Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

A thorough understanding of pathogenic microorganisms and their interactions with host organisms is crucial to prevent infectious threats due to the fact that Pathogen-Host Interactions (PHIs) have critical roles in initiating and sustaining infections. Therefore, the analysis of infection mechanisms through PHIs is indispensable to identify diagnostic biomarkers and next-generation drug targets and then to develop strategic novel solutions against drug-resistance and for personalized therapy. Traditional approaches are limited in capturing mechanisms of infection since they investigate hosts or pathogens individually. On the other hand, the systems biology approach focuses on the whole PHI system, and is more promising in capturing infection mechanisms. Here, we bring together studies on the below listed sections to present the current picture of the research on Computational Systems Biology of Pathogen-Host Interactions: - Computational Inference of PHI Networks using Omics Data - Computational Prediction of PHIs - Text Mining of PHI Data from the Literature - Mathematical Modeling and Bioinformatic Analysis of PHIs Computational Inference of PHI Networks using Omics Data Gene regulatory, metabolic and protein-protein networks of PHI systems are crucial for a thorough understanding of infection mechanisms. Great advances in molecular biology and biotechnology have allowed the production of related omics data experimentally. Many computational methods are emerging to infer molecular interaction networks of PHI systems from the corresponding omics data. Computational Prediction of PHIs Due to the lack of experimentally-found PHI data, many computational methods have been developed for the prediction of pathogen-host protein-protein interactions. Despite being emerging, currently available experimental PHI data are far from complete for a systems view of infection mechanisms through PHIs. Therefore, computational methods are the main tools to predict new PHIs. To this end, the development of new computational methods is of great interest. Text Mining of PHI Data from Literature Despite the recent development of many PHI-specific databases, most data relevant to PHIs are still buried in the biomedical literature, which demands for the use of text mining techniques to unravel PHIs hidden in the literature. Only some rare efforts have been performed to achieve this aim. Therefore, the development of novel text mining methods specific for PHI data retrieval is of key importance for efficient use of the available literature. Mathematical Modeling and Bioinformatic Analysis of PHIs After the reconstruction of PHI networks experimentally and/or computationally, their mathematical modeling and detailed computational analysis is required using bioinformatics tools to get insights on infection mechanisms. Bioinformatics methods are increasingly applied to analyze the increasing amount of experimentally-found and computationally-predicted PHI data. Acknowledgements: We, editors of this e-book, acknowledge Emrah Nikerel (Yeditepe University, Turkey) and Arzucan Özgür (Bogaaziçi University, Turkey) for their contributions during the initiation of the Research Topic.

Image-based Systems Biology --- Network Inference --- OMICS data --- Computational Biology --- bioinformatics --- protein-protein interaction --- text mining --- Constraint-based modeling --- gene regulatory network --- pathogen-host interaction

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows.

Research & information: general --- metabolic networks --- mass spectral libraries --- metabolite annotation --- metabolomics data mapping --- nontarget analysis --- liquid chromatography mass spectrometry --- compound identification --- tandem mass spectral library --- forensics --- wastewater --- gut microbiome --- meta-omics --- metagenomics --- metabolomics --- metabolic reconstructions --- genome-scale metabolic modeling --- constraint-based modeling --- flux balance --- host–microbiome --- metabolism --- global metabolomics --- LC-MS --- spectra processing --- pathway analysis --- enrichment analysis --- mass spectrometry --- liquid chromatography --- MS spectral prediction --- metabolite identification --- structure-based chemical classification --- rule-based fragmentation --- combinatorial fragmentation --- time series --- PLS --- NPLS --- variable selection --- bootstrapped-VIP --- data repository --- computational metabolomics --- reanalysis --- lipidomics --- data processing --- triplot --- multivariate risk modeling --- environmental factors --- disease risk --- chemical classification --- in silico workflows --- metabolome mining --- molecular families --- networking --- substructures --- mass spectrometry imaging --- metabolomics imaging --- biostatistics --- ion selection algorithms --- liquid chromatography high-resolution mass spectrometry --- data-independent acquisition --- all ion fragmentation --- targeted analysis --- untargeted analysis --- R programming --- full-scan MS/MS processing --- R-MetaboList 2 --- liquid chromatography–mass spectrometry (LC/MS) --- fragmentation (MS/MS) --- data-dependent acquisition (DDA) --- simulator --- in silico --- untargeted metabolomics --- liquid chromatography–mass spectrometry (LC-MS) --- experimental design --- sample preparation --- univariate and multivariate statistics --- metabolic pathway and network analysis --- LC–MS --- metabolic profiling --- computational statistical --- unsupervised learning --- supervised learning

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows.

Research & information: general --- metabolic networks --- mass spectral libraries --- metabolite annotation --- metabolomics data mapping --- nontarget analysis --- liquid chromatography mass spectrometry --- compound identification --- tandem mass spectral library --- forensics --- wastewater --- gut microbiome --- meta-omics --- metagenomics --- metabolomics --- metabolic reconstructions --- genome-scale metabolic modeling --- constraint-based modeling --- flux balance --- host–microbiome --- metabolism --- global metabolomics --- LC-MS --- spectra processing --- pathway analysis --- enrichment analysis --- mass spectrometry --- liquid chromatography --- MS spectral prediction --- metabolite identification --- structure-based chemical classification --- rule-based fragmentation --- combinatorial fragmentation --- time series --- PLS --- NPLS --- variable selection --- bootstrapped-VIP --- data repository --- computational metabolomics --- reanalysis --- lipidomics --- data processing --- triplot --- multivariate risk modeling --- environmental factors --- disease risk --- chemical classification --- in silico workflows --- metabolome mining --- molecular families --- networking --- substructures --- mass spectrometry imaging --- metabolomics imaging --- biostatistics --- ion selection algorithms --- liquid chromatography high-resolution mass spectrometry --- data-independent acquisition --- all ion fragmentation --- targeted analysis --- untargeted analysis --- R programming --- full-scan MS/MS processing --- R-MetaboList 2 --- liquid chromatography–mass spectrometry (LC/MS) --- fragmentation (MS/MS) --- data-dependent acquisition (DDA) --- simulator --- in silico --- untargeted metabolomics --- liquid chromatography–mass spectrometry (LC-MS) --- experimental design --- sample preparation --- univariate and multivariate statistics --- metabolic pathway and network analysis --- LC–MS --- metabolic profiling --- computational statistical --- unsupervised learning --- supervised learning