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With the ever increasing amount of high-throughput molecular profile data, biologists need versatile tools to enable them to quickly and succinctly analyze their data. Furthermore, pathway databases have grown increasingly robust with the KEGG database at the forefront. Previous tools have color-coded the genes on different pathways using differential expression analysis. Unfortunately, they do not adequately capture the relationships of the genes amongst one another. Structure Enrichment Analysis (SEA) thus seeks to take biological analysis to the next level. SEA accomplishes this goal by highlighting for users the sub-pathways of a biological pathways that best correspond to their molecular profile data in an easy to use GUI interface.

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.

In cardiac biomechanics, there is an apparent knowledge gap in 3D cardiac elastin structure and its biomechanical roles. In this study, we fill this knowledge gap via novel biomedical imaging and bioengineering means. In Aim 1, we created an overall mapping of 3D microstructures of the epicardial elastin fibers on porcine left ventricles (LV) using a laser scanning confocal microscope. We demonstrated the location- and depth-dependencies of the epicardial elastin network. Histological staining was also applied to reveal the patterns of endocardial and interstitial elastin fibers, as well as elastin fibers associated with the Purkinje fibers. In Aim 2, a novel algorithm was developed to better reconstruct the elastin fiber network and extract topological fiber metrics. We created a “fiberness” mask via fiber segmentation and fiber skeletonization to obtain the one-voxel-thick centerline skeleton and remove spurious fiber branches, thus generating topological and geometrical descriptors and bringing the study of cardiac elastin to a new level. In Aim 3, we successfully developed a semi-quantitative approach to characterize the residual stress in the epicardial layer by calculating the total angular change due to curling. Our novel curling angle characterization clearly reveals the existence of residual stress as well as the direction (circumferential vs. longitudinal) and location-dependency of the residual stress. In Aim 4, for the first time we estimated the regional residual stress of the epicardial layer on the intact LV via a four-step methodology: (i) quantify regional residual strains by comparing in situ and stressree marker dimensions; (ii) obtain regional tension-stretch/stress-stretch curves along the circumferential and longitudinal directions; (iii) adjust the biaxial curves to the 0g load reference; (iv) estimate the circumferential and longitudinal residual stresses via residual strains. This method accurately estimates the residual stress in the epicardial layer in various LV anatomical locations. We found that the location-dependency of circumferential and longitudinal residual stresses correlates with the curvature of heart surfaces. Our studies show that the epicardial layer, with its rich elastin content, might function as a balloon that wraps around the heart, and the residual stress sets up a boundary condition that assists with LV contraction.

Metabolic networks are the collections of all cellular activities taking place in a living cell and all the relationships among biological elements of the cell including genes, proteins, enzymes, metabolites, and reactions. They provide a better understanding of cellular mechanisms and phenotypic characteristics of the studied organism. In order to reconstruct a metabolic network, interactions among genes and their molecular attributes along with their functions must be known. Using this information, proteins are distributed among pathways as sub-networks of a greater metabolic network. Proteins which carry out various steps of a biological process operate in same pathway.<p>The metabolic network of <i>Caenorhabditis elegans</i> was reconstructed based on current genomic information obtained from the KEGG database, and commonly found in SWISS-PROT and WormBase. Assuming proteins operating in a pathway are interacting proteins, currently available protein-protein interaction map of the studied organism was assembled. This map contains all known protein-protein interactions collected from various sources up to the time. Topology of the reconstructed network was briefly studied and the role of key enzymes in the interconnectivity of the network was analysed. The analysis showed that the shortest metabolic paths represent the most probable routes taken by the organism where endogenous sources of nutrient are available to the organism. Nonetheless, there are alternate paths to allow the organism to survive under extraneous variations. <p>Signature content information of proteins was utilized to reveal protein interactions upon a notion that when two proteins share signature(s) in their primary structures, the two proteins are more likely to interact. The signature content of proteins was used to measure the extent of similarity between pairs of proteins based on binary similarity score. Pairs of proteins with a binary similarity score greater than a threshold corresponding to confidence level 95% were predicted as interacting proteins. The reliability of predicted pairs was statistically analyzed. The sensitivity and specificity analysis showed that the proposed approach outperformed maximum likelihood estimation (MLE) approach with a 22% increase in area under curve of receiving operator characteristic (ROC) when they were applied to the same datasets. When proteins containing one and two known signatures were removed from the protein dataset, the area under curve (AUC) increased from 0.549 to 0.584 and 0.655, respectively. Increase in the AUC indicates that proteins with one or two known signatures do not provide sufficient information to predict robust protein-protein interactions. Moreover, it demonstrates that when proteins with more known signatures are used in signature profiling methods the overlap with experimental findings will increase resulting in higher true positive rate and eventually greater AUC. <p>Despite the accuracy of protein-protein interaction methods proposed here and elsewhere, they often predict true positive interactions along with numerous false positive interactions. A global algorithm was also proposed to reduce the number of false positive predicted protein interacting pairs. This algorithm relies on gene ontology (GO) annotations of proteins involved in predicted interactions. A dataset of experimentally confirmed protein pair interactions and their GO annotations was used as a training set to train keywords which were able to recover both their source interactions (training set) and predicted interactions in other datasets (test sets). These keywords along with the cellular component annotation of proteins were employed to set a pair of rules that were to be satisfied by any predicted pair of interacting proteins. When this algorithm was applied to four predicted datasets obtained using phylogenetic profiles, gene expression patterns, chance co-occurrence distribution coefficient, and maximum likelihood estimation for S. cerevisiae and <i>C. elegans</i>, the improvement in true positive fractions of the datasets was observed in a magnitude of 2-fold to 10-fold depending on the computational method used to create the dataset and the available information on the organism of interest. <p>The predicted protein-protein interactions were incorporated into the prior reconstructed metabolic network of <i>C. elegans</i>, resulting in 1024 new interactions among 94 metabolic pathways. In each of 1024 new interactions one unknown protein was interacting with a known partner found in the reconstructed metabolic network. Unknown proteins were characterized based on the involvement of their known partners. Based on the binary similarity scores, the function of an uncharacterized protein in an interacting pair was defined according to its known counterpart whose function was already specified. With the incorporation of new predicted interactions to the metabolic network, an expanded version of that network was resulted with 27% increase in the number of known proteins involved in metabolism. Connectivity of proteins in protein-protein interaction map changed from 42 to 34 due to the increase in the number of characterized proteins in the network.

The increasing prevalence of apicomplexan parasites such as Plasmodium, Toxoplasma, and Cryptosporidium represents a significant global healthcare burden. Treatment options are increasingly limited due to the emergence of new resistant strains. We postulate that parasites have evolved distinct metabolic strategies critical for growth and survival during human infections, and therefore susceptible to drug targeting using a systematic approach. I developed iCS306, a fully characterized metabolic network reconstruction of the model organism Toxoplasma gondii via extensive curation of available genomic and biochemical data. Using available microarray data, metabolic constraints for six different clinical strains of Toxoplasma were modeled. I conducted various in silico experiments using flux balance analysis in order to identify essential metabolic processes, and to illustrate the differences in metabolic behaviour across Toxoplasma strains. The results elucidate probable explanations for the underlying mechanisms which account for the similarities and differences among strains of Toxoplasma, and among species of Apicomplexa.

Metabolic networks are the collections of all cellular activities taking place in a living cell and all the relationships among biological elements of the cell including genes, proteins, enzymes, metabolites, and reactions. They provide a better understanding of cellular mechanisms and phenotypic characteristics of the studied organism. In order to reconstruct a metabolic network, interactions among genes and their molecular attributes along with their functions must be known. Using this information, proteins are distributed among pathways as sub-networks of a greater metabolic network. Proteins which carry out various steps of a biological process operate in same pathway.<p>The metabolic network of <i>Caenorhabditis elegans</i> was reconstructed based on current genomic information obtained from the KEGG database, and commonly found in SWISS-PROT and WormBase. Assuming proteins operating in a pathway are interacting proteins, currently available protein-protein interaction map of the studied organism was assembled. This map contains all known protein-protein interactions collected from various sources up to the time. Topology of the reconstructed network was briefly studied and the role of key enzymes in the interconnectivity of the network was analysed. The analysis showed that the shortest metabolic paths represent the most probable routes taken by the organism where endogenous sources of nutrient are available to the organism. Nonetheless, there are alternate paths to allow the organism to survive under extraneous variations. <p>Signature content information of proteins was utilized to reveal protein interactions upon a notion that when two proteins share signature(s) in their primary structures, the two proteins are more likely to interact. The signature content of proteins was used to measure the extent of similarity between pairs of proteins based on binary similarity score. Pairs of proteins with a binary similarity score greater than a threshold corresponding to confidence level 95% were predicted as interacting proteins. The reliability of predicted pairs was statistically analyzed. The sensitivity and specificity analysis showed that the proposed approach outperformed maximum likelihood estimation (MLE) approach with a 22% increase in area under curve of receiving operator characteristic (ROC) when they were applied to the same datasets. When proteins containing one and two known signatures were removed from the protein dataset, the area under curve (AUC) increased from 0.549 to 0.584 and 0.655, respectively. Increase in the AUC indicates that proteins with one or two known signatures do not provide sufficient information to predict robust protein-protein interactions. Moreover, it demonstrates that when proteins with more known signatures are used in signature profiling methods the overlap with experimental findings will increase resulting in higher true positive rate and eventually greater AUC. <p>Despite the accuracy of protein-protein interaction methods proposed here and elsewhere, they often predict true positive interactions along with numerous false positive interactions. A global algorithm was also proposed to reduce the number of false positive predicted protein interacting pairs. This algorithm relies on gene ontology (GO) annotations of proteins involved in predicted interactions. A dataset of experimentally confirmed protein pair interactions and their GO annotations was used as a training set to train keywords which were able to recover both their source interactions (training set) and predicted interactions in other datasets (test sets). These keywords along with the cellular component annotation of proteins were employed to set a pair of rules that were to be satisfied by any predicted pair of interacting proteins. When this algorithm was applied to four predicted datasets obtained using phylogenetic profiles, gene expression patterns, chance co-occurrence distribution coefficient, and maximum likelihood estimation for S. cerevisiae and <i>C. elegans</i>, the improvement in true positive fractions of the datasets was observed in a magnitude of 2-fold to 10-fold depending on the computational method used to create the dataset and the available information on the organism of interest. <p>The predicted protein-protein interactions were incorporated into the prior reconstructed metabolic network of <i>C. elegans</i>, resulting in 1024 new interactions among 94 metabolic pathways. In each of 1024 new interactions one unknown protein was interacting with a known partner found in the reconstructed metabolic network. Unknown proteins were characterized based on the involvement of their known partners. Based on the binary similarity scores, the function of an uncharacterized protein in an interacting pair was defined according to its known counterpart whose function was already specified. With the incorporation of new predicted interactions to the metabolic network, an expanded version of that network was resulted with 27% increase in the number of known proteins involved in metabolism. Connectivity of proteins in protein-protein interaction map changed from 42 to 34 due to the increase in the number of characterized proteins in the network.

Two phylogenies of Phalaenopsis (Orchidaceae) are presented, one from combined chloroplast DNA data and one from a nuclear actin gene. We used these phylogenies to assess and modify the classification of Phalaenopsis and to examine several morphological characters and geographical distribution patterns. Our results support Christenson’s (2001) treatment of Phalaenopsis as a broadly defined genus that includes the species previously placed in the genera Doritis and Kingidium. Some of Christenson’s subgeneric groups needed to be recircumscribed to reflect a natural classification. We recognized four subgenera and six sections, subgenera Aphyllae, Parishianae (with sections Conspicuum, Delisiosae, Esmeralda, and Parishianae), Phalaenopsis, and Polychilos (with sections Fuscatae and Polychilos). In order to find a set of universally amplifiable, phylogenetically informative, single-copy nuclear regions, we conducted a whole genome comparison of the rice (Oryza sativa) and Arabidopsis thaliana genomes. We constructed a database of both genomes and searched for pairs of sequences using criteria we felt would ensure primers that would reliably amplify using standard PCR protocols. We tested the most promising 142 primer pairs in the lab on eighteen taxa and found four potentially informative markers in Phalaenopsis and one in Helianthus. Our results indicated that it will be difficult to find universal nuclear markers, however our database provides an important tool for finding informative nuclear markers within specific groups. The full set of primer combinations is available online at, “The Conserved Primer Pair Project,” http://aug.csres.utexas.edu:8080/cpp/index.html. We used fourteen Phalaenopsis species and seven horticultural hybrids to create a real dataset with which to test phylogenetic network reconstruction methods. We tested the performance of Neighbor-Net, implemented in SplitsTree, under four different categories of complexity: one hybrid, two independent hybrids (hybrids with no parents in common), three independent hybrids, and two non-independent hybrids (one parent was shared between hybrids). Neighbor-Net was able to predict accurately the parents of hybrids in only about half of the datasets we tested, and there were so many false positives that it was impossible to distinguish the hybrids from the species. We plan to use this dataset to test methods, such as RIATA and RGNet, when they become available. / text

The increasing prevalence of apicomplexan parasites such as Plasmodium, Toxoplasma, and Cryptosporidium represents a significant global healthcare burden. Treatment options are increasingly limited due to the emergence of new resistant strains. We postulate that parasites have evolved distinct metabolic strategies critical for growth and survival during human infections, and therefore susceptible to drug targeting using a systematic approach. I developed iCS306, a fully characterized metabolic network reconstruction of the model organism Toxoplasma gondii via extensive curation of available genomic and biochemical data. Using available microarray data, metabolic constraints for six different clinical strains of Toxoplasma were modeled. I conducted various in silico experiments using flux balance analysis in order to identify essential metabolic processes, and to illustrate the differences in metabolic behaviour across Toxoplasma strains. The results elucidate probable explanations for the underlying mechanisms which account for the similarities and differences among strains of Toxoplasma, and among species of Apicomplexa.

The diploma thesis deals with the study of construction and reconstruction of gas pipeline networks. It will acquaint us with the basic properties of gases, from their extraction to distribution. At the end of the work we apply this knowledge to the real situation of construction of the regulation station together with the reconstruction of the adjacent gas pipeline network in Tišnov with the application of the 3D program Revit.