Browsing by Subject "MTB"
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Item Lurking Pathway Prediction And Pathway ODE Model Dynamic Analysis(2013-11-18) Zhang, RengjingSignaling pathway analysis is so important to study the causes of diseases and the treatment of drugs. Finding the lurking pathway from ligand to signature is a significant issue in studying the mechanism of how the cell response to the stimulation signal. However, recent literature based pathway analysis methods can only tell about highly differentially expressed pathways related to the experiment data, which may tell nothing about our interested specific ligand and signature. In this paper, we designed an approach to successfully detect the most reliable pathways for specific ligand and signature by solving multi-objective optimization problem on the bridge connecting two signaling pathways where the ligand and sig- nature locate. The pathway bridge consisted of enriched looping patterns refined the complicated entire protein interactions network with 39031 links, which made the approach time-saving. The approach was further applied to study the mod- ulator mechanism of the signal molecule, receptor, intermediate transfer proteins, transcription factor, and signature. With preliminary studied pathways, we then employed Ordinary Differential Equations(ODE) to modeling and dynamic analysis the signaling transduction. The biological reactions were represented in terms of differential equations, and the solu- tions to the group of equations were further be optimized to fit the RPPA experiment data. In order to find the potential signaling paths in specific disease and discovery the best therapy, coefficient variation analysis, system robustness study and system outcomes changes to perturbations were also utilized. Our approach successfully predicted the lurking pathway for the signal molecule T GF ?1 and the nova protein OC I AD2 in cancer microenviroment: T GF ?1 ?T GF ?R1 ? SM AD2/3 ? SM AD4/AR ? OC I AD2, and this result was verified by literature. Better than recent pathway analysis tool, our predicted pathway also took care of significant but relatively less regulated proteins in the transduction pro- cess. And by modeling the CCL2 pathway in MTB infected cells, J N K , cM Y C and P LC showed as the most significant modules. Hence, the drug treatments inhibit- ing J N K , cM Y C and P LC would effectively obstruct the increasing of MMPs and further prevent the Mtb infections.Item Using High Throughput Screening to Acquire Promising Drug Candidates Against Mycobacterium tuberculosis(2011-07-27) LaiHing, Steven 1983-Mycobacterium tuberculosis currently affects 1/3 of the world's population. Over the past 20 years tuberculosis has become more resistant to all front line drugs used against it. Because of this, the threat of Multi Drug Resistant (MDR-TB) and Extensive Drug Resistant (XDR-TB) strains has grown greater and emerges as a world health issue. Modern travel has greatly facilitated the spread of these resistant strains. For this reason, more front line drugs are urgently needed in the fight against TB infection. High Throughput Screening can be used to both find and analyze promising drug candidates. Using automation, thousands of compounds can be tested against an attenuated strain of Tuberculosis and separate the promising compounds from the ineffective ones. We have found a select subset of candidates from our custom built ~52,000 compound diversity library which show potent inhibitory effects against our mc^2-7000 attenuated TB strain. These compounds have IC50s ranging from 1.98 muM to 11.3 muM and should be considered for future development as drugs against TB. Among the active compounds, we have found enrichment for hydrazines, as well as representation of several chemi-classes including quinolones. To determine possible toxicity issues, we have also vetted these compounds against a strain of human lymphoma; all of our promising compounds meet the threshold for non-cytoxicity.