Development of an internet based system for modeling biotin metabolism using Bayesian networks

Biotin is an essential water-soluble vitamin crucial for maintaining normal body functions. The importance of biotin for human health has been under-appreciated but there is plenty of opportunity for future research with great importance for human health. Currently, carrying out predictions of biotin metabolism involves tedious manual manipulations. In this paper, we report the development of BiotinNet, an internet based program that uses Bayesian networks to integrate published data on various aspects of biotin metabolism. Users can provide a combination of values on the levels of biotin related metabolites to obtain the predictions on other metabolites that are not specified. As an inherent feature of Bayesian networks, the uncertainty of the prediction is also quantified and reported to the user. This program enables convenient in silico experiments regarding biotin metabolism, which can help researchers design future experiments while new data can be continuously incorporated

Bayesian networks for disease diagnosis: What are they, who has used them and how?

A Bayesian network (BN) is a probabilistic graph based on Bayes' theorem, used to show dependencies or cause-and-effect relationships between variables. They are widely applied in diagnostic processes since they allow the incorporation of medical knowledge to the model while expressing uncertainty in terms of probability. This systematic review presents the state of the art in the applications of BNs in medicine in general and in the diagnosis and prognosis of diseases in particular. Indexed articles from the last 40 years were included. The studies generally used the typical measures of diagnostic and prognostic accuracy: sensitivity, specificity, accuracy, precision, and the area under the ROC curve. Overall, we found that disease diagnosis and prognosis based on BNs can be successfully used to model complex medical problems that require reasoning under conditions of uncertainty.Comment: 22 pages, 5 figures, 1 table, Student PhD first pape

Integration of host, pathogen and microbiome -omics data for studying infectious diseases

In an ever-growing worldwide population, human infectious diseases are an increasingly serious problem for public health. In particular, more than a million deaths and millions of infectious disease cases per year caused by fungal pathogens have been reported globally in recent years. Hence, more investments must be put into fungal research to overcome the problem. The opportunistic pathogen Candida albicans and the airborne Aspergillus fumigatus are the two most prevalent fungal pathogens causing serious issues in medical care units. Despite the recent advances in fungal research, there is little knowledge about the role of fungal metabolism in developing the infection when coexisting within the human body with microbial community members in different organs. This dissertation applied computational tools, and implemented systems biology approaches to uncover key factors in the colonization of the pathogens, especially C. albicans and A. fumigatus, from a systems biology perspective and unseen by wet-lab experiments alone. Next to multi-omics data analysis, a major effort was put into genome-scale metabolic models (GEMs) generation and analysis as a promising approach to shed light on the role of metabolism in developing the infection. In brief, this thesis sheds light on key factors leading to the inhibition or promotion of fungal growth. This especially includes the first available GEM reconstruction of C. albicans to theoretically study the intricate interaction of the fungus with the human host and the microbial community members. Lastly, a platform of 252 A. fumigatus GEMs at the strain resolution was generated. It revealed the phenotypic diversity of A. fumigatus strains isolated from different hospitals and farms in Germany and explained the contribution of the fungus to the shaping of the metabolic landscape of the lung microbiome in a favorable manner for fungal growth

Systems level investigation of the genetic basis of bovine muscle growth and development

Skeletal muscle growth is an economically and biologically important trait for livestock raised for meat production. As such, there is great interest in understanding the underlying genomic architecture influencing muscle growth and development. In spite of this, relatively little is known about the genes or biological processes regulating bovine muscle growth. In this thesis, several approaches were undertaken in order to elucidate some of the mechanisms which may be controlling bovine muscle growth and development. The first objective of this thesis was the development of a novel software tool (SNPdat) for the rapid and comprehensive annotation of SNP data for any organism with a draft sequence and annotation. SNPdat was subsequently utilised in chapters 3 and 6 to facilitate the identification of candidate genes and regions involved in bovine muscle growth. In chapter 4, a number of metrics were explored for their usefulness in assessing convergence of a Markov Chain using a Bayesian approach used in genetic prediction. The need to adequately assess convergence using multiple metrics is addressed and recommendations put forward. These recommendations were then implemented in chapter 3. In addition, three separate investigations of bovine muscle growth and development were performed. In chapter 3, a genome-wide association study was performed to identify regions of the bovine genome associated with four economically important carcass traits. This was followed by an examination of the transcriptional responses in muscle tissue of animals undergoing dietary restriction and compensatory growth (chapter 5). Finally, using high-throughput DNA sequencing, a candidate list of 200 genes was interrogated to identify genes which may be evolving at different rates, and under evolutionary selection pressure, in beef compared to dairy animals (chapter 6). A number of genes and biological pathways were found to be involved in traits related to bovine muscle growth, several of which were identified in more than one study

Application of Bioinformatics to Protein Domain, Protein Network, and Whole Genome Studies.

Bioinformatics primarily focuses on the study of sequence data. Analyzing both nucleotide and protein sequence data provides valuable insight into their function, evolution, and importance in organism adaptation. For this dissertation, I have applied bioinformatics to the study sequence data on three levels of complexity: protein domain, protein network, and whole genome. In the protein domain study, I used sequence similarity searches to identify a novel FIST (F-box and intracellular signal transduction proteins) domain. The domain was found to exist in all three kingdoms of life, pointing to its functional importance. Due to its presence exclusively with transducer and output domains, it was deduced that FIST functions as an input/sensory domain involved in signal transduction. Further functional characterization revealed FIST\u27s proximity to amino acid metabolism and transport genes. This suggested that FIST functions as a small ligand sensor. In the protein network study, I examined the evolution of the chemotaxis system within the clade of Escherichia. Our study confirmed previous results demonstrating that many urinary pathogenic Escherichia coli have lost two of their five chemotaxis receptors. However, sequence analysis demonstrates that this loss occurred as an ancestral event and was not a result of adaptive evolution. The retention of the core of the system in the vast majority of Escherichia confirms that chemotaxis is important for survival in all of Escherichia\u27s habitats. However analysis of the loss and gain of chemotaxis receptors suggests that the array of compounds that Escherichia needs to sense often does not require all 5 canonical receptors. In the genome study, I used comparative genomic analysis to examine the evolutionary history of Azospirillum, agriculturally important plant growth-promoting bacteria. Taxonomic and genomic studies have revealed that Azospirillum are very distinct from their closest relatives in both habitat and genome structure. Comparative genomic analysis revealed that Azospirillum had undergone massive horizontal gene transfer. Among acquired genes were many of those implicated in survival in the rhizosphere and in plant growth-promotion. It is proposed that this bacteria\u27s unique genome plasticity and ability to uptake large amounts of foreign DNA allowed azospirilla to transition from an aquatic to terrestrial environment

Toward a systems-level view of dynamic phosphorylation networks

To better understand how cells sense and respond to their environment, it is important to understand the organization and regulation of the phosphorylation networks that underlie most cellular signal transduction pathways. These networks, which are composed of protein kinases, protein phosphatases and their respective cellular targets, are highly dynamic. Importantly, to achieve signaling specificity, phosphorylation networks must be regulated at several levels, including at the level of protein expression, substrate recognition, and spatiotemporal modulation of enzymatic activity. Here, we briefly summarize some of the traditional methods used to study the phosphorylation status of cellular proteins before focusing our attention on several recent technological advances, such as protein microarrays, quantitative mass spectrometry, and genetically-targetable fluorescent biosensors, that are offering new insights into the organization and regulation of cellular phosphorylation networks. Together, these approaches promise to lead to a systems-level view of dynamic phosphorylation networks

An Inferential Framework for Network Hypothesis Tests: With Applications to Biological Networks

The analysis of weighted co-expression gene sets is gaining momentum in systems biology. In addition to substantial research directed toward inferring co-expression networks on the basis of microarray/high-throughput sequencing data, inferential methods are being developed to compare gene networks across one or more phenotypes. Common gene set hypothesis testing procedures are mostly confined to comparing average gene/node transcription levels between one or more groups and make limited use of additional network features, e.g., edges induced by significant partial correlations. Ignoring the gene set architecture disregards relevant network topological comparisons and can result in familiar