Obesity has reached epidemic proportions in recent years. The World Health Organization estimated in 2008 that 1.4 billion people were overweight of whom 500 million were obese. Obesity associates with a wide range of conditions, such as cardiovascular diseases, cancer, diabetes, and neurological disorders, and causes approximately 2.8 million deaths each year. Many studies have established that obesity strongly impacts the normal function of the immune system: it dysregulates production of inflammatory and anti–inflammatory cytokines, alters numbers of immune cells, and causes an overall weaker immune response. Developing therapies that aim to improve the immune response is crucial in order to increase the quality of life of obese subjects and to reduce their ever–increasing healthcare-related costs. The long-term objective of this work is to contribute to the development of therapies that can increase the immune response in obese macrophages. In particular, gene modifications adjusting the response to infection in obese macrophages closer to that of lean macrophages are desired. To this end, the present work focused on the Toll-like Receptors (TLRs), which play an essential role in the detection of pathogens and the initiation of both innate and acquired immune responses. Genes essential to the transmission of the infection signal were first identified using a model of the TLR signaling pathways. These genes provided the basis for reconstructing a gene regulatory network that not only accounts for information coming from the TLRs, but also regulates key reactions within the pathways. The topology and regulatory functions of this network were identified by applying novel computational techniques to time-series gene-expression datasets. The TLR signaling and gene-regulatory networks were then integrated to develop a modeling framework for macrophage that predicts the time behavior of several markers for infection. Finally, formal verification techniques were used to demonstrate that the model satisfies several properties characteristic of the response to infection in macrophage. The work detailed in this dissertation offers a suitable platform for developing and testing biological hypotheses that aim to improve responses to infection.

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Chronic Obstructive Pulmonary Disease (COPD) is the fifth leading cause of death in the United States. COPD is a highly heterogeneous disease, and patients with COPD experience varying degrees of respiratory findings (lung function decline, chronic bronchitis, and emphysema). However, the molecular changes underlying this heterogeneity are not well understood. For my dissertation research I used bronchial airway gene expression to develop a signature of lung function decline, evaluate a molecule for potential anti-COPD properties, and develop a gene expression-based classification of COPD subtypes. Genome-wide gene expression generated from bronchial epithelial brushings of ever smokers with and without COPD were used to identify differences in gene expression associated with the rate of subsequent lung function decline. I validated this lung function decline signature in an independent set of COPD patients and determined that this signature may be driven by changes in the activity of the transcription factor XBP1. I next identified gene expression changes in human derived bronchial epithelial cells (HBECS) when exposed to a potential novel anti-COPD compound. I performed an in silico analysis to determine if these gene expression changes were related to COPD-associated gene expression differences observed in independent datasets of COPD patients. Lastly, I performed unbiased gene expression clustering on bronchial brushings to identify novel molecular COPD subtypes. I then examined these gene expression changes in independent datasets of COPD. Together, these works may lead to better understanding and treatment of COPD. The signature of lung function decline could be used as an intermediary endpoint in studies evaluating COPD therapies, or for patient stratification. Characterizing the relationship between the gene expression changes associated with COPD and those induced by the novel anti-COPD compound helps inform choices around its development as a potential medication. Lastly, the molecular subtypes of COPD may lead to a better understanding of molecular heterogeneity in the pathogenesis of COPD and ultimately more patient-specific treatments that are targeted to these molecular differences. / 2023-05-25T00:00:00Z

Twenty-first-century globalization has led to an extraordinary rise in international trade and transit. Consequentially, invertebrates, plants, and mammals are displaced more frequently, which has catalyzed a historic rise in biological invasions. The Joro Spider (Araneae: Trichonephila clavata) recently established from Asia in a landlocked region of southern Appalachia. Its range continues to expand; its cold tolerance is expected to favor northward invasion. As a large-bodied orbweaver that forms extensive webs and aggregations, the Joro spider is likely capable of inducing fundamental change to community structure via spatial competition. A valuable first step in estimating any invader’s economic or biological impact is to hypothesize regions susceptible to invasion using species distribution models. Recent work also shows that comparing global and regional distribution models yields insight into different stages of invasion. To examine potential spread and niche utilization differences in the Joro spider, one global and two regional models were developed. Maximum Entropy models were trained using open-source citizen science occurrence data and six bioclimatic variables at 2.5-arcminute resolution. An AUC-weighted ensemble model was used to produce each of the 3 global suitability projections. To compare invasive stage differences, projections were then translated to presence-absence maps using a 50% suitability threshold. The Asia-regional model predicts widespread suitability in eastern North America. However, the US-regional model reflects local adaptation to a climate niche that does not occur in the spider’s historic Asian range. Permutation feature importance shows the US-regional model was driven mainly by precipitation seasonality (64%) and annual oscillations in daily temperature range (29.1%). The Asia-regional model was instead driven by mean temperature of the driest quarter (34.9%), maximum temperature of the warmest month (23.6%), and precipitation of the warmest quarter (20.1%). The introduced Joro spider has invaded a North American niche that it is naïve to, but which co-occurs spatially with a niche akin to its historic Asian niche. If the Asia-regional climatic niche is indeed exploitable in North America, then conservative estimates show the bounds of range suitability should approach the 95th meridian and the 28th and 50th parallels. A total of 1,231,711 km2 within North America was predicted above 50% suitability. Altogether, these findings suggest niche versatility and plentiful suitable habitat favors successful North American invasion by the Joro spider.

Recent advances in biological research point out that many ribonucleic acids (RNAs) are transcribed from the genome to perform a variety of cellular functions, rather than merely acting as information carriers for protein synthesis. These RNAs are usually referred to as the non-coding RNAs (ncRNAs). The versatile regulation mechanisms and functionalities of the ncRNAs contribute to the amazing complexity of the biological system. The ncRNAs perform their biological functions by folding into specific structures. In this case, the comparative study of the ncRNA structures is key to the inference of their molecular and cellular functions. We are especially interested in two computational problems for the comparative analysis of ncRNA structures: the alignment of ncRNA structures and their classification. Specifically, we aim to develop algorithms to align and cluster RNA structural motifs (recurrent RNA 3D fragments), as well as RNA secondary structures. Thorough understanding of RNA structural motifs will help us to disassemble the huge RNA 3D structures into functional modules, which can significantly facilitate the analysis of the detailed molecular functions. On the other hand, efficient alignment and clustering of the RNA secondary structures will provide insights for the understanding of the ncRNA expression and interaction in a genomic scale. In this dissertation, we will present a suite of computational algorithms and software packages to solve the RNA structural motif alignment and clustering problem, as well as the RNA iii secondary structure alignment and clustering problem. The summary of the contributions of this dissertation is as follows. (1) We developed RNAMotifScan for comparing and searching RNA structural motifs. Recent studies have shown that RNA structural motifs play an essential role in RNA folding and interaction with other molecules. Computational identification and analysis of RNA structural motifs remain to be challenging tasks. Existing motif identification methods based on 3D structure may not properly compare motifs with high structural variations. We present a novel RNA structural alignment method for RNA structural motif identi- fication, RNAMotifScan, which takes into consideration the isosteric (both canonical and non-canonical) base-pairs and multi-pairings in RNA structural motifs. The utility and accuracy of RNAMotifScan are demonstrated by searching for Kink-turn, C-loop, Sarcin-ricin, Reverse Kink-turn and E-loop motifs against a 23s rRNA (PDBid: 1S72), which is well characterized for the occurrences of these motifs. (2) We improved upon RNAMotifScan by incorporating base-stacking information and devising a new branch-and-bound algorithm called RNAMotifScanX. Model-based search of RNA structural motif has been focused on finding instances with similar 3D geometry and base-pairing patterns. Although these methods have successfully identified many of the true motif instances, each of them has its own limitations and their accuracy and sensitivity can be further improved. We introduce a novel approach to model the RNA structural motifs, which incorporates both base-pairing and base-stacking information. We also develop a new algorithm to search for known motif instances with the consideration of both base-pairing and base-stacking information. Benchmarking of RNAMotifScanX on searching known RNA structural motifs including kink-turn, C-loop, sarcin-ricin, reverse kink-turn, and E-loop iv clearly show improved performances compared to its predecessor RNAMotifScan and other state-of-the-art RNA structural motif search tools. (3) We develop an RNA structural motif clustering and de novo identification pipeline called RNAMSC. RNA structural motifs are the building blocks of the complex RNA architecture. Identification of non-coding RNA structural motifs is a critical step towards understanding of their structures and functionalities. We present a clustering approach for de novo RNA structural motif identification. We applied our approach on a data set containing 5S, 16S and 23S rRNAs and rediscovered many known motifs including GNRA tetraloop, kink-turn, C-loop, sarcin-ricin, reverse kink-turn, hook-turn, E-loop and tandem-sheared motifs, with higher accuracy than the currently state-of-the-art clustering method. More importantly, several novel structural motif families have been revealed by our novel clustering analysis. (4) We propose an improved RNA structural clustering pipeline that takes into account the length-dependent distribution of the structural similarity measure. We also devise a more efficient and robust CLique finding CLustering algorithm (CLCL), to replace the traditional hierarchical clustering approach. Benchmark of the proposed pipeline on Rfam data clearly demonstrates over 10% performance gain, when compared to a traditional hierarchical clustering pipeline. We applied this new computational pipeline to cluster the posttranscriptional control elements in fly 3’-UTR. The ncRNA elements in the 3’ untranslated regions (3’-UTRs) are known to participate in the genes’ post-transcriptional regulation, such as their stability, translation efficiency, and subcellular localization. Inferring co-expression patterns of the genes by clustering their 3’-UTR ncRNA elements will provide invaluable knowledge for further studies of their functionalities and interactions under specific physiological processes. v (5) We develop an ultra-efficient RNA secondary structure alignment algorithm ERA by using a sparse dynamic programming technique. Current advances of the next-generation sequencing technology have revealed a large number of un-annotated RNA transcripts. Comparative study of the RNA structurome is an important approach to assess the biological functionalities of these RNA transcripts. Due to the large sizes and abundance of the RNA transcripts, an efficient and accurate RNA structure-structure alignment algorithm is in urgent need to facilitate the comparative study. By using the sparse dynamic programming technique, we devised a new alignment algorithm that is as efficient as the tree-based alignment algorithms, and as accurate as the general edit-distance alignment algorithms. We implemented the new algorithm into a program called ERA (Efficient RNA Alignment). Benchmark results indicate that ERA can significantly speedup RNA structure-structure alignments compared to other state-of-the-art RNA alignment tools, while maintaining high alignment accuracy. These novel algorithms have led to the discovery of many novel RNA structural motif instances, which have significantly deepened our understanding to the RNA molecular functions. The genome-wide clustering of ncRNA elements in fly 3’-UTR has predicted a cluster of genes that are responsible for the spermatogenesis process. More importantly, these genes are very likely to be co-regulated by their common 3’-UTR elements. We anticipate that these algorithms and the corresponding software tools will significantly promote the comparative ncRNA research in the future

Haploid selection in animal sperm is a somewhat controversial topic, but recentevidence might shed experimental light on the matter. This thesis investigates thepossibility to detect any genetic selection in an artificial setting for zebrafish spermfrom a single individual. I analyse pooled data acquired from whole-genomesequencing for two distinct groups of short- and long-lived sperm, trying to identifyshifts in allele frequencies. I augment this by designing an accurate computersimulation of selection, that manipulates selection strength and takes biologicalaspects like linkage and sequence coverage into account. This allows large scaletesting and the generation of null distributions for any test metric. The mainconclusion is that selection has to be extremely strong to be detectable unless onewould explicitly account for genetic linkage, as opposed to the straightforwardper-marker approaches that formed the initial basis for our analyses.

Through their motion, proteins perform essential functions in the living cell. Although we cannot observe protein motion directly, over 68,000 crystal structures are freely available from the Protein Data Bank. Computational protein rigidity analysis systems leverage this data, building a mechanical model from atoms and pairwise interactions determined from a static 3D structure. The rigid and flexible components of the model are then calculated with a pebble game algorithm, predicting a protein's flexibility with much more computational efficiency than physical simulation. In prior work with rigidity analysis systems, the available modeling options were hard-coded, and evaluation was limited to case studies. The focus of this thesis is improving accuracy and robustness of rigidity analysis systems. The first contribution is in new approaches to mechanical modeling of noncovalent interactions, namely hydrogen bonds and hydrophobic interactions. Unlike covalent bonds, the behavior of these interactions varies with their energies. I systematically investigate energy-refined modeling of these interactions. Included in this is a method to assign a score to a predicted cluster decomposition, adapted from the B-cubed score from information retrieval. Another contribution of this thesis is in new approaches to measuring the robustness of rigidity analysis results. The protein's fold is held in place by weak, noncovalent interactions, known to break and form during natural fluctuations. Rigidity analysis has been conventionally performed on only a single snapshot, rather than on an entire trajectory, and no information was made available on the sensitivity of the clusters to variations in the interaction network. I propose an approach to measure the robustness of rigidity results, by studying how detrimental the loss of a single interaction may be to a cluster's rigidity. The accompanying study shows that, when present, highly critical interactions are concentrated around the active site, indicating that nature has designed a very versatile system for transitioning between unique conformations. Over the course of this thesis, we develop the KINARI library for experimenting with extensions to rigidity analysis. The modular design of the software allows for easy extensions and tool development. A specific feature is the inclusion of several modeling options, allowing more freedom in exploring biological hypotheses and future benchmarking experiments.

Mapping of strongly inherited classical traits have been immensely helpful in understanding many important traits including diseases, yield and immunity. But some of these traits are too complex and are difficult to map. Taking into consideration gene expression, which mediates the genetic effects, can be helpful in understanding such traits. Together with genetic variation data such data-set is collectively known as expression genetics data. Presence of discrete and continuous variables, observed and latent variables, availability of partial causal information, and under-specfied nature of the data make expression genetics data computationally challenging, but potentially of great biological importance. In this dissertation the underlying regulatory processes are modeled as Bayesian networks consisting of gene expression and genetic variation nodes. Due to the underspecified nature of the data, inferring the complete regulatory network is impractical. Instead, the following techniques are proposed to extract interesting subnetworks with high confidence. The network motif searching technique is used to recover instances of a known regulatory mechanism. The local network inference technique is used to identify immediate neighbors of a given transcript. Application of these two techniques often results in identification of hundreds of individual networks. The network aggregation technique extracts the most common subnetwork from those networks, and identifies its immediate neighbors by collapsing them into a common network. In all the above tasks, simulation studies were carried out to estimate the robustness of the proposed methods and the results suggest that these techniques are capable of recovering the correct substructure with high precision and moderate recall. Moreover, manual biological review shows that the recovered regulatory network substructures are typically biologically sensible.

An agent based model (ABM) is a computational model with ``agents'' that interact with each other in an ``environment.'' This paper analyzes a particular ABM simulating individual ions in cardiac tissue, with the goal of modelling the strength and consistency of the electrical signals needed for a healthy heartbeat. We build several frameworks based on work by M. A. Yereniuk and S. D. Olson to demonstrate robustness of the original model. We conclude a moderate level of robustness using those frameworks, through a combination of proofs and empirical evidence.

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