Rapid advances in high-throughput data acquisition technologies, such as microarraysand next-generation sequencing, have enabled the scientists to interrogate the expression levels of tens of thousands of genes simultaneously. However, challenges remain in developingeffective computational methods for analyzing data generated from such platforms. In thisdissertation, we address some of these challenges. We divide our work into two parts. Inthe first part, we present a suite of multivariate approaches for a reliable discovery of geneclusters, often interpreted as pathway components, from molecular profiling data with replicated measurements. We translate our goal into learning an optimal correlation structure from replicated complete and incomplete measurements. In the second part, we focus on thereconstruction of signal transduction mechanisms in the signaling pathway components. Wepropose gene set based approaches for inferring the structure of a signaling pathway.First, we present a constrained multivariate Gaussian model, referred to as the informed-case model, for estimating the correlation structure from replicated and complete molecular profiling data. Informed-case model generalizes previously known blind-case modelby accommodating prior knowledge of replication mechanisms. Second, we generalize theblind-case model by designing a two-component mixture model. Our idea is to strike anoptimal balance between a fully constrained correlation structure and an unconstrained one.Third, we develop an Expectation-Maximization algorithm to infer the underlying correlation structure from replicated molecular profiling data with missing (incomplete) measurements.We utilize our correlation estimators for clustering real-world replicated complete and incompletemolecular profiling data sets. The above three components constitute the first partof the dissertation. For the structural inference of signaling pathways, we hypothesize a directed signal pathway structure as an ensemble of overlapping and linear signal transduction events. We then propose two algorithms to reverse engineer the underlying signaling pathway structure using unordered gene sets corresponding to signal transduction events. Throughout we treat gene sets as variables and the associated gene orderings as random.The first algorithm has been developed under the Gibbs sampling framework and the secondalgorithm utilizes the framework of simulated annealing. Finally, we summarize our findingsand discuss possible future directions.

Artificial nighttime lighting (ANL) is known to adversely affect animals, but little is known what the consequences are to plants. Two genotypes of Populus balsamifera, a common urban tree, were used to investigate how ANL impacts plants. While the two genotypes varied in their physiological sensitivity to ANL, poorer levels of net leaf carbon assimilation compared to control samples suggested that ANL perturbed the perception of time of day for these plants. Gene set analysis on a subset of PopGenExpress microarray samples identified time of day specific processes in P. balsamifera, and a set of candidate ANL-sensitive genes were identified from these. Transcript measurements from the two genotypes revealed that ANL affects plants at the molecular level, for the diel cycling of the putative ANL-sensitive genes was perturbed. Together, these results suggest that ANL affects plants at the physiological and molecular level by perturbing their perception of time of day.

Artificial nighttime lighting (ANL) is known to adversely affect animals, but little is known what the consequences are to plants. Two genotypes of Populus balsamifera, a common urban tree, were used to investigate how ANL impacts plants. While the two genotypes varied in their physiological sensitivity to ANL, poorer levels of net leaf carbon assimilation compared to control samples suggested that ANL perturbed the perception of time of day for these plants. Gene set analysis on a subset of PopGenExpress microarray samples identified time of day specific processes in P. balsamifera, and a set of candidate ANL-sensitive genes were identified from these. Transcript measurements from the two genotypes revealed that ANL affects plants at the molecular level, for the diel cycling of the putative ANL-sensitive genes was perturbed. Together, these results suggest that ANL affects plants at the physiological and molecular level by perturbing their perception of time of day.