Browsing by Subject "Investigative Techniques"
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Item Integrating Functional Genomics, Proteomics and Computational Analysis for the Characterization of Cellular Networks(2008-09-18) Komurov, Kakajan; White, Michael A.As the study of biological systems progresses from a molecular level to a systems level, the development of new methodology for efficient data acquisition has been a key challenge of biological research in recent years. While development of novel high throughput experimental platforms is essential for an accurate large-scale data collection, novel theoretical methodology is indispensable for proper analysis and interpretation of these data. My projects aim at both, developing novel theoretic-analytical methodology for the analysis of functional patterns in biological networks, and also establishing a high throughput experimental platform for the study of signaling pathways. I have developed a generalized method for the analysis of functional organization in complex networks. This method makes use of several novel metrics used to characterize a node's status in the network. After the nodes are clustered according to their characteristics, statistically significant organizational patterns are revealed by random simulations of the network. Using this approach, I have found important characteristics of eukaryotic protein interaction networks that have direct implications in cellular phenomena like robustness and the efficiency of information processing. I have identified an entirely new class of functional modules with unique properties that contribute to the variability in cellular phenotypes. In addition, my analyses have uncovered a distinct pattern of organization in the protein network (called "rich club connectivity") that provides mechanistic explanations for some cell biological phenomena. This work not only reveals a highly organized functional dynamic layout of the protein interaction network, but also refines and/or corrects several notions proposed by previous studies. Functional genomic screens are a powerful tool for finding novel components of biological networks. However, in order to make these screens effective for assays that may require multiple readouts, it is necessary to channel the assay to another high throughput platform. Here, I used high throughput RNAi as a loss-of-function screen, and reverse-phase protein arrays as a high throughput readoutItem Multidimensional Pain Inventory: Revised Profile Classification Based on Clinical Observations in a Pain Setting(2005-08-11) Ravani, Payal Jitendra; Stowell, Anna W.The purpose of this study was to build a new profile classification system for the Multidimensional Pain Inventory (MPI). According to some clinical researchers, the current profiles of the MPI do not fully portray how chronic pain patients evaluate and manage their pain because of the great variability in each subgroup. This study tried to revise the current profile classifications based on clinical observations, which may then lead to facilitate improved patient assessment, professional communication, and treatment planning. Participants, who completed pre- and post- treatment MPI measures, were randomly selected from the interdisciplinary program at the Eugene McDermott Center for Pain Management at UT Southwestern Medical Center at Dallas. Two hundred and eighty patients were then assigned to three different groups depending upon the re-coded scores from their pre-treatment MPI. Patients were grouped according to their MPI subscale scores. In order to determine if the hypotheses were supported or not, paired t-test were completed on six different psychosocial and functional outcome measures. Analyses were also conducted to check for differences among the nine different groups. As postulated, a number of significant relationships were identified. Paired t-test analyses demonstrated the significance of the relationship between certain MPI subscales. When Pain Severity (PS) and Interference (I) were below average (as determined by recoded T scores), patients had a good prognosis. When Life Control (LC) equaled Affective Distress (AD), participants were seen to have a good prognosis as well. Associations between the other MPI subscales were also assessed, but data did not support those hypotheses. The majority of the outcomes did not meet expectation, because of several limitations with the study design.