Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian hierarchical approach

Cellular response to a perturbation is the result of a dynamic system of biological variables linked in a complex network. A major challenge in drug and disease studies is identifying the key factors of a biological network that are essential in determining the cell's fate. Here our goal is the identification of perturbed pathways from high-throughput gene expression data. We develop a three-level hierarchical model, where (i) the first level captures the relationship between gene expression and biological pathways using confirmatory factor analysis, (ii) the second level models the behavior within an underlying network of pathways induced by an unknown perturbation using a conditional autoregressive model, and (iii) the third level is a spike-and-slab prior on the perturbations. We then identify perturbations through posterior-based variable selection. We illustrate our approach using gene transcription drug perturbation profiles from the DREAM7 drug sensitivity predication challenge data set. Our proposed method identified regulatory pathways that are known to play a causative role and that were not readily resolved using gene set enrichment analysis or exploratory factor models. Simulation results are presented assessing the performance of this model relative to a network-free variant and its robustness to inaccuracies in biological databases

On interaction patterns in proteins

Proteins act like molecular machines that perform various functions in cellular activities. The physical laws determine the rules of atomic arrangements, however the organization of amino acids in proteins inherit evolutionary information. Understanding the three-dimensional structures of proteins are crucial for the exploration of the strong relationship between structure and functionality. This provides motivation to inspect how the network structure a ects communication in global scale. In this thesis, we study the interaction patterns in proteins to explore what kind of local mechanisms and global properties they inherit. Using the spatial information of amino acids, simpli ed models of complex molecular systems are built. We generate synthetic structures that resemble proteins in terms of network properties such as degree distribution and clustering characteristics. The di erences between synthetic structures and proteins are traced to distinguish proteins from non-protein structures. Such a di erentiation points out patterns that are peculiar to proteins and reveal the randomness within the proteins. We introduce the Mutation-Minimization (MuMi) method which mimics single point alanine mutation scan to investigate how proteins respond to naturally occurring random perturbations. Our approach enables us to unravel motifs that are common in protein structures and point out amino acids that have signi cant functional roles in biological activities