Methods for Utilizing Co-expression Networks for Biological Insight

The explosion of high-throughput Omics assays in past 15 years has led to a revolution in the quantity of data and the number of data types which are available to biological researchers. This has necessitated a second revolution in the development of analytical tools to handle this wealth and variety of data. No longer is it practical for a researcher to simply examine a list of differentially expressed compounds and draw meaningful insight about the biological processes at hand; these differentially expressed compounds must be put into context with each other, and integrated with existing biological knowledge. Co-expression techniques, where the simultaneous expression of two or more compounds is analyzed, have become a powerful tool for biological insight in high-throughput Omics settings. The primary goal of this dissertation is to develop techniques for identifying and characterizing patterns of co-expression. In our first project, we develop a Differentially Weighted Factor Model for estimating covariance matrices related through structured experimental design. Our factor model allows us to estimate common structural elements using all available data, and to estimate unique structural elements in a condition specific manner. We develop a method for visualizing the resulting estimates, and implement the method in an R package, DWFM. The second project presents a method using the Prize Collecting Steiner Tree algorithm to integrate and identify modules in lipid and untargeted metabolomic assays in a data-driven manner. These assays are integrated over a co-expression network specific to the applied setting in question, allowing us to capture modules unique to this setting. Our final project presents a second technique for identifying modules of co-expressed biomolecules. This technique addresses a major limitation of PCST based approaches, namely that one is required to choose a cutoff to obtain a list of differentially expressed compounds used as input into the algorithm. Additionally, this second method utilizes a meta-analytic inspired approach to identify patterns of co-expression across multiple data sets, thus reducing the impact of a single noisy assay.PHDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143996/1/tealg_1.pd

Identification of phenotype-specific networks from paired gene expression-cell shape imaging data

The morphology of breast cancer cells is often used as an indicator of tumor severity and prognosis. Additionally, morphology can be used to identify more fine-grained, molecular developments within a cancer cell, such as transcriptomic changes and signaling pathway activity. Delineating the interface between morphology and signaling is important to understand the mechanical cues that a cell processes in order to undergo epithelial-to-mesenchymal transition and consequently metastasize. However, the exact regulatory systems that define these changes remain poorly characterized. In this study, we used a network-systems approach to integrate imaging data and RNA-seq expression data. Our workflow allowed the discovery of unbiased and context-specific gene expression signatures and cell signaling subnetworks relevant to the regulation of cell shape, rather than focusing on the identification of previously known, but not always representative, pathways. By constructing a cell-shape signaling network from shape-correlated gene expression modules and their upstream regulators, we found central roles for developmental pathways such as WNT and Notch, as well as evidence for the fine control of NF-kB signaling by numerous kinase and transcriptional regulators. Further analysis of our network implicates a gene expression module enriched in the RAP1 signaling pathway as a mediator between the sensing of mechanical stimuli and regulation of NF-kB activity, with specific relevance to cell shape in breast cancer

Optimizing resource allocation in computational sustainability: Models, algorithms and tools

The 17 Sustainable Development Goals laid out by the United Nations include numerous targets as well as indicators of progress towards sustainable development. Decision-makers tasked with meeting these targets must frequently propose upfront plans or policies made up of many discrete actions, such as choosing a subset of locations where management actions must be taken to maximize the utility of the actions. These types of resource allocation problems involve combinatorial choices and tradeoffs between multiple outcomes of interest, all in the context of complex, dynamic systems and environments. The computational requirements for solving these problems bring together elements of discrete optimization, large-scale spatiotemporal modeling and prediction, and stochastic models. This dissertation leverages network models as a flexible family of computational tools for building prediction and optimization models in three sustainability-related domain areas: 1) minimizing stochastic network cascades in the context of invasive species management; 2) maximizing deterministic demand-weighted pairwise reachability in the context of flood resilient road infrastructure planning; and 3) maximizing vertex-weighted and edge-weighted connectivity in wildlife reserve design. We use spatially explicit network models to capture the underlying system dynamics of interest in each setting, and contribute discrete optimization problem formulations for maximizing sustainability objectives with finite resources. While there is a long history of research on optimizing flows, cascades and connectivity in networks, these decision problems in the emerging field of computational sustainability involve novel objectives, new combinatorial structure, or new types of intervention actions. In particular, we formulate a new type of discrete intervention in stochastic network cascades modeled with multivariate Hawkes processes. In conjunction, we derive an exact optimization approach for the proposed intervention based on closed-form expressions of the objective functions, which is applicable in a broad swath of domains beyond invasive species, such as social networks and disease contagion. We also formulate a new variant of Steiner Forest network design, called the budget-constrained prize-collecting Steiner forest, and prove that this optimization problem possesses a specific combinatorial structure, restricted supermodularity, that allows us to design highly effective algorithms. In each of the domains, the optimization problem is defined over aspects that need to be predicted, hence we also demonstrate improved machine learning approaches for each.Ph.D

Identifying Novel Targetable Genes and Pathways in Cancer by Integrating Diverse Omics Data.

Omics technologies for high-throughput profiling of human genome, transcriptome and proteome are revolutionizing cancer research and driving a paradigm shift in clinical care, from “one size” fits all treatments to molecularly informed therapies. The success of this new precision medicine paradigm will depend on our ability to combine diverse omics-based measurements to distill clinically relevant information that can be acted upon. This thesis developed bioinformatics approaches to integrate multi-omics datasets and applied these approaches in three distinct studies that identified novel actionable genes and pathways in cancer. In the first study, we aim at finding alternative targetable proteins in non-small cell lung cancers (NSCLC) with activating mutations in KRAS, a well-know but undruggable oncogene, by profiling their transcriptome, proteome and phosphoproteome. By reconstructing targetable networks associated with KRAS dependency, we nominate lymphocyte-specific protein tyrosine kinase (LCK) as a critical gene for cell proliferation in these samples, suggesting LCK as a novel druggable protein in KRAS-dependent NSCLC. In the second study, we aim at identifying oncogenic gene fusions in NSCLC patients of unknown driver gene. By characterizing the highly heterogeneous fusion’s landscape in NSCLC, we show that gene fusions incidence is an independent prognostic factor for poor outcome and discover novel Neurorregulin 1 (NRG1) fusions present exclusively in patients of unknown driver; resembling previously reported kinase fusions. This warrants further studies of the therapeutic opportunities for patients with NRG1 rearrangements. Finally in the third study, we aim at characterizing cancer-related genes that overlap and could be regulated by natural antisense transcripts. By determining the extent of antisense gene expression across human cancers and comparing with well-documented sense-antisense pairs, our results raise the possibility that antisense transcripts could modulate the expression of well-known tumor suppressors and oncogenes. This study provides a resource, oncoNATdb, a catalogue of cancer related genes with significant antisense transcription, which will enable researchers to investigate the mechanisms of sense-antisense regulation and their role in cancer. We anticipate that the computational methods developed and the results found in this thesis would assist others with similar tasks and inspire further studies of the therapeutic opportunities provided by these novel targets.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107215/1/oabalbin_1.pd

Concepts in Light Microscopy of Viruses

Viruses threaten humans, livestock, and plants, and are difficult to combat. Imaging of viruses by light microscopy is key to uncover the nature of known and emerging viruses in the quest for finding new ways to treat viral disease and deepening the understanding of virus–host interactions. Here, we provide an overview of recent technology for imaging cells and viruses by light microscopy, in particular fluorescence microscopy in static and live-cell modes. The review lays out guidelines for how novel fluorescent chemical probes and proteins can be used in light microscopy to illuminate cells, and how they can be used to study virus infections. We discuss advantages and opportunities of confocal and multi-photon microscopy, selective plane illumination microscopy, and super-resolution microscopy. We emphasize the prevalent concepts in image processing and data analyses, and provide an outlook into label-free digital holographic microscopy for virus research

Bioinformatic pipelines to reconstruct and analyse intercellular and hostmicrobe interactions affecting epithelial signalling pathways

The epithelium segregates microorganisms from the immune system through tightly connected cells. The epithelial barrier maintains the integrity of the body, and the microbiome influences this through host-microbe interactions. Therefore its composition has an impact on the host's physiological processes. Disruption in the microbiome composition leads to an impaired epithelial layer. As a consequence, the cell-cell interactions between the epithelium and immune cells will be altered, contributing to inflammation. In this thesis, I examined the interconnectivity of the microbiome, epithelium and immune system in the gastrointestinal tract focusing on the oral cavity and gut in healthy and diseased conditions. I combined multi-omics data with network biology approaches to develop computational pipelines to study host-microbe and cell-cell connections. I used network propagation algorithms to reconstruct intracellular signalling and identify downstream pathways affected by the altered microbiome composition or cell-cell connections. I studied inflammation-related conditions in the oral cavity (periodontitis) and gut (inflammatory bowel disease (IBD)) to reveal the contribution of interspecies and intercellular interactions to diseases. I inferred hostmicrobe protein-protein interaction (HM-PPI) networks between healthy gum-/periodontitisrelated bacteria communities and epithelium, and found altered HM-PPIs during inflammation. I connected the epithelial cells to dendritic cells and identified the Toll-like receptor (TLR) pathway as a potential driver of the inflammation in diseased gingiva. While in the oral cavity I focused on complex microbial communities and their impact on one cell type, I discovered the direct effect of gut commensal bacteria on several immune cells in IBD. This study observed the cell-specific effect of Bacteroides thetaiotaomicron on TLR signalling. The pipelines I developed offer potentially interesting connections that aid detailed mechanistic insight into the relationship between the microbiome, epithelial barrier and immune system. These systems-level analysis tools facilitate the understanding of how microbial proteins may be of therapeutic value in inflammatory diseases

Integration and visualisation of clinical-omics datasets for medical knowledge discovery

In recent decades, the rise of various omics fields has flooded life sciences with unprecedented amounts of high-throughput data, which have transformed the way biomedical research is conducted. This trend will only intensify in the coming decades, as the cost of data acquisition will continue to decrease. Therefore, there is a pressing need to find novel ways to turn this ocean of raw data into waves of information and finally distil those into drops of translational medical knowledge. This is particularly challenging because of the incredible richness of these datasets, the humbling complexity of biological systems and the growing abundance of clinical metadata, which makes the integration of disparate data sources even more difficult. Data integration has proven to be a promising avenue for knowledge discovery in biomedical research. Multi-omics studies allow us to examine a biological problem through different lenses using more than one analytical platform. These studies not only present tremendous opportunities for the deep and systematic understanding of health and disease, but they also pose new statistical and computational challenges. The work presented in this thesis aims to alleviate this problem with a novel pipeline for omics data integration. Modern omics datasets are extremely feature rich and in multi-omics studies this complexity is compounded by a second or even third dataset. However, many of these features might be completely irrelevant to the studied biological problem or redundant in the context of others. Therefore, in this thesis, clinical metadata driven feature selection is proposed as a viable option for narrowing down the focus of analyses in biomedical research. Our visual cortex has been fine-tuned through millions of years to become an outstanding pattern recognition machine. To leverage this incredible resource of the human brain, we need to develop advanced visualisation software that enables researchers to explore these vast biological datasets through illuminating charts and interactivity. Accordingly, a substantial portion of this PhD was dedicated to implementing truly novel visualisation methods for multi-omics studies.Open Acces