<p>Protein tyrosine phosphorylation is an essential posttranslational modification that controls cell signaling involving various biological processes, including cell growth, proliferation, migration, survival, and death. Balancing tyrosine phosphorylation levels is necessary for normal and pathological states, and this reversible mechanism occurs through protein tyrosine kinases and phosphatases. Advancements in instrumentation and applying conventional biochemical and genetic methods have led to cell signaling studies and pharmaceutical development discoveries. However, there is still a lack of understanding of tyrosine phosphatases' mechanisms, substrates, and activities within complex networks. The challenges remain in the tyrosine phosphatase field due to the low abundance and dynamic nature, sample preparation steps, and sensitivity to detect tyrosine phosphorylation events. Although mass spectrometry (MS)-based phosphoproteomics has allowed the identification of thousands of phosphotyrosine sites in a single run, protein phosphorylation poses another analysis caveat of dissecting complex phosphorylation signaling pathways involved in healthy cellular processes similarly in disease pathogenesis. This dissertation discusses strategies for improving tyrosine phosphatase sample preparation and identifying the tyrosine phosphatases' direct substrates. Chapter one is an overview of current techniques to study tyrosine phosphatases. In contrast, chapters two and three highlight the work that has been done to identify the direct substrates of phosphatase SHP2 and PTP1B, respectively, whose dysregulation leads to the development of cancers.</p> <p>In chapter 2, we describe a novel method that incorporated three separate MS-based experiments to identify the direct substrates of phosphatase SHP2: immunoprecipitation of substrate trapping mutants complex, <i>in vivo </i>global phosphoproteomics, and <i>in vitro</i> dephosphorylation of SHP2 phosphatase substrates. With immunoprecipitation of substrate trapping mutant experiment, weak and transient phosphatase-substrate interactions were detected by mass spectrometry after being stabilized by substrate trapping mutant method. This experiment not only identified the interactions between phosphatase and substrates but also revealed phosphotyrosine sites that are potentially protected in the substrate trapping mutant. We identified 80 phosphotyrosine proteins that showed upregulated in SHP2 mutant samples, and GAB1, GAB2, IRS1, SIRPA, and MPZL1 were examined in our list, which are reported SHP2 substrates. In the second experiment in parallel, we explored the global phosphorylation in HEK293 cells stimulated by epidermal growth factor. Peptides containing phosphotyrosine residues were captured by immobilized anti-pY PT-66 antibody and analyzed by LC-MS/MS. The results provided information on how SHP2 regulates downstream protein tyrosine phosphorylation and global phosphotyrosine response initiated by EGF. We used SHP2 substrate trapping mutant to isolate phosphotyrosine-containing proteins to serve as a SHP2 substrate pool for an <i>in vitro </i>phosphatase assay, then analyzed by LC-MS/MS. Finally, the overlap of the three separate MS-based experiments gave us the final list of high-confidence SHP2 substrates. DOK1 was validated to be a direct SHP2 substrate. </p> Chapter 3 describes a novel method that integrates <i>in vivo </i>global phosphoproteomics perturbed by PTP1B inhibitor and stimulated by insulin with <i>in vitro </i>kinetic profile of PTP1B phosphatase to identify its substrates. We were able to identify 114 phosphotyrosine proteins that showed upregulated in PTP1B inhibitor and insulin-treated sample in <i>in vivo </i>global phosphoproteomics experiment. CTTN, EGFR, FER, IRS1, PTPN11, SRC, TYK2, PKM, GAB1, GAB2, and INSR were examined, which are PTP1B reported substrates. <a>In <i>in vitro </i>kinetic profile of the PTP1B phosphatase experiment, we utilized dimethyl labeling to quantify the PTP1B dephosphorylation rate. No PTP1B substrate motif consensus was observed in the labeling experiments. We finally overlapped <i>in vivo </i>and <i>in vitro </i>experiments to identify PTP1B <i>bona fide </i>substrates with high confidence.</a>

As the complexity of our society and computational resources increases, so does the complexity of the problems that we approach using evolutionary search techniques. There are recent approaches to deal with the problem of scaling evolutionary methods to cope with highly complex difficult problems. Many of these approaches are biologically inspired and share an underlying principle: a problem representation based on basic representational building blocks that interact and self-organize into complex functions or designs. The observation from the central dogma of molecular biology that proteins are the basic building blocks of life and the recent advances in proteomics on analysis of structure, function and interaction of entire protein complements, lead us to propose a unifying framework of thought for these approaches: the proteomics approach. This thesis propose to investigate whether the self-organization of protein analogous structures at the representation level can increase the degree of complexity and ``novelty'' of solutions obtainable using evolutionary search techniques. In order to do so, we identify two fundamental aspects of this transition: (1) proteins interact in a three dimensional medium analogous to a multiset; and (2) proteins are functional structures. The first aspect is foundational for understanding of the second. This thesis analyzes the first aspect. It investigates the effects of using a genome to proteome mapping on evolutionary computation. This analysis is based on a genetic algorithm (GA) with a string to multiset mapping that we call the proportional genetic algorithm (PGA), and it focuses on the feasibility and effectiveness of this mapping. This mapping leads to a fundamental departure from typical EC methods: using a multiset of proteins as an intermediate mapping results in a \emph{completely location independent} problem representation where the location of the genes in a genome has no effect on the fitness of the solutions. Completely location independent representations, by definition, do not suffer from traditional EC hurdles associated with the location of the genes or positional effect in a genome. Such representations have the ability to self-organize into a genomic structure that appears to favor positive correlations between form and quality of represented solutions. Completely location independent representations also introduce new problems of their own such as the need for large alphabets of symbols and the theoretical need for larger representation spaces than traditional approaches. Overall, these representations perform as well or better than traditional representations and they appear to be particularly good for the class of problems involving proportions or multisets. This thesis concludes that the use of protein analogous structures as an intermediate representation in evolutionary computation is not only feasible but in some cases advantageous. In addition, it lays the groundwork for further research on proteins as functional self-organizing structures capable of building increasingly complex functionality, and as basic units of problem representation for evolutionary computation.