensive science that is carried out in distributed network envi- ronments, provides computational power and means to efficiently access previously acquired scientific knowledge. This thesis investigates how we can use eScience to improve our chances of constructing biologically relevant models from high-throughput data. Novel methods for model selection and assessment that leverage on computational power and on prior scientific information to "guide" the model selection to models that a priori are likely to be relevant are proposed. In addition, a software system for deploying new methods and make them easily accessible to end users is presented.

<p dir="ltr">The present work seeks to use High Throughput Experimentation (HTE) and continuous flow chemistry as tools to guide drug discovery and development. HTE allows for the grouping, miniaturization and automation of common operations so that hundreds of experiments can be done simultaneously employing less reagents and less time and promoting faster reaction optimization. Continuous flow chemistry provides a greater surface-area-to-volume ratio relative to batch synthesis, promoting greater mixing and heat transfer. Furthermore, it is a complete closed system protected from air exposure and light, ideal for the synthesis of light and oxygen sensitive, as well as toxic compounds. In this context, two themes on this work are Stargardt disease and the use of HTE and continuous flow for the green synthesis of small molecules. Chapter One introduces Stargardt disease and its key culprit, A2E. Through Chapter Two, we revisited the synthesis of A2E and utilized HTE and continuous flow to optimize the classical synthesis from 48 h to a residence time of 33 min, and yield from 49 to 78%. On Chapter Three, we studied the design and synthesis of IRE1 inhibitors for potential treatment of Stargardt disease. Our molecular docking approach afforded us the design of 66 compounds that were synthesized with the aid of HTE for reaction optimization and were evaluated by RT-qPCR and viability assays. Our studies indicated that three of our inhibitors have lower IC_50s than KIRA6 at inhibiting IRE1 activity in retinal cells. On Chapter Four we introduced the use continuous flow chemistry to make a process greener by revisiting the synthesis of Lomustine. Our method substituted DCM for a mixture of two green solvents, 2MeTHF and acetic acid as well as improved the yield and productivity of the reaction by increasing the solubility of the reaction intermediate produced. Finally, in Chapter Five, we studied the use of HTE to guide catalyst and solvent selection for development of a green synthesis of benzamides. Our method was further optimized by the use of microwave heating and was able to convert sterically hindered amines and carboxylic acids into the corresponding benzamides.</p>