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Acute myeloid leukemia (AML) is a highly fatal blood cancer that is characterized by disruption of healthy differentiation of stem cells into functional blood cells in the bone marrow. Most patients with AML consequently die from infections due to the lack of immune cells. For decades, the standard method of remission induction for AML has been chemotherapy using an antineoplastic drug known as AraC. However, even after successful remission induction, aggressive, refractory relapse occurs in the majority of patients within 3 years with dismal survival rates. Here, we sought to develop a genome-wide screening approach to determine the causative genes in AML relapse. In the developed procedure, protein-coding genes of the human genome are screened using a leading-edge technology known as CRISPR (clustered regularly interspaced short palindromic repeats) activation screening. This involves usage of a pooled guide RNA library that upregulates a unique gene for each individual AML cell. By treating these cells with AraC in a mouse xenograft model, the bone marrow will gradually be enriched with cells that carry a guide RNA for a relapse-conducive gene. By harvesting and sequencing all enriched guide RNAs at relapse, the causative genes in AML relapse can be determined. All parameters of the in vivo CRISPR-activation screen have been optimized, and the workflow from preparation to the end of screening has been detailed. Follow-up studies that will validate the results of the screen have also been described. The long-term goal of this developed screen is to elucidate the mechanisms of AML relapse and find ways to clinically target these pathways in conjunction with standard the AraC-based chemotherapy. Thesis Master of Science (MSc)

A minimal subnetwork is extracted from a very complex full network upon exploring the reaction pathways connecting reactants and products with minimum dissociation and formation of chemical bonds. Such a process reduces computational cost and correctly predicts the pathway for two representative reactions., Despite remarkable advances in computational chemistry, prediction of reaction mechanisms is still challenging, because investigating all possible reaction pathways is computationally prohibitive due to the high complexity of chemical space. A feasible strategy for efficient prediction is to utilize chemical heuristics. Here, we propose a novel approach to rapidly search reaction paths in a fully automated fashion by combining chemical theory and heuristics. A key idea of our method is to extract a minimal reaction network composed of only favorable reaction pathways from the complex chemical space through molecular graph and reaction network analysis. This can be done very efficiently by exploring the routes connecting reactants and products with minimum dissociation and formation of bonds. Finally, the resulting minimal network is subjected to quantum chemical calculations to determine kinetically the most favorable reaction path at the predictable accuracy. As example studies, our method was able to successfully find the accepted mechanisms of Claisen ester condensation and cobalt-catalyzed hydroformylation reactions.