Genomic DNA is the template of life - the entity which is characterized by a self-sustaining anatomical development, regulated signaling processes, the ability to reproduce and to respond to stimuli. Through what is classically known as the central dogma, the genome is transcribed into mRNA, which in turn is translated into proteins. The proteins take part in most, if not all, cellular processes, and it is by unraveling these processes that we can begin to understand life and disease-causing mechanisms. In vitro and in vivo assays are two levels at which protein communication may be studied, and which permit manipulation and control over the proteins under investigation. But in order to retrieve a representation of the processes as close to reality as possible, in situ analysis may instead be applied as a complement to the other two levels of study. In situ PLA offers the ability to survey protein activity in tissue samples and primary cell lines, at a single cell level, detecting single targets in their natural unperturbed environment.   In this thesis new developments of the in situ PLA are described, along with a new technique offering in situ enzyme-free detection of proximity between biomolecules. The dynamic range of in situ PLA has now been increased by several orders of magnitude to cover analogous ranges of protein expression; the output signals have been modified to offer a greater signal-to-noise ratio and to limit false-positive-rates while also extending the dynamic range further; simultaneous detection of multiple protein complexes is now possible; proximity-HCR is presented as a robust and inexpensive enzyme-free assay for protein complex detection. The thesis also covers descriptions on how the techniques may be simultaneously applied, also together with other techniques, for the multiple data-point acquisition required by the emerging realm of systems biology. A future perspective is presented for how much more information may be simultaneously acquired from tissue samples to describe biomolecular interactions in a new manner. This will allow new types of biomarkers and drugs to be discovered, and a new holistic understanding of life.

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Indiana University-Purdue University Indianapolis (IUPUI) / Traditional treatment strategy development for diseases involves the identification of target proteins related to disease states, and the interference of these proteins with drug molecules. Computational drug discovery and virtual screening from thousands of chemical compounds have accelerated this process. The thesis presents a comprehensive framework of computational drug discovery using system biology approaches. The thesis mainly consists of two parts: disease biomarker identification and disease treatment discoveries. The first part of the thesis focuses on the research in biomarker identification for human diseases in the post-genomic era with an emphasis in system biology approaches such as using the protein interaction networks. There are two major types of biomarkers: Diagnostic Biomarker is expected to detect a given type of disease in an individual with both high sensitivity and specificity; Predictive Biomarker serves to predict drug response before treatment is started. Both are essential before we even start seeking any treatment for the patients. In this part, we first studied how the coverage of the disease genes, the protein interaction quality, and gene ranking strategies can affect the identification of disease genes. Second, we addressed the challenge of constructing a central database to collect the system level data such as protein interaction, pathway, etc. Finally, we built case studies for biomarker identification for using dabetes as a case study. The second part of the thesis mainly addresses how to find treatments after disease identification. It specifically focuses on computational drug repositioning due to its low lost, few translational issues and other benefits. First, we described how to implement literature mining approaches to build the disease-protein-drug connectivity map and demonstrated its superior performances compared to other existing applications. Second, we presented a valuable drug-protein directionality database which filled the research gap of lacking alternatives for the experimental CMAP in computational drug discovery field. We also extended the correlation based ranking algorithms by including the underlying topology among proteins. Finally, we demonstrated how to study drug repositioning beyond genomic level and from one dimension to two dimensions with clinical side effect as prediction features.