IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

The Planned and the Emergent: An Alternative Model of Learning and Literacy

Within academic institutions, writing centers are uniquely situated, socially rich sites for exploring learning and literacy. I examine the work of the Michigan Tech Writing Center's UN 1002 World Cultures study teams primarily because student participants and Writing Center coaches are actively engaged in structuring their own learning and meaning-making processes. My research reveals that learning is closely linked to identity formation and leading the teams is an important component of the coaches' educational experiences. I argue that supporting this type of learning requires an expanded understanding of literacy and significant changes to how learning environments are conceptualized and developed. This ethnographic study draws on data collected from recordings and observations of one semester of team sessions, my own experiences as a team coach and UN 1002 teaching assistant, and interviews with Center coaches prior to their graduation. I argue that traditional forms of assessment and analysis emerging from individualized instruction models of learning cannot fully account for the dense configurations of social interactions identified in the Center's program. Instead, I view the Center as an open system and employ social theories of learning and literacy to uncover how the negotiation of meaning in one context influences and is influenced by structures and interactions within as well as beyond its boundaries. I focus on the program design, its enaction in practice, and how engagement in this type of writing center work influences coaches' learning trajectories. I conclude that, viewed as participation in a community of practice, the learning theory informing the program design supports identity formation —a key aspect of learning as argued by Etienne Wenger (1998). The findings of this study challenge misconceptions of peer learning both in writing centers and higher education that relegate peer tutoring to the role of support for individualized models of learning. Instead, this dissertation calls for consideration of new designs that incorporate peer learning as an integral component. Designing learning contexts that cultivate and support the formation of new identities is complex, involves a flexible and opportunistic design structure, and requires the availability of multiple forms of participation and connections across contexts.